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Research on resource management focuses on optimizing system performance and energy efficiency by distributing shared resources like processor cores, caches, and main memory among competing applications. This research spans a wide range of applications, including those from high-performance computing, machine learning, and mobile computing. Existing research frameworks often simplify research by concentrating on specific characteristics, such as the architecture of the computing nodes, resource monitoring, and representative workloads. For instance, this is typically the case with cloud systems, which introduce additional complexity regarding hardware and software requirements. To avoid this complexity during research, experimental frameworks are being developed. Nevertheless, proposed frameworks often fail regarding the types of nodes included, virtualization support, and management of critical shared resources. This paper presents Stratus, an experimental framework that overcomes these limitations. Stratus includes different types of nodes, a comprehensive virtualization stack, and the ability to partition the major shared resources of the system. Even though Stratus was originally conceived to perform cloud research, its modular design allows Stratus to be extended, broadening its research use on different computing domains and platforms, matching the complexity of modern cloud environments, as shown in the case studies presented in this paper. [ABSTRACT FROM AUTHOR]

Scheduling tasks in the cloud system is the main issue that needs to be addressed in order to improve customer satisfaction and system performance. This paper proposes DCOHHOTS, a novel multi-objective task scheduling algorithm based on a modified Harris hawks optimizer. In overall, this paper has two main stages. As the first step, DCOHHO is introduced as a new version of Harris Hawks Optimizer. Using the Differential Evolution algorithm, an optimal configuration is selected from the chaotic map, the opposition-based learning, and the ratio of the population. In order to improve the performance of the Harris Hawks Optimizer, this optimal configuration is applied to initialize the hawk's position. In the second stage, DCOHHOTS, a DCOHHO-based Task Scheduling algorithm, is proposed. Multi-objective behavior in the proposed task scheduling algorithm optimizes resource utilization to decrease the makespan, energy consumption, and execution cost. Moreover, prioritizing tasks before submitting them to the scheduler is done using the hierarchical process in the DCOHHOTS algorithm. For the purpose of investigating the performance of the proposed DCOHHO algorithm, a number of experiments are conducted using 20 standard functions and twelve algorithms. The experimental results demonstrate that the DCOHHO algorithm is superior at determining the optimal test function solutions. Additionally, makespan, execution cost, resource utilization, and energy efficiency of DCOHHOTS task scheduling algorithms are analyzed. Compared to existing algorithms, the proposed algorithm saves up to 16% energy in heavy loads. Additionally, resource utilization has increased by 17%. Compared to the conventional algorithm, the proposed algorithm reduced makepan and execution cost by 26% and 8%, respectively. [ABSTRACT FROM AUTHOR]

This paper proposes a novel cloud computingbased parallel deep reinforcement learning (DRL) energy management strategy (EMS) for connected plug-in hybrid vehicles. First, a proximal policy optimization (PPO) algorithm is developed. Since the cloud computing can reduce the computational burden of the connected vehicles, the PPO is deployed in the proposed cloud computing-based EMS. In order to improve the strategy adaptation, a parallel mechanism is proposed to achieve the information interaction with multiple vehicles. Considering the real-time control requirements, thread pool is proposed and applied in the cloud computing based parallel EMS. The thread pool-based strategy provides an efficient real-time control ability and strategy improvement solution. To verify the PPO based EMS, the dynamic programming, deep Q-network and double deep Q-network strategies are developed for comparison. It is found that the PPO can achieve similar fuel efficiency improvement with the DP strategy among the three DRL algorithms. For parallel training of multiple connected vehicles, the cloud computing-based parallel EMS improves fuel economy by approximately 7.7%. Threadpool based parallel real-time EMS reduces average time for computational interactions by 20% and further improves the fuel efficiency. The proposed strategy has the advantages of realtime control, adaptability and continuous learning for improved fuel efficiency. [ABSTRACT FROM AUTHOR]

A mechanism of fog computing environment is employed in order to enhance the cloud computing services toward the edge devices in a range of locations with low latency. A fog computing environment is effective when compared to cloud computing for providing communication between various edge devices such as smart devices and mobile devices used by users in the same location. Even though fog servicing extends the best services of cloud computing, it also suffers from a set of security threats like authentication, key management, data privacy and trust management. Authentication with effective key management between edge devices is the most pressing security issue in fog computing. This paper proposes an effective two-way authentication between edge devices with key management in fog computing environments (TAKM-FC). The edge nodes are the user's mobile devices and set of smart devices controlled by the fog server. To improve the proposed authentication system, we have made use of techniques like fuzzy extractor and one-way hash with cryptographic primitives. The proposed TAKM-FC scheme is validated mathematically based on the ROR model and then verified using the ProVerif tool. The TAKM-FC scheme has been evaluated using iFogSim to measure the performance parameters like throughput, end-to-end delay, packet loss, energy consumption and network usage. The overhead analysis of the proposed scheme is carried out and shows that the computation cost, communication cost and storage cost are improved by 11–21%, 8–19% and 6–13%, respectively, compared to existing schemes. [ABSTRACT FROM AUTHOR]

Automobiles have undergone a transformation during the past two decades due to the merger of the electronics and automotive industries. The combination of autos and electronic sensors has resulted in a new generation of vehicles known as autonomous vehicles (AVs). These AVs have a few hundred thousand sensors, producing an enormous amount of raw data for computation. Data from the vehicular network can be offloaded to existing telecommunication infrastructure to address the problem of processing resources. In order to address vehicular network requirements, large-capacity servers deployed in major telecommunications networks are first used to offload resource-intensive tasks. Mobile Cloud Computing (MCC) is a critical enabling technology for 5 G networks, which has a key feature of offloading to divide application tasks into local and cloud server execution components. This paper proposes a novel Three TierEdge cloud computing (T2 EC2) system which uses an Energy-aware Dynamic Task offloading and collaborative task execution algorithm (EA-DTOCTE) for multilayer vehicular cloud computing networks. The EA-DTOCTE algorithm is included in the decision-making engine in the proposed system, which selects whether to offload the task to the remote environment or implement it locally. EA-DTOCTE focuses on consumption of energy by tasks both locally and remotely since its goal is to efficiently and dynamically split the application into tasks and schedule them on local devices and cloud resources. The proposed T2 EC2 has been evaluated in terms of parameters such as energy consumption, completion time, and throughput. Experimental results indicate that the proposed T2EC2 can save up to 28% of system energy consumption compared with other state-of-art techniques. [ABSTRACT FROM AUTHOR]

After the service provider temporarily selects the required edge nodes based on social and storage capabilities, application execution causes the edge nodes to cache part of the application data. Therefore, offloading part of the application computing tasks to the selected edge nodes can effectively improve application execution performance. However, in cases where the resources of user's IoT devices are insufficient, tasks can be further offloaded to traditional edge servers or even to the cloud to maximize application execution efficiency. In this paper, the entire uninstall utility is modeled as a weighted sum of task completion time and energy consumption. Under the premise of considering users' preferences for completion time and energy consumption, a game-based uninstallation algorithm is proposed. The algorithm performs uninstallation by optimizing the uninstallation decision. Based on user preferences, the total system overhead is relatively small. The subsequent simulation experiments show that the algorithm can reduce system overhead on the basis of satisfying user preferences and has relatively good adaptability. [ABSTRACT FROM AUTHOR]

As users have higher and higher requirements for the quality of experience, traditional cloud computing is gradually unable to meet the needs of user equipments. Hence mobile edge computing networks mounted by unmanned aerial vehicles are introduced to improve user experience and reduce energy consumption. However, most current work is based on neural networks, which require large amounts of labeled data or long training times. Given these challenges, this paper proposes an energy-efficient multi-stage alternating optimization scheme to reduce the weighted energy consumption of the entire network. We analyze the energy consumption of each device and formulate a non-convex optimization problem. Considering the impact of task offloading, resource allocation, and path planning on network energy consumption, we transform the energy consumption problem into three subproblems. And use the coordinate descent algorithm, interior point method, and successive convex approximation method to optimize them alternately. The simulation results show that the proposed optimization scheme can significantly reduce the network's total energy consumption. [ABSTRACT FROM AUTHOR]

The cloud-computing paradigm based on containers has progressively grown in recent years as a flexible strategy that has proven to be energy efficient. The increasing usage of the container as a service technology in data centers (DCs) among cloud providers highlights the necessity of the container installation design phase in cloud environments. Cloud providers attempt to enhance resource utilization and reduce energy consumption by employing various VM selection and placement policies. This procedure for placement acquires a new aspect, with containers now being deployed on virtual machines (VMs) and those guest VMs being installed on physical machines (PMs). The intricacy of this issue increases when the variety of the containers, VMs, and PMs is taken into account. In this paper, an optimal placement strategy for containers is proposed based on the bio-inspired algorithms. The firefly algorithm has been modified to use discretization strategy (Discrete Firefly Algorithm, DFF) and has also used local search mechanism (Discrete Firefly with Local Search Mechanism, DFFLSM). The proposed versions of firefly algorithm are compared with first fit, first fit decreasing, random algorithm and ant colony algorithm. The comparison is done based on average energy consumption, average active VM, average active PM and average overall service-level agreement violations in the DC. The results show that DFFLSM performs better than all pre-existing container placement algorithms in terms of energy efficiency. It reduces average energy consumption of DC by 9.32% and 40.85% and average active PM by 18.30% and 21.89% in homogenous and heterogeneous environment, respectively. [ABSTRACT FROM AUTHOR]

Problems with task distribution in cloud data centers persist despite earlier research in cloud computing (CC). Particularly in the infrastructure-as-a-service (IaaS) cloud paradigm. In cloud data centers, effective task allocation is essential due to the restricted availability of resources and virtual machines (VMs). IaaS is one of the main CC models since it controls the backend, which includes VMs and data centers. Cloud service providers can ensure satisfactory service delivery performance in these models by preventing situations of host underutilization or overloading. This is because both results increase network execution time and lead to VM failure. To solve these problems, an improved load balancing approaches was proposed in this work. Therefore, this paper suggested an enhanced load balancing approaches to address these issues. The Artificial Bee Colony (ABC) method and the Bat algorithm are combined to create the balancing technique known as the Hybrid BAT and ABC (HBABC) algorithm, which dynamically distributes resources. The suggested HBABC method was assessed using CloudSim and standard workload format (SWF) data sets, which had file sizes of 200 KB and 400 KB. The evaluation was conducted on even workloads ranging from 200 to 20,000, and the performance of the HBABC method was compared with other state-of-the-art algorithms. The implementation of the suggested HBABC method resulted in a reduction of the Makespan (energy level) within the data center and showed improved accuracy in task allocation for VMs in a cloud data center. The ANOVA comparison test revealed a 1.98 percent enhancement in VM accuracy and task distribution, as well as a 0.98 percent decrease in the Makespan or energy level of the cloud data center. The outcomes are in line with various services broker rules that are employed during process of simulating the suggested algorithm in a cloud datacenter. The suggested method will be employed in subsequent studies as a prediction strategy for the resource management system in cloud datacenters. [ABSTRACT FROM AUTHOR]

Energy wastage and service level access are challenges of cloud networks, while efficient migration of virtual machines is a solution for them. Of course, uncontrolled migration increases overhead in cloud comprising: violation in service and high energy consumption. Performance requirements is very important in cloud, generally, it in the form of service level agreements are formalized. The main goal of migration is energy and performance trade-off. Detecting overloaded and under-loaded physical machine as sub-problem of migration, which effects the performance and energy. In this paper, with definition a system modelling, in first for detect overloaded and under-loaded physical machines a predict detection method is proposed. The forecast of the future utilization of the processor by using predict physical machines utilization method is provided. Predict physical machine's utilization module in predict detection method has been called. Unlike the previous physical machine detection methods, the current load of physical machines along with the future load of them is considered, and will lead to more exact results. Then for selection of virtual machines on overloaded physical machines a fuzzy selection method is proposed. Unlike the previous virtual machine-selection methods, all affective variables are considered and will lead to better results. In overall, the predict detection-fuzzy selection algorithm as intelligent approach has proposed and simulated with PlanetLab workload, CloudSim toolkit and Matlab. Simulation results demonstrate that significantly outperforms benchmark algorithms in terms of the energy consumption, migration count, service level agreement violation, energy-service level agreement violation, and energy-service level agreement violation-migration. [ABSTRACT FROM AUTHOR]

A cloud computing environment processes user workloads or tasks by exploiting its high performance computational, storage, of reducing and network resources. The virtual machines in the cloud environment are allocated to tasks with the aim of reducing overall execution time. The use of high performance resources incurs monetary costs, as well as high power consumption. The heuristic based approaches implemented for scheduling tasks are unable to cope with the complexity of optimizing multiple parameters. In this paper, we propose a multi-objective grey-wolf optimization based algorithm for scheduling tasks on cloud platforms. The proposed algorithm targets to minimize schedule length (overall execution time), energy consumption, and monetary cost required for executing tasks. For optimization, the algorithm incorporates steps that are performed iteratively for mimicking the behavior of grey wolves attacking their prey. It uses discrete values for positioning wolves for encircling and attacking the prey. The assignment of tasks to virtual machines is performed using the solution found after multi-objective optimization that incorporates weighted sorting for arranging solutions. Our experimentation performed using the CloudSim framework shows that the proposed algorithm outperforms other algorithms with performance improvement ranging from 3.98% to 16.07%, while considering the schedule length, monetary cost, and energy consumption. • For cloud computing environments, we present a novel task scheduling approach, termed as GWOTS. • Using the proposed approach, the schedule length, monetary cost, and energy consumption are optimized. • The GWOTS algorithm uses multi-objective optimization and incorporates weighted sorting for arranging solutions. • The proposed GWOTS algorithm outperforms other state-of-the-art algorithms by producing optimized schedules. [ABSTRACT FROM AUTHOR]

Fog computing and the Internet of Things (IoT) have revolutionized healthcare monitoring systems, enabling real-time health data collection and transmission while overcoming cloud computing limitations. However, efficiently selecting fog nodes for application modules with varying deadline requirements and ensuring adherence to quality of service (QoS) criteria pose significant challenges due to resource constraints and device limitations. In this paper, we present a novel two-layered hierarchical design for fog devices, leveraging cluster aggregation to optimize the selection of fog nodes for healthcare applications. We introduce three efficient algorithms to minimize system latency and reduce energy consumption in fog computing environments. Our proposed model is rigorously evaluated using the iFogSim toolkit and compared with cloud-based and latency-aware model [Mahmud R, Ramamohanarao K, Buyya R in ACM Transactions on Internet Technology.19, 2018, 10.1145/3186592]. In four distinct network topologies, our model exhibits an average latency reduction of at least 87% and energy consumption reduction of at least 76% when compared to the Cloud-based model. Similarly, when compared to the Latency-aware model proposed in [Mahmud R, Ramamohanarao K, Buyya R in ACM Transactions on Internet Technology. 19, 2018, 10.1145/3186592], our model showcases a minimum reduction of 43% in average latency and 27% in energy consumption. Our contribution lies in addressing the complexity of selecting fog nodes for application modules with diverse deadline requirements, while ensuring QoS. This work advances the field of real-time healthcare monitoring systems, promising substantial improvements in efficiency and effectiveness. [ABSTRACT FROM AUTHOR]