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In order to improve the efficiency of cloud computing resource utilization and avoid the problem of computing resource allocation and scheduling lagging behind load changes, the author proposes a cloud computing resource on-demand allocation and elastic scheduling method based on network load prediction. Firstly, the author takes the network load data of Wikimedia as the research object and proposes an adaptive two-stage multi network model load prediction method based on LSTM (i.e. ATSMNN-LSTM load prediction method). This method can classify the network load data into climbing and descending types based on the trend and characteristics of the input network load data, And adaptively schedule the input network load data to the LSTM load prediction model that matches its type for prediction based on the classification results. The author proposes a maximum cloud service revenue computing resource quantity search algorithm based on network load prediction (i.e. MaxCSPRNWP algorithm), which aims to improve cloud service revenue as the optimization objective. Under the premise of ensuring task service quality and system stability, the algorithm allocates cloud computing resources on demand and flexibly schedules them in advance based on the predicted network load results. The experimental results show that the ATSMNNLSTM load prediction method proposed by the author can obtain more accurate network load prediction results compared to other load prediction methods, and the MaxCSPR-NWP algorithm, which is based on network load prediction and is capable of effectively converting the network load prediction results into the required number of cloud servers, is the maximum cloud service revenue computing resource quantity search algorithm proposed by the author, not only does it achieve the early allocation and scheduling of cloud computing resources, thereby avoiding the impact of lagging behind in computing resource allocation and scheduling due to load changes on the quality of cloud computing task services and resource utilization efficiency, at the same time, it has also achieved on-demand allocation and flexible scheduling of cloud computing resources with the goal of improving cloud service revenue. [ABSTRACT FROM AUTHOR]

In the modern landscape, exploiting multiple cloud services stands out as a potent strategy for enhancing both data privacy and accessibility. Our research delves into the cost-effective shift towards multicloud storage computing, with a key emphasis on seamlessly integrating on-demand resource provisioning and robust access control policies. By fragmenting datasets across diverse cloud platforms, we bolster privacy while adeptly managing complexities through cutting-edge Multi-Cloud technologies. Our approach dynamically allocates resources and enforces stringent access controls, thus optimizing security and resource utilization. This framework adeptly tackles the challenges associated with managing diverse cloud environments, offering organizations a comprehensive solution for secure and efficient data management in the multi-cloud era. [ABSTRACT FROM AUTHOR]

Cloud computing is a dynamic paradigm which aims to provide on-demand computing resources to a user over the Internet. Most of the existing cloud providers use fixed pricing strategy. However, a few cloud service providers have recently priced their services using auction-style pricing. While cloud consumers take care of Quality of Service (QoS), their preferences for QoS differ from those of cloud providers. Consumers that use the cloud, for example, typically expect high availability, therefore, they need to bring availability, reliability, and scalability together to achieve true high availability. In contrast, high availability entails more expenditures in hardware and/or software resources, resulting in higher expenses for cloud providers. In this paper, we propose a novel auction strategy to include QoS parameters bundle into the price, allocating available cloud services between users, and establishing an auction strategy to enable revenue approximation. In order to accomplish this, we formulate the problem of allocating a cloud service coupled with bundle of QoS parameters to the winners as a multi-attribute auction problem. To address this problem, we present a novel multi-attribute auction pricing model with numerous consumers and a single cloud provider. We also introduce the new QoS Parameter Bundle Pricing Scheme that establishes the relation between a cloud service's bundle of QoS parameters and the estimated price of cloud resources, which can produce more revenue than a fixed pricing strategy. Last, we put forward three algorithms MAAPM (i.e., to solve a multi-attribute auction problem), FMAA (i.e., represents existing state-of-the-art auction mechanisms in the literature) and Fixed-Payment (i.e., represents the currently used fixed payment strategy). Extensive simulations have revealed that MAAPM outperforms FMAA and Fixed Payment in all the metrics except the execution time, also MAAPM results at growing revenues for cloud providers and decreasing expenditure for users. [ABSTRACT FROM AUTHOR]

Resource scheduling is indispensable for enhancing the system performance during big data processing on clouds. It is highly useful for attaining significant utilization of computing resources completely concentrating towards the facilitation of resource scalability and on-demand services. The resources essential for running different applications is determined to be maximum heterogeneous in cloud computing. This heterogeneous resource demand introduces a resource gap in which some of the resource potentialities are drained on par with the other resource potentialities still available in the same server resulting in imbalanced resource utilization. This imbalanced resource allocation condition is more apparent when the computing resources are more heterogeneous. At this juncture, intelligent resource scheduling strategy becomes essential to distribute resources for big data processing by adopting a potential decision-making process that focusses on the objective of achieving necessitated tasks over time. In this paper, Hybrid Gradient Descent Golden Eagle Optimization (HGDGEO) algorithm-based efficient heterogeneous resource scheduling process is proposed for handling the challenges that are highly possible during big data processing in the Hadoop heterogenous cloud environment. This HGDGEO algorithm is proposed as an adaptive resource scheduling strategy that handles the dynamic characteristics of the resources and users' fluctuating demand during big data stream processing by mimicking the golden eagles' intelligence which alternates the speed of tuning at different spiral trajectory stages of hunting. It handles big data processing by adopting two adaptive parameters which completely concentrates on optimal resource allocation to suitable VMs in the shortest time possible depending on their requirements. The simulation results of HGDGEO algorithm confirmed its predominance in terms of makespan, load balance and throughput on par with the competitive resource scheduling algorithms. [ABSTRACT FROM AUTHOR]

Recently, the boosting growth of computation-heavy applications raises great challenges for the Fifth Generation (5G) and future wireless networks. As responding, the hybrid edge and cloud computing (ECC) system has been expected as a promising solution to handle the increasing computational applications with low-latency and on-demand services of computation offloading, which requires new computing resource sharing and access control technology paradigms. This work establishes a software-defined networking (SDN) based architecture for edge/cloud computing services in 5G heterogeneous networks (HetNets), which can support efficient and on-demand computing resource management to optimize resource utilization and satisfy the time-varying computational tasks uploaded by user devices. In addition, resulting from the information incompleteness, we design an evolutionary game based service selection for users, which can model the replicator dynamics of service subscription. Based on this dynamic access model, a Stackelberg differential game based cloud computing resource sharing mechanism is proposed to facilitate the resource trading between the cloud computing service provider (CCP) and different edge computing service providers (ECPs). Then we derive the optimal pricing and allocation strategies of cloud computing resource based on the replicator dynamics of users’ service selection. These strategies can promise the maximum integral utilities to all computing service providers (CPs), meanwhile the user distribution can reach the evolutionary stable state at this Stackelberg equilibrium. Furthermore, simulation results validate the performance of the designed resource sharing mechanism, and reveal the convergence and equilibrium states of user selection, and computing resource pricing and allocation. [ABSTRACT FROM AUTHOR]

Cloud computing is the delivery of on‐demand computing resources. It shares the resources or provides vir‐utilization that enables single user to access various Cloud services such as CPU, memory, storage devices, network, and so on. However, more commercial cloud services offered by several cloud service providers (CSPs) are available in the market place. Most CSPs must, therefore, deal with the dynamic resource allocation where the mobile services are migrating from one cloud to another cloud environment to provide heterogeneous resources based on user needs. There is still a lack of heuristics that are able to check requested resources and available resources to allocate and deallocate before it begins the secure service migration. We proposed a resource allocation security protocol that allows resources to be allocated and migrated efficiently in a secure service migration between cloud infrastructures. Furthermore, formal methods can be used for protocols to verify the desired properties, detecting attacks and producing accurate outcomes. This article presents formal modeling and verification of this abstract protocol using ProVerif cryptographic tool to validate the security properties such as secrecy of resources, authentication from both parties and key exchange in order to securely migrate resources in commercial cloud environments. [ABSTRACT FROM AUTHOR]

Now more than ever, optimizing resource allocation in cloud computing is becoming more critical due to the growth of cloud computing consumers and meeting the computing demands of modern technology. Cloud infrastructures typically consist of heterogeneous servers, hosting multiple virtual machines with potentially different specifications, and volatile resource usage. This makes the resource allocation face many issues such as energy conservation, fault tolerance, workload balancing, etc. Finding a comprehensive solution that considers all these issues is one of the essential concerns of cloud service providers. This paper presents a new resource allocation model based on an intelligent multi-agent system and reinforcement learning method (IMARM). It combines the multi-agent characteristics and the Q-learning process to improve the performance of cloud resource allocation. IMARM uses the properties of multi-agent systems to dynamically allocate and release resources, thus responding well to changing consumer demands. Meanwhile, the reinforcement learning policy makes virtual machines move to the best state according to the current state environment. Also, we study the impact of IMARM on execution time. The experimental results showed that our proposed solution performs better than other comparable algorithms regarding energy consumption and fault tolerance, with reasonable load balancing and respectful execution time. [ABSTRACT FROM AUTHOR]

In order to unify the management and scheduling of cloud resources, cloud platforms use virtualization technology to re-integrate multiple computing resources in the cloud and build virtual units on physical machines to achieve dynamic provisioning of resources by configuring virtual units of various sizes. Therefore, how to reasonably determine the mapping relationship between virtual units and physical machines is an important research topic for cloud resource scheduling. In this paper, we propose a fair cloud virtual machine resource allocation method of using the stable matching theory. Our allocation method considers the allocation of resources from both user’s demand and cloud computing resource provider’s request. When multiple users apply for resources, firstly select a user by user priority, and then deal with this user’s task. Because the user priority is dynamic, so as to avoid a user’s long-term share of resources. This strategy makes user task scheduling is relatively fair. On the basis of weighing the fair allocation of user resources, the stable matching between physical machines and virtual machines is achieved. Our simulation experiments especially given that the main focus of the paper is not to develop a very novel algorithm, but to demonstrate our virtual machine resource allocation method, which effectively improves the average utilization rate of computing resources and reduces the operating costs of cloud providers. [ABSTRACT FROM AUTHOR]

Summary: The exponential rise in decentralized computing demands has made cloud computing an inevitable technical need; however, under exceedingly high load dynamism, ensuring service level agreement (SLA) has remained a challenge. Despite of the fact that heuristic‐driven VM migration or load‐balancing methods are effective towards quality of service (QoS) and reliable cloud services, the major existing solutions ignore the issues of local minima and convergence and hence undergo SLA violation (SLAV). Moreover, most of the existing methods are designed as a reactive scheduling concept that often results into delayed scheduling and hence SLAV or QoS compromise. To alleviate such issues, in this paper, a robust intrinsically modified evolutionary computing‐driven dynamic load‐balancing model (intrinsically modified ant colony system [IMACS]) is proposed for mega cloud infrastructures. To address aforesaid issues, IMACS employs stochastic prediction‐based overload detection, followed by SLA‐sensitive VM selection and IMACS for load balancing. In IMACS, the use of stochastic prediction‐based overload detection method enabled timely hotspot detection and hence improved proactive decision ability. Moreover, it applies minimum migration time‐sensitive VM selection concept that too helped in guaranteeing load balancing without undergoing downtime or SLAV. To improve VM migration, unlike classical heuristic methods, we designed IMACS with multitype population, adaptive pheromone update, adaptive pheromone diffusion, and coevolution concept, which not only helped in reducing the search space and allied computation but also enabled time‐efficient scheduling decision. The overall proposed model was realized in sync with two controllers named local controller and global controller. Here, the first controller collects the real‐time VM and VM manager (VMM) information which is used to detect overloaded host and the VM to be migrated. While the global controller is designed with IMACS to exploit the information from local controller to execute QoS‐sensitive VM migration. The proposed IMACS‐driven load‐balancing model was realized with CloudSim platform were simulating it over 800 number of independent VMs, each encompassing autonomous task, the performance was analyzed in terms of SLAV, number of migrations, SLA per active host, and energy consumption. Simulation results revealed that the proposed model exhibits 20% higher migration, while retaining almost 19% better SLA in comparison to the other state‐of‐art methods like genetic algorithm or classical ant colony system‐based load‐balancing methods. IMACS exhibited 30% higher host shutdown signifying better energy efficiency (15% lower power consumption). [ABSTRACT FROM AUTHOR]

In recent years, cloud computing can be considered an emerging technology that can share resources with users. Because cloud computing is on-demand, efficient use of resources such as memory, processors, bandwidth, etc., is a big challenge. Despite the advantages of cloud computing, sometimes it is not a proper choice due to its delay in responding appropriately to existing requests, which led to the need for another technology called fog computing. Fog computing reduces traffic and time lags by expanding cloud services to the network and closer to users. It can schedule resources with higher efficiency and utilize them to impact the user's experience dramatically. This paper aims to survey some studies that have been done in the field of scheduling in fog/cloud computing environments. The focus of this survey is on published studies between 2015 and 2021 in journals or conferences. We selected 71 studies in a systematic literature review (SLR) from four major scientific databases based on their relation to our paper. We classified these studies into five categories based on their traced parameters and their focus area. This classification comprises 1—performance 2—energy efficiency, 3—resource utilization, 4—performance and energy efficiency, and 5—performance and resource utilization simultaneously. 42.3% of the studies focused on performance, 9.9% on energy efficiency, 7.0% on resource utilization, 21.1% on both performance and energy efficiency, and 19.7% on both performance and resource utilization. Finally, we present challenges and open issues in the resource scheduling methods in fog/cloud computing environments. [ABSTRACT FROM AUTHOR]

Cloud computing is arising as a popular computing paradigm, as it is good in offering its users on-demand scalable resource-based services over the internet. In peak hours, a single cloud is not efficient in serving an application; therefore, the collaborative cloud model has been introduced. Collaborative cloud computing (CCC) uses the globally-scattered distributed cloud resources of the diverse organizations collectively in a cooperative manner to provide the required service to the user. The allocation, as well as the management of the resources, is being a challenging task in the CCC, due to the heterogeneity of the resources. On the other hand, the assurance of the Quality of Service (QoS) and reliability of these resources is challenging. Further, it would be efficient if the resources are provided based on the system behavior. In this research work, a novel trust computing model is developed, which predicts both the QoS and Trust via analyzing the system behavior. The proposed model encloses three major phases: Trust-QoS behavior estimation, resource matching and resource allocation. Initially, the QoS and Trust behavior of the system is estimated via a Neural Network (NN) model. Subsequently, the resource allocation is performed using the parallel resource matching framework, which is based on the concept of Map-Reduce. More particularly, the precious resource allocation is achieved by an optimization logic called Improved Grey Wolf Optimizer (I-GWO). Here, the improvement of GWO emphasis the consideration of both the best and worst fitness. [ABSTRACT FROM AUTHOR]

With the evolutionary era of IoT and its implied changes in area of computing, Distributed systems have resurfaced as Cloud Computing, Grid Computing, Utility Computing, Cluster Computing, Edge Computing and Fog Computing. Load balancing is very vital in distributed systems for optimum usage of resources: computing power, storage, bandwidth, latency while maintaining the required levels of security and fault tolerance. The research on this topic is very extensive with varying algorithms, architectures, programming models, communication protocols. This research survey paper makes a comparison of the research works, research gaps and related future scope for improvement through bridging the gap. [ABSTRACT FROM AUTHOR]

In order to meet the demand for efficient computing services in big data scenarios, a cloud edge collaborative computing allocation strategy based on deep reinforcement learning by combining the powerful computing capabilities of cloud is proposed. First, based on the comprehensive consideration of computing resources, bandwidth, and migration decisions, an optimization problem is constructed that minimizes the sum of all user task execution delays and energy consumption weights. Second, a dynamic offloading scheduling algorithm based on Q -learning is proposed based on the optimization problem. This algorithm makes full use of the computing power for cloud and edge, which effectively meets the demand for efficient computing services in Internet of Things' scenarios. Finally, facing the environment dynamic changes of edge nodes in edge cloud, the algorithm can adaptively adjust the migration strategy. Experiments show that when the number of Internet of Things' devices is 30, the total energy consumption of Internet of Things' devices of proposed algorithm is reduced by 24.67% and 19.44%, respectively, compared with other algorithms. The experimental results show that proposed algorithm can effectively improve the success rate of task offloading and execution, which can reduce the local energy consumption. [ABSTRACT FROM AUTHOR]

Cloud resources provide various categories of (VM) virtual machine requests which is assigned with clients for an exact timespan. Currently, the method of VM scheduling within the Cloud environment is decided by a fixed-price scheduling algorithm, during which the user pays a fixed amount per unit time so as to obtain the resources. However, such a scheduling algorithm is not effective for Cloud even though the Cloud resources are dynamically allocated and released. To address this issue, the adaptive scheduling algorithm called as Dynamic pricing based Combinatorial Auction allocation mechanism is proposed. It will be used to increase the resource utilization as well as user satisfaction through dynamic pricing with the combinatorial auction. Our proposed market-based scheduling algorithm uses the principle of auction mechanism for the purpose of extends the satisfaction of Cloud suppliers and clients. This technique reconstructs the current preferences of resource allotment so as to allot resources in advance for emergent virtual machine demands. Then Collective-target augmentation numerical prototype is demonstrated, which forms the minimal execution equivalent range connecting physical machines with virtual machines, and the objective of resource allotment is to obtain minimal quantity of physical machines. The simulation experimental outcomes express that the proposed scheduling methodology and Collective-target augmentation numerical prototypes are capable to adequately increase the quality of service (QoS), improves profit of suppliers and resource utilization. • In our paper, we proposed Dynamic pricing with combinatorial action mechanism to get efficient performance. • Our DPCAM provides higher user satisfaction. • It increases resource utilization rate. • Using our scheme all the providers may get a chance to win an auction. • So that quality of service can be achieved. [ABSTRACT FROM AUTHOR]

Axiomatic Foundations for Nonadditive Multiattribute Portfolio Utility Functions Selecting which portfolio of project candidates (e.g., products, infrastructure investments, or policy options) to implement with the available resources is an important decision problem faced frequently by organizations. Such decisions are commonly supported with additive portfolio models in which portfolio utility is computed as the sum of the projects' multiattribute utilities. Nevertheless, such models lack decision-theoretic justification and can therefore result in decision recommendations that are misaligned with the decision maker's preferences. In "Nonadditive Multiattribute Utility Functions for Portfolio Decision Analysis," J. Liesiö and E. Vilkkumaa establish the axiomatic foundations of a more general class of multilinear portfolio utility functions, which includes additive and multiplicative portfolio utility functions as special cases. They also develop preference elicitation techniques to assess these portfolio utility functions and optimization models that enable the use of standard off-the-shelf optimization software to identify the most preferred portfolio in view of resource and other constraints. The results of this paper enable the use of portfolio decision analysis in practical applications even when the restrictive preference assumptions underlying the additive model do not hold. Portfolio decision analysis models support selecting a portfolio of projects in view of multiple objectives and limited resources. In applications, portfolio utility is commonly modeled as the sum of the projects' multiattribute utilities, although such approaches lack rigorous decision-theoretic justification. This paper establishes the axiomatic foundations of a more general class of multilinear portfolio utility functions, which includes additive and multiplicative portfolio utility functions as special cases. Furthermore, we develop preference elicitation techniques to assess these portfolio utility functions as well as optimization models to identify the most preferred portfolio in view of resource and other constraints. We also examine how the functional form of the portfolio utility function affects decision recommendations by using randomly generated and real problem instances. [ABSTRACT FROM AUTHOR]

Cloud computing is a demanding business platform for services related to the field of IT. The goal of cloud customers is to access resources at a sustainable price, while the goal of cloud suppliers is to maximize their services utilization. Previously, the customers would bid for every single resource type, which was a limitation of cloud resources allocation. To solve these issues, researchers have focused on a combinatorial auction in which the resources are offered by the providers in bundles so that the user bids for their required bundle. Still, in this allocation mechanism, some drawbacks need to be tackled, such as due to the lower average bid price the users are dropped from the auction process. To solve this problem, we proposed a "Negotiation based Combinatorial Double Auction Mechanism for Resource Allocation (N-CDARA) in cloud computing". The proposed method negotiates with dropped users. Lower average bid price users are asked by our proposed mechanism to increase their bids, as by the quoted bids they will be dropped by the auctioneer. Most of the users that are close to winning accept the proposal and increase their bid prices. The proposed mechanism is implemented in a CloudSim simulation toolkit. Results are compared with the latest model and performance study shows that in our proposed scheme more users win and get their requested services and the utilization of offered services is increased up to 18.4% than the existing schemes. [ABSTRACT FROM AUTHOR]

Edge computing is able to provide proximity solutions for the future wireless network to accommodate different types of devices with various computing service demands. Meanwhile, in order to provide ubiquitous connectivities to massive devices over a relatively large area, densely deploying remote radio head (RRH) is considered as a cost-efficient solution. In this work, we consider a vertical and heterogeneous multi-access edge computing system. In the system, the RRHs are deployed for providing wireless access for the users and the edge node with computing capability can process the computation requests from the users. With the objective to minimize the total energy consumption for processing the computation task, a joint radio resource allocation and offloading decision optimization problem is presented under the explicit consideration of capacity constraints of fronthaul and backhaul links. Due to the non-convexity of the formulated problem, we divide the original problem into several sub-problems and address them accordingly to find the optimal solution. Extensive simulation studies are conducted and illustrated to evaluate the advantages of the proposed scheme. [ABSTRACT FROM AUTHOR]

Service Oriented Architecture (SOA) uses the concept of distributed systems. It is an underlying framework that manages different services using component-based software engineering that invoke interfaces in collaboration with web services management. SOA is known as the backend behind all service oriented systems like cloud computing, grid computing, utility computing and web services. In this paper SOA covers the gap between implementations of web services and consuming applications giving a logical view of all the available resources. This paper focuses on proposing an approach to resource allocation management across SOA environment to deal with how these resources are managed and allocated to these services. The approach is known as Active/Standby Resource Management Allocation (ASRMA). The main concern for the proper management of these resources is to minimize waiting time of request to be allocated resources thereby meeting an aspect of the Quality of Service (QoS) to multiple providers and requesters as well. Resource allocation basically deals with assigning resources to each incoming service request, these allocations deal with time and limited resources. The challenge of ever increasing needs of users or consumers with limited service resources such as CPU time, memory capacity and network bandwidth prompted the need for quality of service thereby improving proper allocation of these resources. This approach can be achieved by having more resources on standby in case of over utilization or underutilization of the active resources. [ABSTRACT FROM AUTHOR]

This paper mainly proposes and solves a two-phase truck-cargo (T-C) matching model for truck alliance and task set in drop-and-pull (D-P) transport. Firstly, the candidate trucks were selected for each task through feature matching between the set of trucks and the set of tasks. Next, the trucks were grouped into a truck alliance; the demand-capacity fitness (DCF) between the truck alliance and task set was computed based on the demand vector of the task set and the demand vector of the truck alliance; a T-C matching model for truck alliance and task set was established to maximize the DCF. Finally, the GA was introduced to solve the proposed model on MATLAB with and without truck alliance. The solving results show that the truck alliance can effectively improve the DCF, and optimize the allocation of social resources. [ABSTRACT FROM AUTHOR]

Resource allocation is vital for improving system performance in big data processing. The resource demand for various applications can be heterogeneous in cloud computing. Therefore, a resource gap occurs while some resource capacities are exhausted and other resource capacities on the same server are still available. This phenomenon is more apparent when the computing resources are more heterogeneous. Previous resource-allocation algorithms paid limited attention to this situation. When such an algorithm is applied to a server with heterogeneous resources, resource allocation may result in considerable resource wastage for the available but unused resources. To reduce resource wastage, a resource-allocation algorithm, called the minimizing resource gap (MRG) algorithm, for heterogeneous resources is proposed in this study. In MRG, the gap between resource usages for each server in cloud computing and the resource demands among various applications are considered. When an application is launched, MRG calculates resource usage and allocates resources to the server with the minimized usage gap to reduce the amount of available but unused resources. To demonstrate MRG performance, the MRG algorithm was implemented in Apache Spark. CPU- and memory-intensive applications were applied as benchmarks with different resource demands. Experimental results proved the superiority of the proposed MRG approach for improving the system utilization to reduce the overall completion time by up to 24.7% for heterogeneous servers in cloud computing. [ABSTRACT FROM AUTHOR]

Cloud computing is a hybrid paradigm which makes use of utility computing, high performance cluster computing and grid computing and it offers various benefits such as flexibility, expandability, little or almost no capital investment, disaster recovery, moveable work space and much more. However, due to constantly increasing number of data centers worldwide, the issue of energy consumption by these data centers has attracted attention of researchers. Resource allocation and resource utilization are the major criterion in which the problem of energy efficiency can be addressed. In this research, we aim to provide energy-efficient resource allocation using Multi-Objective Optimization (MOO) method. Further, We propose MOO-based virtual machine (VM) allocation policy and implement it in CloudSim environment. The results are compared with existing policies. The results depict that MOO-based policy leads to saving in energy due to efficient resource allocation, without compromising performance of data center operations. [ABSTRACT FROM AUTHOR]

The demand for cloud computing infrastructure is increasing day by day to meet the requirement of small and medium enterprises. The data center-centric cloud technology has a high share of energy consumption from the IT-industry. The amount of energy consumption in a data center depends on the allocation of user service requests to virtual machines running on the different host. Minimization of energy consumption in the data center is a significant issue and addressed by optimal allocation of cloud resources. In this paper, we have discussed how service allocation strategies have been used to optimize the energy consumption in a cloud system. A generalized system architecture is presented based on which we define the service allocation problem and energy model. Further, we present the taxonomy of various energy-efficient resource allocation techniques found in the literature. In the end, various research challenges related to the energy-efficient service allocation in cloud are discussed. [ABSTRACT FROM AUTHOR]

Cloud computing is a utility model that offers everything as a service and supports dynamical resource provisioning and auto-scaling in data center. The proper load balancing and dynamic resource provisioning improves cloud performance and attracts the cloud users. This impacts a need for adaptive and automated provisioning of resources, aligned with clients' Service Level Agreement (SLA) amidst the time variant cloud environment. The focus of our work is to study how speculative analysis can be used to predict exact resources for an application, whose accuracy demands solution for under/over-utilization of the resource. We have tested our simulator with varying resource load and proved that our system reduces resource allocation latencies and SLA violations. Experimental results show that our proposed model offers more adaptive resource provisioning, as compared to heuristic and other machine learning algorithms. Our experimental results demonstrate adaptive resource allocation over customer-driven service management, under the rapidly changing requirements of cloud computing. [ABSTRACT FROM AUTHOR]

The fast-developing of cloud computing causes the resource management to the hot and heat research. Some researcher have studied the resource allocation and proposed some resource allocation mechanisms in the cloud computing, such as max–min fairness that is used in the data center. In order to satisfy the demand of cloud computing, we need to design a efficient and fair resource allocation mechanism. Wang et al. (Proceedings of the USENIX Conference on File and Storage Technologies (FAST), 229–242, 2014) proposed a new resource allocation mechanism, called balancing fairness and efficiency with bottleneck-aware allocation (BAA). BAA aims to find the fair between the users and maximize the resource utilization. However, BAA only consider the two resource types and the resource pool may have multiple resource types such as CPU, memory and storage. In addition, BAA consider the static allocation and do not take into account the dynamic allocation of users join the system one by one. To over this drawback, we propose the bottleneck-aware allocation of multiple resources (MRBAA) and dynamic bottleneck-aware allocation (DBBA) fair allocation mechanism. MRBAA and DBBA have lots of good properties. In addition, we characterizes the properties of our proposed mechanisms. Furthermore, our proposed mechanisms achieves the multiple resources fair and dynamic allocation to become more adaptable the real-world scenarios. Compared with the existing popular mechanism dominant resource fairness (DRF) from the literature, the simulation results show that our proposed mechanisms can efficient use of heterogeneous resources, increase multiple resources utilization, and schedule more tasks to benefit users. [ABSTRACT FROM AUTHOR]

Mobile edge computing has attracted great interests in the popularity of fifth-generation (5G) networks and Internet of Things. It aims to supply low-latency and high-interaction services for delay-sensitive applications. Utilizing mobile edge computing with Smart Home, which is one of the most important fields of Internet of Things, is a method to satisfy users' demand for higher computing power and storage capacity. However, due to limited computing resource, how to improve efficiency of resource allocation is a challenge. In this article, we propose a hierarchical architecture in Smart Home with mobile edge computing, providing low-latency services and promoting edge process for smart devices. Based on that, a Stackelberg Game is designed in order to allocate computing resource to devices efficiently. Then, one-tomany matching is established to handle resource allocation problems. It is proved that the allocation strategy can optimize the utility of mobile edge computing server and improve allocating efficiency. Simulation results show the effectiveness of the proposed strategy compared with schemes based on auction game, and present performance with different changing system parameters. [ABSTRACT FROM AUTHOR]

The concept of network slicing (NS) has been proposed for flexible resource provisioning where a physical resource is partitioned into logically independent networks on demand. The NS resource allocation implies the definition of a feasible path in the infrastructure network with adequate resource availability. However, due to complex structural characteristics of the backhaul transport network, a number of issues arise when fast deploying the end-to-end (E2E) slices onto network infrastructures. In this paper, a pair-decision resource allocation model is firstly formulated to construct the mapping relationship between logical networks and substrate networks in a coordinated way. In order to improve extreme quality of service (QoS) and user experiment, latency-optimal virtual resource allocation problem is defined, subject to the backhaul capacity and bandwidth constraints. The problem is formulated as an integer linear programming (ILP) and solved with the branch-and-bound scheme, whose resolution yields an optimal virtual network function (VNF) placement and traffic routing policy. Numerical results reveal that the proposed scheme can enable the transport network latency optimization with a reduction of up to 30% and 41.6% compared to the Network Slice Design Problem (NSDP) and Random Fit Placement Algorithm (RFPA) schemes respectively. In the meanwhile, the network load balance and serviceability have been improved efficiently with better resource utilization as well. [ABSTRACT FROM AUTHOR]

With the construction and application of large-scale datacenters, the issue of resource allocation in cloud computing becomes a serious concern. Although the current static allocation method can make applications get corresponding resources, there still exist some shortcomings such as resource surpluses or shortages. This kind of problem is more crucial in real-time requirements of mobile cloud computing service. Therefore, it is necessary to establish a forecasting model to predict the future resource demands, and then perform on-demand distribution, which can effectively reduce the unnecessary daily network management fees and address the issues mentioned above. This paper focuses on CPU resource forecasting, establishing three forecasting models including Markov chain, weighted Markov chain and stacking weighted Markov chain. By comparing and analyzing the experiment results, the most reasonable forecasting model is found and explained. [ABSTRACT FROM AUTHOR]

The measurement of an individual’s heart rate has many relevant theoretical implications for communication research. However, there are reasons to believe that the most common way of reporting cardiac response—beats per minute (BPM)—might not be appropriate for all methodological situations. This article presents an overview of the historical use of cardiac activity within communication research, and provides a summary of the current conceptualizations of attention and resource allocation. To fully understand the difference between BPM and interbeat interval (IBI)—an alternate method for reporting cardiac response—a comparative analysis was performed on a data set from a previously published study. This article concludes with a set of suggestions that researchers may want to consider when utilizing BPM or IBI within communication research. [ABSTRACT FROM AUTHOR]

Cloud computing is a new computing paradigm which features renting the computation devices instead of buying them. In a typical cloud computing environment, there will always be different kinds of cloud resources and a number of cloud services making use of cloud resources to run on. As we can see, these cloud services usually have different performance traits. Some may be I/O-intensive, like those data querying services, while others might demand more CPU cycles, like 3D image processing services. Meanwhile, cloud resources also have different kinds of capabilities such as data processing, I/O throughput, 3D image rendering, etc. A simple fact is that allocating a suitable resource will greatly improve the performance of the cloud service, and make the cloud resource itself more efficient as well. In this paper, a new cloud resource allocating algorithm via fitness-enabled auction is proposed to guarantee the fitness of performance traits between cloud resources (sellers) and cloud services (buyers). We study the allocating algorithm in terms of economic efficiency and system performance, and experiments show that the allocation is far more efficient in comparison with the continuous double auction in which the idea of fitness is not introduced. [ABSTRACT FROM PUBLISHER]

While constructing virtual enterprises, it is crucial to flexibly integrate heterogeneous business resources and processes of different business partners and make them collaborate dynamically. Keeping involved IT systems or components as autonomous and loose-coupled services, the 'Everything as a Service' concept supports flexible integration of heterogeneous applications. We adopt this concept and analyze the challenges in virtual enterprise construction, then propose a service platform for on-demand virtual enterprises. The platform supports flexible integration of networked resources, and facilitates virtual enterprise construction with business process utility, trusted service composition and data service centric business collaborations. At the end of the paper, together with a case study, experimental evaluations in contexts of concurrent multi-users are presented, showing the effectiveness and performance of the platform. [ABSTRACT FROM AUTHOR]

A model for job flow scheduling and fair resource sharing in distributed computing environments is proposed. The model helps one to take into account requirements in the virtual organization imposed by the users on efficiency and job performance quality. With the proposed slot selection algorithms, one can find alternatives optimal with respect to a given criterion for each job in the batch. [ABSTRACT FROM AUTHOR]

Cloud computing is a utility computing paradigm that has become a solid base for a wide array of enterprise and end-user applications. Providers offer varying service portfolios that differ in resource configurations and provided services. A comprehensive solution for resource allocation is fundamental to any cloud computing service provider. Any resource allocation model has to consider computational resources as well as network resources to accurately reflect practical demands. Another aspect that should be considered while provisioning resources is energy consumption. This aspect is getting more attention from industrial and government parties. Calls for the support of green clouds are gaining momentum. With that in mind, resource allocation algorithms aim to accomplish the task of scheduling virtual machines on the servers residing in data centers and consequently scheduling network resources while complying with the problem constraints. Several external and internal factors that affect the performance of resource allocation models are introduced in this article. These factors are discussed in detail, and research gaps are pointed out. Design challenges are discussed with the aim of providing a reference to be used when designing a comprehensive energy-aware resource allocation model for cloud computing data centers. [ABSTRACT FROM PUBLISHER]

Utility computing models have long been the focus of academic research, and with the recent success of commercial cloud providers, computation and storage is finally being realized as the fifth utility. Computational economies are often proposed as an efficient means of resource allocation, however adoption has been limited due to a lack of performance and high overheads. In this paper, we address the performance limitations of existing economic allocation models by defining strategies to reduce the failure and reallocation rate, increase occupancy and thereby increase the obtainable utilization of the system. The high-performance resource utilization strategies presented can be used by market participants without requiring dramatic changes to the allocation protocol. The strategies considered include overbooking, advanced reservation, just-in-time bidding, and using substitute providers for service delivery. The proposed strategies have been implemented in a distributed metascheduler and evaluated with respect to Grid and cloud deployments. Several diverse synthetic workloads have been used to quantity both the performance benefits and economic implications of these strategies. [ABSTRACT FROM AUTHOR]

Abstract: We consider the problem of providing QoS guarantees to Grid users through advance reservation of resources. Advance reservation mechanisms provide the ability to allocate resources to users based on agreed-upon QoS requirements and increase the predictability of a Grid system, yet incorporating such mechanisms into current Grid environments has proven to be a challenging task due to the resulting resource fragmentation. We use concepts from computational geometry to present a framework for tackling the resource fragmentation, and for formulating a suite of scheduling strategies. We also develop efficient implementations of the scheduling algorithms that scale to large Grids. We conduct a comprehensive performance evaluation study using simulation, and we present numerical results to demonstrate that our strategies perform well across several metrics that reflect both user- and system-specific goals. Our main contribution is a timely, practical, and efficient solution to the problem of scheduling resources in emerging on-demand computing environments. [Copyright &y& Elsevier]

Optical backbone networks are becoming increasingly intelligent and flexible. These networks are able to establish high-bandwidth wavelength connections on-demand to support future network-centric applications. Choosing an efficient path in a timely manner, while considering important criteria such as operation costs and network performance, is a key problem confronting the network operators. Subtle path preferences of different dynamic routing algorithms (which are usually ignored by traditional analysis techniques) can make a significant difference in performance on mesh networks. It opens new research to advance routing algorithms in both analysis and implementation paradigms. In this paper, we propose an opportunity cost model that provides fast and accurate analysis for threshold-based online congestion-aware routing algorithms. The model is simple to compute, robust to different network topologies, and scalable. We show that our model further aids in the design of a number of new routing algorithms that can be easily applied to practical networks. In contrast to previous work, the optimal threshold values for our algorithms can be identified analytically, and the values sustain good performance on different network topologies and sizes. [ABSTRACT FROM PUBLISHER]

The article discusses interconnection networks, examining the architectural challenges faced in creating utility computing data centers (UCDC). In utility computing, the article states, computing resources are assigned to customers on-demand, allowing customers to pay only for the computing power they use. Virtual servers that contain a subset of resources that can fulfill user demands are dynamically created by a UCDC. Also discussed are interconnection networks than can support seamless virtualization.

Launching and supporting successful maintenance projects is an overlooked aspect of maintenance management research. This paper examines the effects of four important factors on project success: resource allocation, team leader authority, significant project objectives, and top management involvement. Additionally, we investigate the moderating effects of top management involvement on the relationship between inputs and team performance. By examining the responses from 60 industrial teams we found that a project team's ability to function efficiently is positively related to resource allocation and significant project objectives and negatively related to team leader authority and top management involvement. A project team's ability to achieve its business goals was positively related to significant project objectives. Moreover, the relationship was stronger for significant project objectives and project efficiency when top management was involved. The moderator also enhanced the team leader authority-goal achievement relationship but negatively impacted the team leader authority-project efficiency relationship. Implications are discussed. [ABSTRACT FROM AUTHOR]

In on-demand computing services, the customer pays based on actual usage, and the service provider is free to allocate unused capacity to other customers. Capacity planning becomes an important issue in such a shared environment. It is desirable that a portfolio effect occurs so that less total capacity is required in a shared system than in a dedicated system. In this article a quantile-based statistical approach is proposed to quantify the portfolio effect and the associated risks. It is shown that the portfolio effect may or may not exist for a given Set of workloads, depending crucially on their joint distribution and the assumed risk levels. For Gaussian workloads, the portfolio effect almost always exists regardless of the statistical correlation and risk level. For non-Gaussian workloads, the portfolio effect is not guaranteed and may even be negative, even if the workloads are negatively correlated or statistically independent. However, when simultaneously recorded workload history is available, the portfolio effect can be estimated directly from the data. Based on the data-driven approach, an optimization problem is formulated for capacity planning with the aim of maximizing the portfolio effect for a given set of workloads. This problem calls for consolidation of the workloads into one or more portfolios so that each portfolio can be served satisfactorily by a dedicated system and the total capacity requirement is minimized. Iterative algorithms are proposed to solve the optimization problem numerically. The method is applied to a server consolidation problem with real workload data. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach called VSP (Variable Size Packets) for satellite access lower layers. The VSP solution aims at optimizing the transport of packets issued from network layers with highly variable sizes. Therefore, VSP relies on the definition of multiple encapsulation sizes associated to a waveform, and an access adapted to the transport of variable size packets. We recall the VSP concept, insisting on its impact at the physical and access layers, and present an efficient coding and modulation scheme for the VSP solution, resources allocation constraints and packet sizes and slot sizes definition strategies. [ABSTRACT FROM AUTHOR]

The article presents a formulation and a solution method for the leasing problem for on-demand computing. The purpose is to minimize the cost of leasing resources while still keeping an adequate quality of service that we evaluate by the average wait time of requests. The problem is demonstrated as a infinite-horizon, continuous-time Markov decision problem. Results showed that the shape of the optimal cost function is convex-like in the number of resources leased and non-decreasing in the number of requests in the system. It is then concluded that when demand for the service is recognized to exist, but is unpredictable, it is advisable to lease more than fewer resources.

This retrospective on 20 years of ubiquitous computing research identifies opportunities for leveraging utility computing and the Internet of Things to grow the ubicomp infrastructure, and discusses remaining challenges to taking ubicomp systems to where they indeed become ubiquitous. [ABSTRACT FROM PUBLISHER]