Energy-efficient Task Scheduling Research Articles

EETS: An energy-efficient task scheduler in cloud computing based on improved DQN algorithm

The huge energy consumption of data centers in cloud computing leads to increased operating costs and high carbon emissions to the environment. Deep Reinforcement Learning (DRL) technology combines of deep learning and reinforcement learning, which has an obvious advantage in solving complex task scheduling problems. Deep Q Network(DQN)-based task scheduling has been employed for objective optimization. However, training the DQN algorithm may result in value overestimation, which can negatively impact the learning effectiveness. The replay buffer technique, while increasing sample utilization, does not distinguish between sample importance, resulting in limited utilization of valuable samples. This study proposes an enhanced task scheduling algorithm based on the DQN framework, which utilizes a more optimized Dueling-network architecture as well as Double DQN strategy to alleviate the overestimation bias and address the shortcomings of DQN. It also incorporates a prioritized experience replay technique to achieve importance sampling of experience data, which overcomes the problem of low utilization due to uniform sampling from replay memory. Based on these improved techniques, we developed an energy-efficient task scheduling algorithm called EETS (Energy-Efficient Task Scheduling). This algorithm automatically learns the optimal scheduling policy from historical data while interacting with the environment. Experimental results demonstrate that EETS exhibits faster convergence rates and higher rewards compared to both DQN and DDQN. In scheduling performance, EETS outperforms other baseline algorithms in key metrics, including energy consumption, average task response time, and average machine working time. Particularly, it has a significant advantage when handling large batches of tasks.

An Implementation on Energy Efficient Task Scheduling in Cloud Environment

Abstract: Cloud computing has revolutionized the way computing resources are utilized by providing scalable, on-demand access to a shared pool of resources over the internet. It offers significant advantages such as cost savings, flexibility, and accessibility, making it an essential technology for businesses and individuals alike. However, the increasing energy consumption associated with cloud data centres has become a critical concern, necessitating the development of energy-efficient task scheduling algorithms. This paper explores various algorithms designed to optimize task scheduling in cloud environments to reduce energy consumption. Among these, heuristic algorithms like Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are popular for their ability to find near-optimal solutions in large search spaces. Additionally, machine learning-based approaches, such as Reinforcement Learning (RL), have shown promise in dynamically adapting to workload variations and improving energy efficiency. Other notable algorithms include Ant Colony Optimization (ACO) and Dynamic Voltage and Frequency Scaling (DVFS), each offering unique mechanisms to balance performance and energy usage. The focus of this paper is on the implementation and comparative analysis of these task scheduling algorithms in a cloud environment. We present a comprehensive evaluation of their performance, highlighting their strengths and limitations in achieving energy efficiency. The results demonstrate that while no single algorithm is universally optimal, tailored combinations of these approaches can significantly enhance energy savings in cloud data centres.

Deadline-aware and energy efficient IoT task scheduling using fuzzy logic in fog computing

Deadline-aware and energy efficient IoT task scheduling using fuzzy logic in fog computing

METSM: Multiobjective energy-efficient task scheduling model for an edge heterogeneous multiprocessor system

Along with the growth of computing requirements, edge devices, such as satellites and unmanned aerial vehicles, are equipped with heterogeneous multiprocessor systems to cope with complicated missions, including attitude control, signal processing, and objective detection. Concerning the extremely limited power on these devices, reducing the energy and time overhead of task execution evolves into a crucial challenge. Therefore, a multiobjective energy-efficient task scheduling technique (METSM) is proposed. First, a mathematical model is established for the energy-efficient task scheduling problem on edge heterogeneous multiprocessor systems. In this model, both makespan and total energy consumption are the optimization objectives. The decision variables include the task execution sequence, processor assignment, and dynamic voltage and frequency scaling level for each processor. Second, a problem-specific algorithm, namely, iterated greedy-based multiobjective optimizer (IMO), is proposed. Specifically, destruction–reconstruction and local search are redesigned for optimizing task scheduling and resource allocation. Considering local optima avoidance, a probabilistic mutation operation is developed. In addition, multiobjective-oriented strategies of optimal solution selection and acceptance criteria are adopted to accelerate the convergence. Finally, our proposed IMO is compared with several of the latest algorithms through multiple performance metrics. The experimental results show that IMO can obtain optimal Pareto fronts among several multiobjective methods. Savings of approximately 10% and 12% in time and power consumption, respectively, can be achieved by IMO. Moreover, in comparison with classic list-based heuristics for solving test cases, while IMO maintains a similar makespan, energy is reduced by nearly 90%.

Ford Fulkerson and Newey West Regression Based Dynamic Load Balancing in Cloud Computing for Data Communication

In Cloud Computing (CC) environment, load balancing refers to the process of optimizing resources of virtual machines. Load balancing in the CC environment is one of the analytical approaches utilized to ensure indistinguishable workload distribution and effective utilization of resources. This is because only by ensuring effective balance of dynamic workload results in higher user satisfaction and optimal allocation of resource, therefore improve cloud application performance. Moreover, a paramount objective of load balancing is task scheduling because surges in the number of clients utilizing cloud lead to inappropriate job scheduling. Hence, issues encircling task scheduling has to be addressed. In this work a method called, Ford Fulkerson and Newey West Regression-based Dynamic Load Balancing (FF-NWRDLB) in CC environment is proposed. The FF-NWRDLB method is split into two sections, namely, task scheduling and dynamic load balancing. First, Ford Fulkerson-based Task Scheduling is applied to the cloud user requested tasks obtained from Personal Cloud Dataset. Here, employing Ford Fulkerson function based on the flow of tasks, energy-efficient task scheduling is ensured. The execution of asymmetrical scientific applications can be smoothly influenced by an unbalanced workload distribution between computing resources. In this context load balancing signifies as one of the most significant solution to enhance utilization of resources. However, selecting the best accomplishing load balancing technique is not an insignificant piece of work. For example, selecting a load balancing model does not work in circumstances with dynamic behavior. In this context, a machine learning technique called, Newey West Regression-based dynamic load balancer is designed to balance the load in a dynamic manner at run time, therefore ensuring accurate data communication. The FF-NWRDLB method has been compared to recent algorithms that use the markov optimization and the prediction scheme to achieve load balancing. Our experimental results show that our proposed FF-NWRDLB method outperforms other state of the art schemes in terms of energy consumption, throughput, delay, bandwidth and task scheduling efficiency in CC environment.

Reinforcement learning-based multi-objective energy-efficient task scheduling in fog-cloud industrial IoT-based systems

Reinforcement learning-based multi-objective energy-efficient task scheduling in fog-cloud industrial IoT-based systems

Energy-efficient scheduling of imprecise mixed-criticality real-time tasks based on genetic algorithm

Previous work focuses on the problem of energy for Imprecise Mixed-Criticality Systems on multiprocessor platforms, which provides degraded services for low criticality tasks in the high criticality mode. However, the proposed solution based on the partitioning heuristic Criticality-Unaware Worst Fit Decreasing method (CU-WFD) may consume more energy. In this paper, we propose a novel partitioning algorithm named EEAA based on the genetic algorithm and explore various parameter combinations, aiming to find a task-to-processor assignment method that can further reduce energy consumption with the best parameter combinations. In addition, we conduct experiments to evaluate EEAA and the experimental result shows that EEAA achieves energy savings of 12.56% compared to CU-WFD.

A Bio-inspired Energy Efficient Dynamic Task Scheduling (BEDTS) scheme and classification for virtualization CDC

Cloud Data Centers (CDCs) have evolved into an essential computing infrastructure for businesses. These applications generate a variety of data traffic with varying needs that must be examined and processed on CDCs. Every CDC is made up of multiple servers, virtual infrastructures, and physical connections that manage the internet's informative traffic. CDCs make use of a number of critical technologies, such as virtualization and Service Level Agreements (SLA). Virtualization makes it easier to share cloud computing resources (for example, by separating a powerful Physical Machine (PM) into a series of Virtual Machines (VM)) whose power is comparatively less. Even though virtualization increases the use of PMs by establishing a set of VMs for offering customised services to satisfy the needs of end-users, it also introduces another difficulty to cloud computing: map the VMs to the appropriate PMs. This is referred to as the VM deployment problem, and it is a Nondeterministic Polynomial time problem (NP-problem). CDC task scheduling is appropriate to augment the energy efficiency and resource usage in cloud computing. In the case of real-time tasks in virtualized CDC, the Bio-Inspired Energy Efficient Dynamic Task Scheduling (BEDTS) method is proposed. The Adaptive Elephant Herding Optimization (AEHO) technique is introduced in the BEDTS algorithm for optimal selection of VMs with resource restrictions and jobs. Initially, heterogeneous jobs and VMs are identified using a previous scheduling record. The Bayes classifier and Historical Scheduling Record (HSR), which allow for the identification of both the task type and VM type, serve as the foundation for task categorization. Then, related tasks are combined and then scheduled to make the best use of the host's operational status. When compared to previous approaches, experimental results reveal that BEDTS considerably enhances the scheduling performance on the whole, attains improved CDC resource utilisation, boosts task guarantee ratio, lowers average response time, and decreases the energy usage.

Energy-efficient task scheduling for mobile edge computing with virtual machine I/O interference

Mobile edge computing (MEC) is expected to support the computation-intensive and delay-sensitive applications of mobile internet users. In this paper, we investigate the resource allocation of MEC with the effect of I/O interference among parallel virtual machines (VMs) while satisfying the quality of service (QoS) of tasks. Different from existing works, we propose a flexible task scheduling approach that combining parallel and sequential computing to minimize the computing energy consumption of MEC server. We formulate the task scheduling problem as a mixed-integer nonlinear programming (MINLP) and decompose it as a CPU resource allocation subproblem, a computing time slot subproblem, and a VM selection subproblem. We show the first subproblem is a convex problem and propose a CPU frequency allocation (CFA) algorithm based on the Karush–Kuhn–Tucker (KKT) conditions to obtain the optimal CPU frequency resource allocation. For the time slot allocation and VM selection subproblems, we propose the three step allocation (TSA) and urgency based adjusting (UBA) algorithms to obtain the near-optimal solutions, respectively. Simulation results show that compared with several time slot allocations and VM selections, the proposed TSA and UBA algorithms can save up to 21.7% and 95.8% of energy consumption, respectively.

Performance Evaluation of Hybrid Meta-Heuristics-Based Task Scheduling Algorithm for Energy Efficiency in Fog Computing

Task scheduling in fog computing is one of the areas where researchers are having challenges as the demand grows for the use of internet of things (IoT) to access cloud computing resources. Many resource scheduling and optimization algorithms were used by many researchers in fog computing; some used single techniques while others used combined schemes to achieve dynamic scheduling in fog computing, many optimization techniques were assessed based on deterministic and meta-heuristic to find out solution to task scheduling problem in fog computing but could not achieve excellent results as required. This article proposes hybrid meta-heuristic optimization algorithm (HMOA) for energy efficient task scheduling in fog computing, the study combined modified particle swarm optimization (MPSO) meta-heuristic and deterministic spanning tree (SPT) to achieve task scheduling with the intention of eliminating the drawbacks of the two algorithms when used separately, the MPSO was used to schedule user task requests among fog devices, while hybrid MPSO-SPT was used to perform resource allocation and resource management in the fog computing environment. The study implemented the proposed algorithm using iFogSim; the performance of the algorithm was evaluated, assessed, and compared with other state-of-the-art task scheduling and resource management algorithms, the proposed method performs better in terms of energy consumption, resource utilization and response time, and the study proposed future research on evaluating the execution time using the hybrid algorithm.

EETSQ: Energy Efficient Task Scheduling based on QoS Parameters in Cloud Computing Environment

Now a day, energy consumption is the big challenge in heterogeneous cloud computing environment that needs to be considered. Cloud service provider also needs to satisfy customer’s Quality of Service (QoS) for better utilization. An energy efficient task scheduling based on QoS parameter has been proposed to address above said challenge. Firsty, all the incoming tasks are categorized into four classes based on some special attributes and prioritize according to importance of the classes. Secondly, Physical Machines (PMs) type confirmation list is selected based on the number of resource blocks and then select one PM that has maximum QoS value. All the Virtual Machines (VMs) on selected PM are prioritized according to their weight. Experimental evaluation done on CloudSim shows the effectiveness and efficiency of proposed approach.

Energy-Efficient Joint Task Assignment and Migration in Data Centers: A Deep Reinforcement Learning Approach

Energy-efficient task scheduling in data centers is a critical issue and has drawn wide attention. However, the task execution times are mixed and hard to estimate in a real-world data center. It has been conspicuously neglected by existing work that scheduling decisions made at tasks’ arrival times are likely to cause energy waste or idle resources over time. To fill in such gaps, in this paper, we jointly consider assignment and migration for mixed duration tasks and devise a novel energy-efficient task scheduling algorithm. Task assignment can improve resource utilization, and migration is required when long-running tasks run in low-load servers. Specifically: 1) We formulate mixed duration task scheduling as a large-scale Markov Decision Process (MDP) problem; 2) To solve such a large-scale MDP problem, we design an efficient Deep Reinforcement Learning (DRL) algorithm to make assignment and migration decisions. To make the DRL algorithm more practical in real scenarios, multiple optimizations are proposed to achieve online training; 3) Experiments with real-world data have shown that our algorithm outperforms the existing baselines 14% on average in terms of energy consumption while keeping the same level of Quality of Service (QoS).

Energy-Efficient Task Scheduling in Fog Computing Based on Particle Swarm Optimization

Energy-Efficient Task Scheduling in Fog Computing Based on Particle Swarm Optimization

Online energy-efficient scheduling of DAG tasks on heterogeneous embedded platforms

Heterogeneous distributed computing systems are becoming more and more common to process practical applications in many embedded systems, where energy efficiency is very important, especially for battery-powered systems. This paper studies the problem of how to adaptively schedule dynamic applications on the fly on a heterogeneous embedded system such that the system’s energy consumption can be minimized, while meeting applications’ response time and reliability requirements simultaneously. We first study the problem of scheduling a set of static applications. This problem is formulated as an integer nonlinear programming problem by deriving the response time equation of directed acyclic graph (DAG) applications. A reliability-aware scheduling strategy is designed to solve this problem. On top of this strategy, we develop an algorithm that tries to use as few processors as possible to finish the computation because, in this way, the system’s static energy can be saved. We then extend this solution to handle the problem of scheduling dynamic applications. By developing a task migration algorithm and a schedule adjustment algorithm, the system can adaptively handle suspended and newly released applications on the fly. Extensive simulation experiments are conducted, and the results demonstrate the high efficiency of our proposed approaches against some classic heuristics and a state-of-the-art approach.

EEOA: Cost and Energy Efficient Task Scheduling in a Cloud-Fog Framework.

Cloud-fog computing is a wide range of service environments created to provide quick, flexible services to customers, and the phenomenal growth of the Internet of Things (IoT) has produced an immense amount of data on a daily basis. To complete tasks and meet service-level agreement (SLA) commitments, the provider assigns appropriate resources and employs scheduling techniques to efficiently manage the execution of received IoT tasks in fog or cloud systems. The effectiveness of cloud services is directly impacted by some other important criteria, such as energy usage and cost, which are not taken into account by many of the existing methodologies. To resolve the aforementioned problems, an effective scheduling algorithm is required to schedule the heterogeneous workload and enhance the quality of service (QoS). Therefore, a nature-inspired multi-objective task scheduling algorithm called the electric earthworm optimization algorithm (EEOA) is proposed in this paper for IoT requests in a cloud-fog framework. This method was created using the combination of the earthworm optimization algorithm (EOA) and the electric fish optimization algorithm (EFO) to improve EFO's potential to be exploited while looking for the best solution to the problem at hand. Concerning execution time, cost, makespan, and energy consumption, the suggested scheduling technique's performance was assessed using significant instances of real-world workloads such as CEA-CURIE and HPC2N. Based on simulation results, our proposed approach improves efficiency by 89%, energy consumption by 94%, and total cost by 87% over existing algorithms for the scenarios considered using different benchmarks. Detailed simulations demonstrate that the suggested approach provides a superior scheduling scheme with better results than the existing scheduling techniques.

ETS-TEE: An Energy-Efficient Task Scheduling Strategy in a Mobile Trusted Computing Environment

A trusted execution environment (TEE) is a system-on-chip and CPU system with a wide security solution available on today's Arm application (APP) processors, which dominate the smartphone market. Generally, mobile APPs create a trusted application (TA) in the TEE to process sensitive information, such as payment or message encryption, which is transparent to the APPs running in the rich execution environments (REEs). In detail, the REE and TEE interact and eventually send back the results to the APP in the REE through the interface provided by the TA. Such an operation definitely increases the overhead of mobile APPs. In this paper, we first present a comprehensive analysis of the performance of open-source TEE encrypted text. We then propose a high energy-efficient task scheduling strategy (ETS-TEE). By leveraging the deep learning algorithm, our policy considers the complexity of TA tasks, which are dynamically scheduled between modeling on the local device and offloading to an edge server. We evaluate our approach on Raspberry Pi 3B as the local mobile device and Jetson TX2 as the edge server. The results show that compared with the default scheduling strategy on the local device, our approach achieves an average of 38.0% energy reduction and 1.6 × speedup. This greatly reduces the performance loss caused by mobile devices in order to protect the safe execution of applications, so that the trusted execution environment has both security and high performance.

Hybrid Meta-Heuristics Based Task Scheduling Algorithm for Energy Efficiency in Fog Computing

Task scheduling in fog computing is one of the areas where researchers are having challenges as the demand grows for the use of Internet of Things (IoT) to access cloud computing resources. Many resource scheduling and optimization algorithms were used by many researchers in fog computing; some used single techniques while others used combined schemes to achieve dynamic scheduling in fog computing, many optimization techniques are reassessed based on deterministic and meta-heuristics to find out solution to scheduling problem in fog computing. This paper proposes Hybrid Meta-Heuristics Optimization Algorithm (HMOA) for energy efficient task scheduling in fog computing, the study combines Particle Swarm Optimization (PSO) Meta-heuristics and deterministic Spanning Tree (SPT) to achieve task scheduling with the intention of eliminating the drawbacks of the two algorithms when used separately, the PSO is used to schedule user task requests among fog devices, while hybrid MPSO-SPT will be used to perform resource allocation and resource management in the fog computing environment. The study proposed to implement the algorithms using iFogSim in the future work such that performance of the algorithms will be evaluated, assessed and compared with other state of art scheduling and resource management algorithms

Providing Aerial MEC Service in Areas Without Infrastructure: A Tethered-UAV-Based Energy-Efficient Task Scheduling Framework

The evolution of information and communication technology, besides the proliferation of mobile devices, pushes the horizon of the Internet of Things. The main challenges for mobile devices are limited battery power and insufficient computational resources. Multiaccess edge computing (MEC) is an emerging paradigm that can provide task offloading services in the proximity of mobile devices to alleviate their load. In addition, mobile devices cannot work in emergency scenarios (e.g., post-earthquake, post-flood, and post-hurricane) or areas without infrastructure. Tethered unmanned aerial vehicles (UAVs) have received widespread attention as an alternative base station because of their line-of-sight solid links status, flexible deployment, and sufficient energy supply. With this in mind, we propose a tethered-unmanned-aerial-vehicle-based aerial MEC network to provide communication and task offloading services in areas without infrastructure. In addition, an energy-efficient task scheduling framework is proposed to achieve more energy-efficient task scheduling. First, we propose a geometry-based placement algorithm to generate optimal placement positions for the UAV placement problem. Then, for the nonconvex task scheduling and the resource allocation problem, we propose a low complexity divide and conquer scheme, which decomposes the original problem into three subproblems and solves them separately. Extensive simulations demonstrate the better energy efficiency of the proposed framework.

HunterPlus: AI based energy-efficient task scheduling for cloud–fog computing environments

Cloud computing is a mainstay of modern technology, offering cost-effective and scalable solutions to a variety of different problems. The massive shift of organization resource needs from local systems to cloud-based systems has greatly increased the costs incurred by cloud providers in expanding, maintaining, and supplying server, storage, network, and processing hardware. Due to the large scale at which cloud providers operate, even small performance degradation issues can cause energy or resource usage costs to rise dramatically. One way in which cloud providers may improve cost reduction is by reducing energy consumption. The use of intelligent task-scheduling algorithms to allocate user-deployed jobs to servers can reduce the amount of energy consumed. Conventional task scheduling algorithms involve both heuristic and metaheuristic methods. Recently, the application of Artificial Intelligence (AI) to optimize task scheduling has seen significant progress, including the Gated Graph Convolution Network (GGCN). This paper proposes a new approach called HunterPlus which examine the effect of extending the GGCN’s Gated Recurrent Unit to a Bidirectional Gated Recurrent Unit. The paper also studies the utilization of Convolutional Neural Networks (CNNs) in optimizing cloud–fog task scheduling. Experimental results show that the CNN scheduler outperforms the GGCN-based models in both energy consumption per task and job completion rate metrics by at least 17 and 10.4 percent, respectively.

Energy-Efficient Task Scheduling and Resource Allocation for Improving the Performance of a Cloud–Fog Environment

Inadequate resources and facilities with zero latency affect the efficiencies of task scheduling (TS) and resource allocation (RA) in the fog paradigm. Only the incoming tasks can be completed within the deadline if the resource availability in the cloud and fog is symmetrically matched with them. A container-based TS algorithm (CBTSA) determines the symmetry relationship of the task/workload with the fog node (FN) or the cloud to decide the scheduling workloads (whether in the fog or a cloud). Furthermore, by allocating and de-allocating resources, the RA algorithm reduces workload delays while increasing resource utilization. However, the unbounded cloud resources and the computational difficulty of finding resource usage have not been considered in CBTSA. Hence, this article proposes an enhanced CBTSA with intelligent RA (ECBTSA-IRA), which symmetrically balances energy efficiency, cost, and the performance-effectiveness of TS and RA. Initially, this algorithm determines whether the workloads are accepted for scheduling. An energy-cost–makespan-aware scheduling algorithm is proposed that uses a directed acyclic graph (DAG) to represent the dependency of tasks in the workload as a graph. Workloads are prioritized and selected for the node to process the prioritized workload. The selected node for processing the workload might be a FN or cloud and is decided by an optimum efficiency factor that trades off the schedule length, cost, and energy. Moreover, a Markov decision process (MDP) was adopted to allocate the best resources using the reinforcement learning scheme. Finally, the investigational findings reveal the efficacy of the presented algorithms compared to the existing CBTSA in terms of various performance metrics.