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The proliferation of Internet of Things (IoT) devices has given rise to applications that demand real-time responses and minimal delay. Fog computing has emerged as a suitable platform for processing IoT applications, extending cloud computing services to the edge of the network. This enables more cost-effective and time-efficient processing at the network's edge. However, determining how to allocate tasks to fog nodes presents a fundamental challenge, involving factors like energy consumption and limited fog server capacity, impacting quality of service parameters such as delay. This paper introduces a mathematical formula for resource allocation to minimize delay and energy consumption while considering quality of service criteria. The subsequent step involves presenting a hybrid genetic algorithm (GA) and the gray wolf optimization (GWO), constituting an improved hybrid approach where the GA exhaustively explores the solution space to reduce the risk of converging to a locally optimal point. The combination of these algorithms produces multiple solutions. Despite incurring processing costs and computation delays, the implementation of these algorithms is crucial for enhancing the Quality of Service (QoS). In conclusion, the results indicate that the simultaneous use of positive aspects from both algorithms significantly improves execution time, final task completion time compared to the other methods. [ABSTRACT FROM AUTHOR]

Function-as-a-service has reduced the user burden by allowing cloud service providers to overtake operational activities such as resource allocation, service deployment, auto-scaling, and load-balancing, to name a few. The users are only responsible for developing the business logic through event-triggered functions catering to an application. Although FaaS brings about multiple user benefits, a typical challenge in this context is the time incurred in the environmental setup of the containers on which the functions execute, often referred to as the cold-start time leading to delayed execution and quality-of-service violations. This paper presents an efficient scheduling strategy FASE that uses a finite-sized warm pool to facilitate the instantaneous execution of functions on pre-warmed containers. Test-bed evaluations over AWS Lambda confirm that FASE achieves a 40% reduction in the average cold-start time and 1.29 × speedup compared to the baselines. [ABSTRACT FROM AUTHOR]

An auction mechanism is an effective way to allocate resources through market behavior. However, in existing studies, most auction mechanisms are designed based on the maximization of social welfare, and there are few studies on potential revenue. Based on cloud computing resource allocation, this paper studies an auction mechanism with revenue limits under single-dimensional and multidimensional resource allocation. That is, the resource provider proposes the lowest revenue limit B. The mechanism aims to maximize the total social welfare while conforming to the lowest revenue limit of the provider. Specifically, we design a new price-raising auction mechanism based on resource similarity and the user cost-effectiveness value, which unifies the two stages of resource allocation and payment pricing, overcoming the problem of low revenue caused by overallocated resources and the lowest winning price. This mechanism can also achieve truthfulness, individual rationality and computational efficiency. In the experimental section, the proposed mechanism is compared with the optimal VCG mechanism and the monotonic mechanism with critical values in terms of revenue, social welfare, resource utilization, etc., and the results demonstrate the good effects of the mechanism designed in this article. [ABSTRACT FROM AUTHOR]

With the proliferation of the Internet of Things and smart devices, there exists an urge to address the critical computation demands of end users for several real-time applications. Fog computing (FC) targets to address the computation requirements nearer to the end user in almost real-time. Mainly, the fog environments (FE) exhibit characteristics of resource constraint and wide heterogeneity. In addition, the complex fog environment enforces the fog resources (FR) deployment either within fixed or distributed over random geographical locations. In the literature, there exist several solutions for the optimization of FR allocation. However, the location-dependent FR allocation has not been addressed thoroughly. Hence, this paper proposes a novel hybrid clustering-based deep Q-network for location-based optimal resource allocation (HCDQN-ORA) model. Here, the first objective is to minimize the search operation for identifying suitable FR and to meet the quality of service (QoS) demand using an enhanced fog resource clustering (E-FRC) algorithm. The second objective aims to achieve optimal FR allocation among the set of location-dependent FRs using reinforcement learning techniques. Problem formulation has been modelled using Markov decision process. A deep Q-network algorithm with two variants, namely enhanced optimal resource allocation using deep Q-network (EORA-DQN) and enhanced optimal resource allocation using deep Q-network with experience replay (EORA-DQN-ER), has been incorporated. To analyse the statically located and randomly distributed FR environment, the experimental results exhibit that, in the case of clustering of FRs, E-FRC is more efficient than conventional clustering algorithms. Next, the location-dependent optimal FR allocation problem incorporates the performance analysis based on the convergence of loss function and the parametric analysis based on batch size, respectively. Results exhibit that EORA-DQN performs better only for statically allotted resources. However, the proposed EORA-DQN-ER outperforms both types of FR with a success rate of 89.6% of optimal allocation with the batch size of 200. [ABSTRACT FROM AUTHOR]

Effective resource allocation is a crucial aspect in improving the performance of visible light communication (VLC)/hybrid WiFi networks. This study presents a dynamic resource allocation algorithm for multiuser scenarios. A fuzzy logic technique is employed to select the network resources with higher scores for communication. In this sense, to ensure fairness in the hybrid VLC-WiFi network, an enhanced proportional fairness (PF) algorithm is utilized. The algorithm considers the access delay and the distance to the access point as users move, striking a reasonable balance between fair allocation of user resources and maximizing system resource utilization. Therefore, to enhance the user experience in real-time scheduling, a compensation factor is introduced to compensate users with higher latency, increasing their priority. A thorough analysis is provided for the throughput, latency, utility value, and fairness models of the VLC-WiFi heterogeneous network system. The algorithm proposed in this paper is simulated and compared with four traditional algorithms, namely the Round-Robin algorithm, maximum carrier to interference ratio (Max C/I) algorithm, modified largest weighted delay first algorithm, and PF algorithm, under different metrics. Simulation results show that the proposed method is effective, and the improved algorithm yields better fairness and higher throughput compared to the conventional algorithm. As the number of frames increases, the fairness index of the proposed algorithm increases the fastest and gradually converges to 1. The throughput of this method is significantly greater than that of previous algorithms, reaching a stable rate of roughly 1.3 Mbps. The suggested algorithm is approximately 150% higher than the throughput achieved by conventional PF algorithms. The algorithm improves user satisfaction in different regions, and the utility value remains around 0.975. The packet loss rate is approximately 75% lower than traditional typical algorithms, and the average latency is approximately 60% lower. Simulation results show that the proposed algorithm has good application prospects in heterogeneous networks. [ABSTRACT FROM AUTHOR]

The utilization of cloud computing environments is highly popular for carrying out workflow executions due to its ability to provide clients with immediate access to computing resources. Among the various workflow scheduling problems in the cloud, deadline-constrained workflow scheduling has garnered increasing attention in recent years. This paper introduces a hybrid scheduling algorithm known as Partial Critical Path–Ant Colony Optimization (PCP–ACO), which aims to minimize the execution cost of a workflow while ensuring that it meets the user-defined deadline in cloud environments. PCP–ACO is a list scheduling algorithm that combines the PCP heuristic algorithm with the meta-heuristic ACO to achieve faster convergence. The list scheduling algorithm consists of two phases: task ordering and resource selection. In the case of PCP–ACO, the first step involves calculating a topological sort of the workflow tasks to assign priority to each task. Subsequently, the ACO meta-heuristic is employed to allocate the appropriate resource to each task of the workflow, based on their respective sub-deadlines that are computed using the PCP heuristic. In order to evaluate the effectiveness of the proposed algorithm, several experiments were conducted using five real scientific workflows. The results demonstrate that PCP–ACO outperforms the IC-PCP, L-ACO, and HP-GA algorithms in terms of average execution cost, achieving reductions of 19%, 17.3%, and 21.5%, respectively. [ABSTRACT FROM AUTHOR]

The emergence of a new generation of applications such as the Internet of Things, Artificial Intelligence, and 5G has brought fog computing as cloud development toward the edge network. It is done by adding an extra layer of resources to meet low latency, high performance, mobility, and interoperability challenges. The development of fog markets and new beneficial business paradigms such as fog services considerably affect the future of fogonomics (which is defined as the economics of fog computing). Thus, it is a challenging issue to design a market-oriented model to deal with the economics-based resource management in fog computing, which adjusts the resource supply of Fog Resource Providers (FRPs) and the demands of User Services (USs) based on users' requirements while satisfying all parties' utilities. This paper proposes the Fog-Marketing scheme consisting of a Resource Propagation Component (RPC) and a Contract-aware Market Selection Component (CMSC) for resource provisioning in multi-tier fog computing. The RPC is deployed in each fog node to perform hierarchical resource propagation of FRPs to auction-based Fog Decentralized Markets (FDMs) close to USs to increase revenue opportunities, and embedded CMSC in the user agent applies a matching strategy to assign USs and FDMs aims to promote USs' utility. The simulation results under extensive experiments confirmed that the proposed scheme succeeded in reducing the Round-Trip-Time, increasing the revenue of FRPs, and improving the utility of USs compared to existing models. [ABSTRACT FROM AUTHOR]

With the rapid advancement of 5G mobile networks and Internet of Things technology, an increasing number of data-intensive applications are generating massive amounts of information, such as face recognition, video stream analysis, and augmented reality. These applications not only demand significant computational resources but also require high real-time performance, posing challenges to the existing cloud-computing model. Deploying these data-intensive applications in a mobile edge computing environment can reduce response time for processing user tasks and meet low-latency requirements. However, conventional CPU scheduling strategies fail to effectively enhance the performance of data-intensive applications on edge nodes with limited computing resources. In this study, we focus on real-time application feature awareness and propose a strategy considering data arrival rate and cost fair CPU scheduling (RCFS) with two components: CPU resource allocation based on process weights and process scheduling based on distributed weighted round-robin. In compared to the default scheduling strategy in Linux, named Completely Fair Scheduler, experiments on edge nodes show that the RCFS strategy can effectively enhance CPU utilization, reduce running time of data-intensive applications, and improve the system's data throughput. In the best-case scenario, the RCFS strategy achieved a remarkable increase in CPU utilization of data-intensive applications by up to 98.5%, reduced running time by up to 45.5%, and improved data throughput by up to 83.6%. [ABSTRACT FROM AUTHOR]

The Internet of things (IoT) paradigm has emerged to connect the number of devices using the Internet resulting in the deployment of smart cities. Cloud computing has been applied to execute the computational demands of IoT devices by collecting data from the physical environment of a smart city. However, cloud computing could not become a proper choice for latency-sensitive applications because of remote cloud data centers. To overcome this challenge, fog computing has emerged to deal with the inherent limitations of cloud computing environment through provision of computing to the edge of a network. However, resource allocation of IoT service requests among fog nodes is considered as an NP-hard problem, which should be addressed in the fog computing environment. In this paper, an efficient optimization approach based on improved binary particle swarm optimization (IBPSO) algorithm has been provided for resource allocation of IoT requests in the hybrid fog–cloud computing environment. The proposed method aims to reduce the service request latency with ensuring load balancing among fog nodes. The performance of the proposed algorithm has been compared by the binary genetic algorithm (BGA), binary particle swarm optimization (BPSO), binary grey wolf optimization (BGWO)-based, and ranked-based resource allocation methods in terms of latency, missed deadline requests, run time, and load balancing. The results show that the proposed algorithm outperformed with an average of around 11 % , 22 % , 21 % , and 22 % percent in the IBPSO-based method rather than the BGA-based, BPSO-based, BGWO-based, and ranked-based resource allocation methods, respectively. Moreover, the resource allocation based on IBPSO achieved around 11 % , 28 % , 27 % , and 25 % decline in total latency compared to the BGA-based, BPSO-based, BGWO-based, and ranked-based resource allocation methods. Furthermore, the run time of the proposed algorithm could enhance by 45 % , 9 % , and 8 % compared to the BGA-based, BPSO-based, and BGWO-based resource allocation methods. [ABSTRACT FROM AUTHOR]

Mobile Edge Computing is an innovative network architecture in the 5G era that aims to provide low-latency services to end users.By utilizing network slicing technology, infrastructure providers can create separate slices for each service and offer them to service providers (SP). In order to ensure the effectiveness of the network, InP must accept slice requests from SP, thus increasing their revenue and avoiding degradation of the services when scaling up is necessary. Reinforcement learning methods have been increasingly used to address this challenge, particularly Q-learning. However, one major limitation of this method is its lack of compatibility with increasing state-action spaces. To overcome this limitation and enable control over slice acceptance in continuous environments, we propose an approximate Q-learning method that addresses the weaknesses of the Q algorithm. This method achieves fast convergence of the algorithm through modifications to crucial algorithmic functions, including defining and weighting important network features. Extensive evaluations were conducted to validate the effectiveness of our proposed approach. Metrics such as coverage, cumulative rewards, resource utilization, slice acceptance, penalty ratio, and revenue were considered. The results demonstrate significant improvements in terms of network efficiency, revenue generation, and resource allocation. [ABSTRACT FROM AUTHOR]

In order to enhance resource allocation in fog computing networks and establish an energy-aware service, this paper proposes a user satisfaction-based energy-saving computation offloading mechanism that jointly optimizes service decision, task offloading ratio, uplink bandwidth resource ratio, and computing resource ratio. Specifically, the proposed mechanism takes user satisfaction as a priority. It constructs a novel satisfaction function that considers the historical energy consumption distribution to capture the user's subjective perception of the service quality. Then, we develop a user satisfaction-based service decision (US-SD) algorithm to select unique service nodes for the users. Furthermore, to minimize the processing energy consumption, a subtask partition and resource allocation-based intelligent computation offloading (SPRA-ICO) algorithm is proposed. In such an algorithm, we design an innovative actor-critic network structure and add noise to the continuous output action to guarantee the randomness of deterministic policy exploration. Meanwhile, the experience replay buffer mechanism and parameter soft update operation are comprehensively employed to reduce the mutual guidance of training samples and improve the function convergence performance. Finally, the simulation results show that compared with other benchmark schemes, the proposed mechanism can realize good convergence speed and user retention rate while effectively mitigating the total energy consumption. [ABSTRACT FROM AUTHOR]

In this research, a hot module location-aware heuristic mapping algorithm for placement of hot modules is proposed, which aims to prevent the negative effects of tasks on the performance metrics of 2D network-on-chip. This method uses modular systematic and exact search optimization techniques to obtain an optimal solution to the mapping problem. Significant performance degradation and unfair allocation of system resources can occur with one or more hot modules on the network on the wormhole-base network system. By defining a specific access system for hot modules, the proposed approach optimizes system latency and throughput and minimizes energy consumption under bandwidth limitations. The efficiency and effectiveness of the proposed approach have been compared in simulation environments and similar traffic conditions. Saving energy consumption of the proposed approach compared to the equivalent algorithms for embedded systems, up to 81.69% less delay and 68% energy saving have been obtained. [ABSTRACT FROM AUTHOR]

Task scheduling (TS) in cloud computing is a complex problem that involves balancing workload distribution, resource allocation, and power consumption. Existing methods often fail to optimize these objectives simultaneously and efficiently. This paper introduces a novel technique for scheduling independent tasks in cloud computing using multi-objective optimization and deep reinforcement learning (DRL). The proposed technique, DMOTS-DRL, combines Dueling deep Q-networks and dynamic prioritized experience replay to optimize two critical objectives: scheduling completion time (makespan) and power consumption. The performance of DMOTS-DRL is evaluated using CloudSim and compared with several state-of-the-art TS algorithms. The experimental results show that DMOTS-DRL outperforms the other algorithms in reducing makespan, power consumption, and other metrics, demonstrating its effectiveness and reliability for cloud computing services. Specifically, DMOTS-DRL achieves percentage improvements ranging from − 44.04 to − 0.19% in makespan, from − 0.26 to − 27.90% in power consumption, as well as better performance on other metrics such as energy consumption, degree of imbalance, resource utilization, and average waiting time. [ABSTRACT FROM AUTHOR]

Load imbalance severely affects cluster performance, and the polarization of resources due to load skewing leads to further worsening of system throughput and latency problems. The proliferation of tasks to be processed in the big data era leads to more severe load skewing. How to cope with the surge of skewed data stream in the context of big data is a new challenge now. In this paper, we propose a coordinated load balancing strategy on skewed data streams (referred to as St-Stream), which is a two-tier hierarchical system for handling data streams. The proposed strategy is characterized by performing a migration pairing strategy for resources at the task allocation stage by cutting and moving out the tasks of high-load nodes in a hierarchical manner, and the moved-out operators are placed in the routing table, and the routing table operators are moved out to these nodes sequentially according to the tasks required by low-load nodes. We further design a two-tier coordination scheme for the resource allocation problem, which can adjust the skewed load from within the nodes and then dynamically restore the balance between the nodes. We implemented St-Stream on Apache Storm, which achieves a 21% coordination in processing CPU utilization, a 17.6% reduction in latency, and a 0.3 improvement in load balance recovery compared to the baseline design. Our experimental results demonstrate that the proposed load balancing strategy better balances the cluster load and improves the performance of the stream processing system. [ABSTRACT FROM AUTHOR]

Big Data Analytics has been spiralling as one of the eminent technologies in realising agile, secure and efficient operability in practically almost everything being done contemporarily. Hadoop is one of the efficient and highly scalable open-source frameworks that use commodity hardware for the processing of big data in a distributed environment entailing just candid programming strategies. But finding the optimal utility for a collection of autonomous, disparate distributed computing systems presents myriad perplexities. The focus of this study is mostly pivoted on finding ways to improve the scheduling procedures. This paper presented a novel scheduling algorithm Binary Hypergraphed Particle Swarm Optimisation (BHGPSO) that strives to allocate resources fairly and reduces execution time by making maximum use of available resources. It is also eligible in achieving a higher data locality rate by presenting the tasks and nodes as hypergraphs and using an enhanced eigen-trick-induced spectral clustering to group the nodes to obtain the locality weights. The experimental results demonstrate that the proposed algorithm significantly improves the degree of imbalance, makespan, execution time, and efficiency. It is also worth noting that it outperformed the execution time estimation schemes and the greedy resource allocation approach. The proposed scheduler increased data locality by up to 39.81% while decreasing execution time by 18.3%. The BHGPSO is a policy that can be plugged into the Hadoop architecture. It does escalate the scalability of Hadoop, making it more user-friendly. [ABSTRACT FROM AUTHOR]

Scientific communities are motivated to schedule their large-scale data analysis workflows in heterogeneous cluster environments because of privacy and financial issues. In such environments containing considerably diverse resources, efficient resource allocation approaches are essential for reaching high performance. Accordingly, this research addresses the scheduling problem of workflows with bag-of-task form to minimize total runtime (makespan). To this aim, we develop a mixed-integer linear programming model (MILP). The proposed model contains binary decision variables determining which tasks should be assigned to which nodes. Also, it contains linear constraints to fulfill the tasks requirements such as memory and scheduling policy. Comparative results show that our approach outperforms related approaches in most cases. As part of the post-optimality analysis, some secondary preferences are imposed on the proposed model to obtain the most preferred optimal solution. We analyze the relaxation of the makespan in the hope of significantly reducing the number of consumed nodes. [ABSTRACT FROM AUTHOR]

This paper discusses radio resource management in the telecommunications industry that utilizes opportunistic scheduling approaches and methods. It proposes a new scheduler called IFMR (Improved Fairness of MaxRate) to extend the opportunistic approach with the proposition of a new scheduling solution that enables to significantly maximize the system throughput. Our goal is to ensure better results that maximize system throughput, ensure perfect fairness at three levels and maximize the number of satisfied users who have achieved their desired throughput. Similarly, the performance of the proposed algorithm is evaluated and compared with reference algorithms: round robin, MaxSNR and proportional fair (PF). The focus of this study is to evaluate and analyze the quality of service guaranteed to each network subscriber, considering the heterogeneous data traffic based on simulations. The algorithms presented in this paper are programmed and simulated using the MATLAB simulation software. The study aims to effectively analyze the impact of radio resource management algorithms and schedulers, including the round robin algorithm, MaxSNR and PF, among others, with the main objective of evaluating the performance of the schedulers used by telecommunication and cellphone network operators. The scheduling phase, which precedes resource allocation and assignment, is a critical aspect of the study. The impact of these schedulers on throughput and fairness will also be evaluated and analyzed based on specific criteria. [ABSTRACT FROM AUTHOR]

Mobile edge computing is a promising paradigm that provides edge users with dependable computing services. However, due to the dynamic nature of mobile users and the limited resources of edge servers, it is essential to emphasize the load balancing of edge servers and the cooperation of heterogeneous computing resources. This paper proposes a Dynamic Resource Allocation (DRA) scheme based on a Quantum Approximate Optimization Algorithm (QAOA). The DRA is composed of the two components listed below. Firstly, we apply generative adversarial network to predict the future user density in various regions, which is an effective resource allocation aid. Secondly, QAOA is utilized to pre-allocate edge servers resources based on an advanced model of user density. The simulation results demonstrate that the efficient application of DRA ensures the load balancing of edge servers and simultaneously alleviates communication latency. [ABSTRACT FROM AUTHOR]

A weighted majority cooperative game (WMCG) theory-based heterogeneous fog architecture with fog-to-fog (F2F) communication is proposed in this article to reduce the delay and power consumption of the network. The fog devices are participated as players in the game, and they are operated cooperatively to perform a task. If one fog device is unable to complete the task, only a part of task is completed by it and the remaining task is completed by neighbour fog devices. Among the neighbour fog devices, the high majority fog device is selected using WMCG. The proposed TARA has generated ∼ 7–24, 9–28 and 11–30% less delay and ∼ 9–28, 11–31, and 12–32% less power consumption for transmitting 1 Kb, 5 Kb, and 10 Kb of data, respectively, than other networks. Results show that the proposed TARA is a delay and power efficient network. [ABSTRACT FROM AUTHOR]

Recently, wide-area distributed computing environments have become popular due to its huge resource capacities. In the wide-area distributed computing environment, matching between tasks and resources plays an important role in improving system performance. However, the geographically distribution of diverse resources complicates the matching problems, thus degrading the system performance. To achieve an efficient matching between task requirements and resource capacities, this study proposes a dynamic two-side matching of tasks and resources in wide-area distributed computing environments. First, the proposed method establishes the task requirement model and the resource capacity model using a uniform description based on the characteristic analysis to mitigate the impact of diversity and simplify the matching process. Then, a two-side matching degree metric is designed to comprehensively quantify the matching quality between the task requirements and resource capacities. Finally, a resource selection strategy is raised to guide the scheduling based on the matching degree. The experimental results indicate that compared with the state-of-the-art matching methods, the proposed method reduces the overall completion time and response delay by up to 35.60% and 29.28%, respectively. [ABSTRACT FROM AUTHOR]

Multicore systems integrated with hardware accelerators provide better performance for executing real-time applications in time-critical fields, such as robots, avionics, and aerospace. The integration of hardware accelerators brings new challenges for system scheduling; the software scheduling problem is extended to a hardware–software co-scheduling problem. Efficient co-scheduling strategy maximizes the benefits of hardware acceleration, which is important for time-critical systems. To solve this problem, we propose a co-scheduling strategy to minimize the system execution time. It combines hardware–software resource allocation and a real-time schedule method. Our scheduling can fit the different parallel in software and hardware (e.g., CPUs and FPGAs). The key component of our strategy is its novel hardware–software resource allocation and a high-performance heuristic scheduling algorithm. In the experiments, we evaluate our approach using both simulated and real parallel applications. The results illustrate that our algorithm obtains efficient solutions within reasonable runtimes compared to the state of the art. [ABSTRACT FROM AUTHOR]

In Network Function virtualization (NFV), network functions are virtualized as Virtual Network functions (VNFs). A network service consists of a set of VNFs. One of the major challenges in implementing this paradigm is allocating optimal resources to VNFs. Most existing work assumes that services are represented as service functional chains (SFC), which are chains. However, for more complex and diversified network services, a more appropriate representation is Virtual Network Function Forwarding Graph (VNF-FG), namely directed acyclic Graph (DAGs). Previous works failed to take advantage of this special graph structure, which makes them unsuitable for complex and diverse network service scenarios. Aiming at the problem of virtual network function Forwarding Graph mapping (VNF-FGE) represented by DAG, this paper proposes a virtual network function (VNF) deployment algorithm based on graph convolution network (GCN) and deep reinforcement learning (DRL), called GDRL-VNFP. First, we describe the VNF-FGE problem as an integer linear programming (ILP) problem and the virtual network service request (NSR) as a DAG. Secondly, in order to overcome the challenges brought about by the different sizes and dynamic arrival of the DAG representation of NSR, an efficient algorithm based on GCN and DRL is proposed, which can generate deployment solutions in real time under the premise of meeting the quality of service and minimize the total cost of deployment. We use GCN to extract features from the physical network topology and VNF-FG represented by DAG, and in addition, and construct a sequence-to-sequence model based on the attention mechanism for the output mapping scheme. Finally, we trained the model using a policy-based gradient-based reinforcement learning algorithm that could quickly find a near-optimal solution for the problem instance. Simulation results show that our GDRL-VNFP outperforms state-of-the-art solutions in terms of deployment cost, service request reception rate and runtime. [ABSTRACT FROM AUTHOR]

In cooperative cognitive radio networks (CCRN), Primary Users (PUs) share their unused spectrum resources with Secondary Users in order to facilitate cooperative communication in cognitive radio networks (SUs). The novel hybrid Giza Pyramids construction-based Complex valued Satin Bower Optimization (GPC-CSBO) algorithm is proposed in this paper to tackle the multiobjective resource allocation problem in CCRN. To operate the cooperative intermediate nodes and increase their throughput, a variety of constraints are taken into consideration, including the amount of power transmitted, QoS constraints, channel capacity, energy consumption, the priority of resource requests from secondary users (SUs), throughput, fairness evaluation constraints, etc. By constructing a cluster of SUs with the help of the SU base station, the GPC algorithm is used for effective load balancing. By providing 11 different constraints as an input to the hybrid GPC-CSBO algorithm, the SUs message request is prioritized and queued. The hybrid GPC-CSBO algorithm selects an optimal path between the SU to the PU base station based on fairness. The efficiency of the proposed algorithm is evaluated in an IEEE 802.11 WLAN area by comparing the proposed methodology with existing techniques. Throughput, energy efficiency, fairness index, delay, packet delivery, and network lifetime are taken into consideration when determining how effective the proposed approach is. [ABSTRACT FROM AUTHOR]

The fog-assisted cloud computing gives better quality of service (QoS) to Internet of things (IoT) applications. However, the large quantity of data transmitted by the IoT devices results in the overhead of bandwidth and increased delay. Moreover, large amounts of data transmission generate resource management issues and decrease the system's throughput. This paper proposes the optimized task scheduling and preemption (OSCAR) model to overcome the limitations and improve the QoS. The dataset used for the study is a real-time crowd-based dataset which provides task information. The processes involved in this paper are as follows: (i) Initially, the tasks from the IoT devices are clustered based on the priority and deadline by implementing expectation–maximization (EM) clustering to decrease the computational complexity and bandwidth overhead. (ii) The clustered tasks are then scheduled by implementing a modified heap-based optimizer based on the QoS and service level agreement (SLA) constraints. (iii) Distributed resource management is performed by allocating resources to the tasks based on multiple constraints. The categorical deep Q network is the deep reinforcement learning model is implemented for this purpose. The dynamic nature of tasks from the IoT devices is addressed by performing preemption of tasks using the ranking method, where the tasks with higher priority, with a short deadline replaces less priority task by moving it into the waiting queue. The proposed model is experimented with in the iFogsim simulation tool and evaluated in terms of average response time, loss ratio, resource utilization, average makespan time, queuing waiting time, percentage of tasks satisfying the deadline and throughput. The proposed OSCAR model outperforms the existing model in achieving the QoS and SLA with maximal throughput and reduced response time. [ABSTRACT FROM AUTHOR]

At present, the cloud computing environment (CCE) has emerged as one of the significant technologies in communication, computing, and the Internet. It facilitates on-demand services of different types based on pay-per-use access such as platforms, applications and infrastructure. Because of its growing reputation, the massive requests need to be served in an efficient way which gives the researcher a challenging problem known as task scheduling. These requests are handled by method of efficient allocation of resources. In the process of resource allocation, task scheduling is accomplished where there is a dependency between tasks, which is a Directed Acyclic Graph (DAG) scheduling. DAG is one of the most important scheduling due to wide range of its applicable in different areas such as environmental technology, resources, and energy optimization. NP-complete is a renowned concern, so various models deals with NP-complete that have been suggested in the literature. However, as the Quality of Service (QoS)-aware services in the CCEplatform have turned into an attractive and prevalent way to provide computing resources emerges as a novel critical issue. Therefore, the key aim of this manuscript is to develop a novel DAG scheduling model for optimizing the QoS parameters in the CCEplatform and validation of this can be done with the help of extensive simulation technique. Each simulated result is compared with the existing results, and it is found that newly developed algorithm performs better in comparison to the state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Resource optimization is becoming a prime factor in the progress of Internet-based technology, Cloud Computing. A resource management model is highly required in cloud data center paradigms to utilize available resources effectively. Bin-Packing problem is an applicable combinatorial optimization for Virtual Machine (VM) to Physical Machine (PM) allocation to minimize the required PMs. In this paper, we have proposed an efficient resource allocation and management algorithm in two phases. During the first phase, a Load Balanced Multi-Dimensional Bin-Packing (LBMBP) heuristic for Virtual Machine (VM) to Physical Machine (PMs or host) allocation is introduced, considering multidimensional resources: CPU, RAM, and Network Bandwidth. In the Second Phase, to perform VM migration, a mechanism to detect overloaded and underloaded hosts based on outliers has been described. The proposed work illustrated the simulation results using CloudSim Plus Simulator and observed a reduction in the number of active PMs. Energy consumption and the number of migrations with improved resource utilization. [ABSTRACT FROM AUTHOR]

Graphic processing units (GPU) concept, combined with CUDA and OpenCL programming models, offers new opportunities to reduce latency and power consumption of throughput-oriented workloads. GPU can execute thousands of parallel threads to hide the memory access latency. However, for some memory-intensive workloads, it is very likely in some time intervals that all threads of a core will be stalled while waiting for their data to be provided by the main memory. In this research, we aim to make GPU memory access latency shorter to increase the thread activity time and to decrease core underutilization. In order to improve non-optimal time of cores, we focus on the memory buffer and the interconnection network to prioritize the packets of the cores with the greatest number of stalled threads. As a result, more critical packets will receive the higher priority in arbitration and resource allocation, so their memory requests will be handled faster, and overall cores' stall time is reduced. 28% maximum and 12.5% average speed-up improvements among the used benchmarks, without significant effect on system area and power consumption, are reported. [ABSTRACT FROM AUTHOR]

With lower network latency and powerful hardware, mobile edge computing (MEC) is effective for computation-intensive and delay-sensitive tasks. The rising energy and low-latency demands of mobile applications for MEC pose challenges to task allocation. In this work, we consider computation offloading in MEC that is composed of a set of mobile devices, each with multiple tasks to offload to a nearby MEC server, with both single and multiple access points (APs). With multiple APs deployed, each mobile device can directly communicate with one or more APs, and their tasks can offload to different MEC servers that are within direct communication range. By considering binary computation offloading mode and limited subchannels, we formulate the multitask allocation problem as an integer programming problem, with the objective of minimizing the total energy consumption of all mobile devices while meeting deadline requirements. To solve this complicated problem, we propose efficient algorithms for single and multiple APs, and analyze related properties, including the approximation ratio and complexity. Experiments show that the algorithm can find high-quality solutions in a short time. [ABSTRACT FROM AUTHOR]

Cognitive Internet of things (CIoT) is the solution for resource allocation problem in an exponentially increasing number of Internet of things in fifth generation wireless networks. Two-tier massive multiple-input-multiple-output (MIMO) and multiple-input-single-output small cell-based CIoT using weighted majority cooperative game (WMCG) is deployed in RAM. We have proposed a two-level WMCG theoretic model and two utility functions, to detect the channel is free or not and for allocating the spectrum to a high majority CIoT device by the spectrum manager based on their applied maximum weight or price values. In RAM, we have calculated power consumption, signal-to-interference-plus-noise ratio (SINR), spectral efficiency, and energy efficiency of the proposed network. We have simulated the RAM using QualNet 7.1 simulator. The power consumption of the RAM has been shown to decrease by approximately 15–30%, and SINR has been shown to increase by approximately 6–7% as compared to the existing approaches. [ABSTRACT FROM AUTHOR]

In the era of the Internet of Things (IoT), the volume of data is increasing immensely causing rapid growth in network data communication and data congestion. Computational offloading thus becomes a crucial and imperative action in terms of delay-sensitive task completion and data processing for the resource constraint end-users. Nowadays fog computing, as a complement of cloud computing, has emerged a well-known concept in terms of enhancing data processing capability as well as energy conservation in low-powered networks. In this paper, we consider a heterogeneous fog-cloud network architecture where the data processing is performed on the local or remote computing device by adopting a binary offloading policy. Based on the proposed system model, we calculate the total delay and energy consumption of data processing throughout the network and formulate a mixed-integer optimization problem to jointly optimize the offloading decision and bandwidth allocation. In order to solve such an NP-hard problem, we have proposed a deep-learning-based binary offloading strategy that employs multiple parallel deep neural networks (DNNs) to make offloading decisions. Such offloading decisions are subsequently placed in a relay memory system to train and test all DNNs. Simulation results show a near-optimal performance of the proposed offloading strategy while remarkably maintaining the quality of service by decreasing overall delay and energy consumption. [ABSTRACT FROM AUTHOR]

Thermal-aware (TA) task allocation is one of the most effective software-based dynamic thermal management techniques to minimize energy consumption in data centers (DCs). Compared to its counterparts, TA scheduling attains significant gains in energy consumption. However, the existing literature overlooks the heterogeneity of computing elements in terms of thermal constraints while allocating or migrating user jobs, which may significantly affect the reliability of racks and all the equipment therein. Moreover, the workload distribution among these racks/servers is not fair and efficient in terms of thermal footprints; it is potentially beneficial to determine the workload proportion for each computing node (rack/server) based on its marginal contribution in disturbing the thermal uniformity (TU) in a DC environment. To solve the said problems, we model the workload distribution in DCs as a coalition formation game with the Shapley Value (SV) solution concept. Also, we devise Shapley Workload (SW), a TA scheduling scheme based on the SV to optimize the TU and minimize the cooling cost of DCs. Specifically, the scheduling decisions are based on the ambient effect of the neighboring nodes, for the ambient temperature is affected by the following two factors: (1) the current temperature of computing components and (2) the physical organization of computing elements. This results in lower temperature values and better TU, consequently leading to lower cooling costs. Simulation results demonstrate that the proposed strategy greatly reduces the total energy consumption compared to the existing state-of-the-art. [ABSTRACT FROM AUTHOR]

The fog paradigm extends the cloud capabilities at the edge of the network. Fog computing-based real-time applications (Online gaming, 5G, Healthcare 4.0, Industrial IoT, autonomous vehicles, virtual reality, augmented reality, and many more) are growing at a very fast pace. There are limited resources at the fog layer compared to the cloud, which leads to resource constraint problems. Edge resources need to be utilized efficiently to fulfill the growing demand for a large number of IoT devices. Lots of work has been done for the efficient utilization of edge resources. This paper provided a systematic review of fog resource management literature from the year 2016–2021. In this review paper, the fog resource management approaches are divided into 9 categories which include resource scheduling, application placement, load balancing, resource allocation, resource estimation, task offloading, resource provisioning, resource discovery, and resource orchestration. These resource management approaches are further subclassified based on the technology used, QoS factors, and data-driven strategies. Comparative analysis of existing articles is provided based on technology, tools, application area, and QoS factors. Further, future research prospects are discussed in the context of QoS factors, technique/algorithm, tools, applications, mobility support, heterogeneity, AI-based, distributed network, hierarchical network, and security. A systematic literature review of existing survey papers is also included. At the end of this work, key findings are highlighted in the conclusion section. [ABSTRACT FROM AUTHOR]

Fog computing can be an effective way to improve quality of services and solve network problems, with the demand for real-time, latency-sensitive applications increasing as well as limitations such as network bandwidth and Internet of Things users' resources. Due to the fact that different tasks in a network can create overhead that can reduce the quality of service, dynamic voltage, and frequency scaling along with a ranking function and a high number of physical and virtual machines were used in this research. A profit function phase is used to analyze network tasks in order to improve QoS by sending them to physical machines and sending them via the network to physical machines. The simulation results demonstrate that this method is the most effective in allocating radio and computational resources to IoT devices in fog computing. A comparison is presented in the results section between the proposed method and the SPA, Markov-Fog, and TRAM methods. Criteria for evaluating performance include the response time for heterogeneous environments, energy consumption against tasks and users, memory processing, energy consumption for physical and virtual machines, and network profitability. [ABSTRACT FROM AUTHOR]

The allocation of resources and scheduling of tasks, specifically mapping, in multicore systems on-chip (MCSoC), poses significant challenges. Tasks have diverse resource requirements and interact with each other, while network-on-chip (NoC)-based MCSoC consists of heterogeneous cores and communication networks. The heterogeneity of resources in MCSoC, along with the varying computational and communication demands of applications, makes mapping a complex optimization problem. We have mathematically modeled the mapping problem in NoC-based MCSoC using mixed-integer linear programming (MILP) with the objective of minimizing the execution time of applications. This model incorporates computation and communication capacity, energy budget constraints of MCSoC, and execution time requirements of applications. We further propose heuristics, including simulated annealing (SA) and genetic algorithm (GA), considering the capacity and budget constraints of MCSoC systems to accelerate applications and provide quick mapping solutions. Simulation results demonstrate that the performance from SA and GA heuristics is very close (within 10%) to the optimal solutions from MILP across various applications for 2D-Mesh NoCs with 16–100 cores. The energy consumption of SA and GA heuristics is also very close to the optimal solutions from MILP, with a few exceptions on small-scale 16-core NoC. Additionally, SA outperforms GA in most cases for all applications. [ABSTRACT FROM AUTHOR]

Course scheduling is a combinatorial optimization problem with multiple constraints. To achieve the reasonable allocation of teaching resources, all courses should be arranged under a number of specified constraints. With the increased number of courses, the solution space increases exponentially. However, the traditional methods cannot quickly find the optimal solution. In order to overcome the disadvantages of low efficiency and the possibility of high conflict in traditional course scheduling, this research study proposes a course scheduling, method based on an improved binary cuckoo search algorithm (IBCS). First of all, a multi-objective and multi-constraint mathematical model of course scheduling is established. Course scheduling involves issues, such as allocating teachers, courses, classes, classrooms and time under specific constraints. The class element is defined to represent the class and the course that the teachers will teach. Therefore, the problem of course scheduling is transformed into the process of mapping from the class element to the classroom-time pair. Six hard constraints and three soft constraints are also defined accordingly. Then, the BCS algorithm is used to search the best course scheduling scheme in the binary-encoded solution space. In order to control the convergence rate effectively and avoid falling into the local optimum, a dynamically adjustable, equilibrium coefficient is introduced. Therefore, the algorithm retains the diversity of the solutions while converging. Finally, this research study constructs the data set and carries out the simulation experiment. It also compares the IBCS algorithm in this paper with the genetic algorithm and the standard binary cuckoo search algorithm (BCS). Experimental results show that the IBCS algorithm can converge in effective time. Meanwhile, it can achieve an improved global scheduling scheme with higher stability. [ABSTRACT FROM AUTHOR]

Large-scale applications of the Internet of Things (IoT) necessitate significant computing tasks and storage resources that are progressively installed in the cloud environment. Related to classical computing models, the features of the cloud, such as pay-as-you-go, indefinite expansions, and dynamic acquisition, signify various services to these applications utilizing the IoT structure. A major challenge is to fulfill the quality of service necessities but schedule tasks to resources. The resource allocation scheme is affected by different undefined reasons in real-time platforms. Several works have considered the factors in the design of effective task scheduling techniques. In this context, this research addresses the issue of resource allocation and management in an IoT-enabled CC environment by designing a novel quasi-oppositional Aquila optimizer-based task scheduling (QOAO-TS) technique. The QOAO technique involves the integration of quasi-oppositional-based learning with an Aquila optimizer (AO). The traditional AO is stimulated by Aquila's behavior while catching the prey, and the QOAO is derived to improve the performance of the AO. The QOAO-TS technique aims to fulfill the makespan by accomplishing the optimum task scheduling process. The proposed QOAO-TS technique considers the relationship among task scheduling and satisfies the client's needs by minimizing the makespan. A wide range of simulations take place, and the results are investigated in terms of the span, throughput, flow time, lateness, and utilization ratio. [ABSTRACT FROM AUTHOR]

Blockchain is a promising technology that may act as a distributed ledger for Internet of Things (IoT). To maintain blockchain and save operation cost, IoT nodes may play roles of miners (who generate or mine blocks), by renting mining rigs from edge computing service providers. During the mining process, miners will receive rewards, while these rewards incentivize miners to keep mining in turn. However, if the rewards are insufficient, there might be a gap time where no miners participate in mining. We call a blockchain without a gap time a normal chain and a gap chain otherwise. This paper is the first to investigate the allocation of edge computing resources when normal and gap chains coexist. In this paper, we propose a normal gap game to model the allocation over the two types of blockchains which was never proposed before. In our game, miners compete with each other to maximize their respective utilities, by determining the per chain shares and the starting time of their mining rigs. We then develop a calculation framework, which factors in various system parameters to quantify the investment and allocation of mining rigs. We finally evaluate the miners' utilities under different scenarios. This study is very helpful to assist miners for reasonable investment and allocation of edge computing resources. [ABSTRACT FROM AUTHOR]

Usually, a large number of concurrent bag-of-tasks (BoTs) application execution requests are submitted to cloud data centers (CDCs), which needs to be optimally scheduled on the physical cloud resources to obtain maximal performance. In the current paper, NP-Hard cloud task scheduling (CTS) problem for scheduling concurrent BoT applications is modeled as an optimization problem involving minimization of makespan and energy consumption. Whale optimization algorithm (WOA) has been found effective in solving a wide range of optimization problems. However, standard WOA has certain deficiencies such as inadequate exploration ability, slow convergence, high computation complexity, and insufficient exploration–exploitation phase trade-off. These deficiencies eventually result in unacceptable results when the original WOA is applied over task scheduling optimization problems. To address these limitations, a multi-objective scheduling algorithm called OWPSO is suggested, which incorporates opposition-based learning (OBL) and particle swarm optimization (PSO) mechanisms into the standard WOA method. Firstly, the OBL method is applied to produce an optimal initial population to enhance the exploration and convergence speed of the proposed OWPSO approach in the successive generations. Secondly, PSO and OBL methods are incorporated in the exploration phase of the standard WOA approach to enhance exploration ability further. Thirdly, a fitness-based switching mechanism is added to provide an adequate exploration–exploitation phase trade-off. Finally, a discrete resource allocation heuristic is incorporated in the OWPSO to provide an efficient resource allocation. Simulation experiments over the CloudSim simulator reveal that OWPSO approach results in makespan reduction in the range of 1.68−18.38% (for CEA-Curie workloads), 2.10−24.32% (for HPC2N workloads), and energy consumption reduction in the range of 0.93−14.70% (for CEA-Curie workloads), and 0.73−25.94% (for HPC2N workloads) over other well-known meta-heuristics. Statistical tests and box plots further revealed the robustness of proposed OWPSO algorithm. [ABSTRACT FROM AUTHOR]

Scheduling plays a pivotal role in cloud computing systems. Designing an efficient scheduler is a challenging task. The challenge comes from several aspects, including the multi-dimensionality of resource demands, heterogeneity of jobs, diversity of computing resources, and fairness between multiple tenants sharing the cluster. This survey provides a multi-perspective overview of the cluster scheduling problem. We present a multi-dimensional classification of existing cluster management solutions based on their scheduling architectures, objectives, and methods. We also survey the recent research works which have employed machine learning solutions in cloud computing resource management. Existing cluster scheduling systems face many challenges, such as achieving a tradeoff between multiple conflicting objectives, finding the balance between jobs' requirements, scaling to the new operational demands, and choosing the appropriate scheduling architecture. Using machine learning in cluster scheduling is a promising direction to go to develop the future generation of intelligent resource schedulers. [ABSTRACT FROM AUTHOR]

Due to the massive deployment of mobile terminal equipment in the multi-access edge computing environment, user terminals will frequently handover between different base stations, resulting in problems such as reduced device response speed and increased performance loss. In light of the foregoing, this study presents a method for terminal equipment in the MEC environment to execute adaptive handover between wireless access points. To achieve elastic and dynamic network resource management and minimize the load index, an adaptive handover model based on traffic perception is proposed. Then, the adaptive handover algorithm based on AP weight is used to calculate the AP with the highest weight, and the network state is centrally controlled and monitored in real time by software-defined network controller. Then, a multi-factor joint resource management method based on SDN is proposed to reduce the delay caused by computing task offloading. This includes a resource allocation model based on minimizing communication, computing, and energy consumption performance established, and the optimal values of task offloading decision and resource allocation vector calculated through the greedy strategy. The suggested technique may successfully increase system reaction speed and task offloading loss, according to the experimental findings. [ABSTRACT FROM AUTHOR]

Big data analytics in cloud environments introduces challenges such as real-time load balancing besides security, privacy, and energy efficiency. This paper proposes a novel load balancing algorithm in cloud environments that performs resource allocation and task scheduling efficiently. The proposed load balancer reduces the execution response time in big data applications performed on clouds. Scheduling, in general, is an NP-hard problem. Our proposed algorithm provides solutions to reduce the search area that leads to reduced complexity of the load balancing. We recommend two mathematical optimization models to perform dynamic resource allocation to virtual machines and task scheduling. The provided solution is based on the hill-climbing algorithm to minimize response time. We evaluate the performance of proposed algorithms in terms of response time, turnaround time, throughput metrics, and request distribution with some of the existing algorithms that show significant improvements. [ABSTRACT FROM AUTHOR]

Cloud computing is a business model where having users and providers are competing to sell and buy services. Due to diverse software applications and dynamic cloud market, customers' requirements are ever changing. Dynamic auction-based resource allocation models, namely: combinatorial double auction resource allocation (CDARA) model attracted the researchers' attention. In existing work services are allocated to users based on less price, which may lead to service-level agreement (SLA) violation and increase user's dissatisfaction in the cloud marketplace. Decision on single attribute price may create genuine and ungenuine providers issue, as the providers will try to provide low-quality services. To solve these issues, we proposed adaptive market-oriented combinatorial double auction resource allocation (AMO-CDARA) model that allocates services to users based on multiple parameters such as less price, QoS, and providers ranking. In the proposed model, bids are received by auctioneer from multiple providers and users. Auctioneer calculates users and providers bid densities and allocates services to the most efficient users from most efficient providers according to their required request. After successful running of tasks, the auctioneer asks for feedback of the used services from users and calculates the final ranking of providers for future auctions. The simulation results show that the proposed model guaranteed SLA violation up to 99% due to twice punishment penalty and ranking of providers. The guaranteed QoS users/brokers will provide extra payment for that, and the extra payment will be from 1 to 10% of the total price, according to the final price. Furthermore, we solved the bidder drop problem up to 10% by increasing service price by adding QoS price. [ABSTRACT FROM AUTHOR]

The traditional cloud computing technology provides services to a plethora of applications by providing resources. These services support numerous industries for computational purposes and data storage. However, the obstruction of the cloud computing framework is its inadequate flexibility and problem to accommodate the diverse requirements generated from an IoT-based environment. Cloud computing is emerging with the latest paradigms to ensure that the connected heterogeneous system can achieve high-performance computing (HPC). Furthermore, many of today's requirements prefer diverse geographic distribution of resources and near to the end device location. Hence, the new fog computing paradigm provides some innovative solutions for real-time applications. The fog computing framework's prime agenda is to support latency-sensitive applications by utilizing all available resources. In this paper, a novel approach is designed for resource allocation and management. TRAM , a technique for resource allocation and management, is proposed to ensure resource utilization at the fog layer. This approach is used to track the intensity level of existing tasks using expectation maximization (EM) algorithm and calculate the current status of resources. All the available resources manage by using a wireless system. This paper provides a scheduling algorithm for the resource grading process in the fog computing environment. The performance of this approach is tested on the iFogSim simulator and compared the results with SJF, FCFS and MPSO. The experimental results demonstrated that TRAM effectively minimizes execution time, network consumption, energy consumption and average loop delay of tasks. [ABSTRACT FROM AUTHOR]

Recently, with the expansion of communications and generated data, the need for processing this high volume of data in minimum time and maximum speed has increased. Also, performing this volume of computing operations requires high processing and storage resources leading to hardware cost increment. In such systems, one of the most critical challenges is the task scheduling problem, which tries to find the optimal allocation for maximum resource usage and reduce the response time. Therefore, the purpose of this study is to design an infrastructure for smart home energy management with minimum hardware cost using cloud and fog computing and to propose a latency-aware scheduling algorithm based on virtual machine matching using meta-heuristics. Among heuristic methods, Tabu search makes it a common practice because of its high expansion in various optimization issues, as well as memory and high-speed features. Thereby, a novel algorithm based on the Tabu search is proposed that is improved using approximate nearest neighbor (ANN) and fruit fly optimization (FOA) algorithms. Finally, to validate the proposed method, a case study is simulated and the proposed algorithm is implemented considering target factors of execution time, latency, allocated memory, and cost function to illustrate the performance of the algorithm. The comparison results show that the proposed algorithm outperforms the Tabu search, genetic algorithm, PSO, and simulated annealing methods. [ABSTRACT FROM AUTHOR]

Memory access as a primary performance bottleneck of each processing unit also plays a significant role in GPU performance. In addition to high challenging parts of GPU's memory access path, the low locality property among the requests considerably increases the memory access delay. Despite the GPU's immense processing power, they cannot reach their maximum throughput values because of the memory access bottlenecks. Memory divergence and miss locality among the L1 missed requests significantly impose the Last-Level-Cache contention and main memory row switching overheads. In addition, interconnection network routes the request packets regardless of locality properties, such routing algorithm considerably disrupts the locality among the requests. In this paper, we proposed Locality-Aware Resource Allocation (LARA) to reduce the Streaming-Multiprocessors stall time with arbitrage among the memory request packets in favor of locality maintenance at the interconnection network of GPU. In addition, before injecting the memory requests to the interconnection network, they will be reordered at the injection-port buffer based on their thread block equality. Memory-divergence and miss-locality among the requests are two main factors that increase the rates of Last-Level-Cache contention and main memory row switching. We proposed a comprehensive approach to improving the GPU performance by decreasing the average memory access delay. We focused on the request locality property to decrease the Last-Level-Cache contention overheads and main memory row switching rate. As a result, 33% maximum and 17% average speed-up improvements among the used benchmarks, without significant effect on system areas and power consumptions, are reported. [ABSTRACT FROM AUTHOR]

The problem of mutual exclusion is a highly focused area in the distributed architecture. To avoid inconsistency in data, mutual exclusion ensures that no two processes running on different processors are allowed to enter into the same shared resource simultaneously in the system. In recent years, the consistent development of ongoing internet and mobile communication technologies, the devices, infrastructure and resources in networking systems like Ad Hoc Networks are becoming more complex and heterogeneous. Various algorithms have been introduced as a solution to mutual exclusion problem in the domain of distributed architecture over the past years. The performance and adaptability of these solutions depend on the different strategies used by them in the system. Various classifications of these strategies have been proposed such as token-based and non-token-based (also, permission-based). This paper presents a survey of various existing token-based distributed mutual exclusion algorithms (TBDMEA) in the focus of their performance measures and fault-tolerant capabilities which comprises the associated open challenges and directions to future research. In conjunction with traditional to latest proposed TBDMEA, token-based distributed group mutual exclusion algorithms (TBDGMEA) and token-based self-stabilizing distributed mutual exclusion algorithms (TBStDMEA) have also been surveyed in this paper as new variants of the token-based scheme. [ABSTRACT FROM AUTHOR]

Task scheduling is an attractive research topic in cloud computing nowadays. This process is very challenging and well known as NP-complete problem. Due to the dynamic and heterogeneous nature of user's request and provider's resource in cloud computing, the scheduling process still needs intelligent algorithms to achieve an efficient cloud resource allocation and to guarantee a good Quality of Service (QoS) for the users and their request classes. An important aspect for meeting these objectives is to design an effective task scheduling scheme which can not only satisfy users' varying priorities and QoS requirements, but also enhance providers' profit and system performances. In this paper, we introduce a new strategy to address the priority issue in both users' requests and providers' resources. We propose an efficient priority tasks scheduling called MCPTS, where the priority is adjusted according to four tasks' parameters including length, waiting time, deadline and burst time. MCPTS scheme consists of three sub-models such as tasks priority, task queueing priority and resources priority. A new hybrid multi-criteria decision-making (MCDM) method, namely ELECTRE III, and a meta-heuristic algorithm called differential evolution are proposed to evaluate and determine tasks' priorities. Further, we introduce a novel dynamic priority-queue algorithm based on queueing model. Furthermore, we adjust dynamically the resources priority based on tasks priority model in order to design an efficient and flexible relation between both resources and tasks classes. The proposed algorithm is validated through the CloudSim simulator. The experimental results indicate the superiority of MCPTS algorithm compared to other existing algorithms. Also, it shows the effectiveness of our algorithm in providing good system performance, satisfying users' priorities as well as QoS requirements, enhancing load balancing and improving resources utilization. [ABSTRACT FROM AUTHOR]

For measuring the efficiency of workflow scheduling, determining makespan and execution cost is essential. As estimating makespan and cost is difficult in a Cloud environment, designing an efficient computation of workflow scheduling remains a challenge. The Cloud resources are scaled up and down in accordance with user demand by following a scheduling policy. The scalability of the work environment is achieved through the virtualization process. Based on system experience, this paper proposes the priority-based backfilling backpropagation neural network (PBF-NN) hybrid scheduling algorithm for measuring makespan and execution cost accurately. The backfill algorithm is used to schedule tasks to the available resources. The percentage of migration is reduced when this algorithm is used compared to the First Come First Serve algorithm. Then, the Berger model is used to measure the fairness of resource allocation. The system decides task reallocation based on the fairness value. The backpropagation neural network handles the virtual machine placement process with necessary training and testing. The proposed algorithm dynamically allocates the tasks and reduces the utilization of resources. We use an experimental study to illustrate how the proposed system enables higher efficiency in cost, makespan, and performance. [ABSTRACT FROM AUTHOR]

With the popularity of mobile devices, the next generation of mobile networks has faced several challenges. Different applications have been emerged, with different requirements. Offering an infrastructure that meets different types of applications with specific requirements is one of these issues. In addition, due to user mobility, the traffic generated by the mobile devices in a specific location is not constant, making it difficult to reach the optimal resource allocation. In this context, network function virtualization (NFV) can be used to deploy the telecommunication stacks as virtual functions running on commodity hardware to meet users' requirements such as performance and availability. However, the deployment of virtual functions can be a complex task. To select the best placement strategy that reduces the resource usage, at the same time keeps the performance and availability of network functions is a complex task, already proven to be an NP-hard problem. Therefore, in this paper, we formulate the NFV placement as a multi-objective problem, where the risk associated with the placement and energy consumption are taken into consideration. We propose the usage of two optimization algorithms, NSGA-II and GDE3, to solve this problem. These algorithms were taken into consideration because both work with multi-objective problems and present good performance. We consider a triathlon circuit scenario based on real data from the Ironman route as an use case to evaluate and compare the algorithms. The results show that GDE3 is able to attend both objectives (minimize failure and minimize energy consumption), while the NSGA-II prioritizes energy consumption. [ABSTRACT FROM AUTHOR]

The cloud services can be received at anytime from anywhere based on the need of the customers. According to the necessity of the customers, the virtualization of technologies is applied to deliver the cloud services accurately. A large amount of data transfers from user to host and hosts to the user, in cloud environment. To pin the virtual machine on an appropriate host and transferring the data is a challenging task. This paper explains DAVmS: Distance Aware Virtual Machine Scheduling Algorithm, which is applied to arrange virtual machines according to its capability, and pin the VMs to the nearest physical host for accessing the cloud services from the adjacent data center of the customer. This introduced virtual machine scheduling algorithm is used to reduce propagation time and enhance the execution process to reduce the response time. The main purpose of the introduced DAVmS: Distance Aware Virtual Machine Scheduling Algorithm is to deliver an enhanced virtual machine provision policy to physical hosts and access the services from the adjacent data center. [ABSTRACT FROM AUTHOR]