An Evolutionary Computing-Based Efficient Hybrid Task Scheduling Approach for Heterogeneous Computing Environment

Sulaiman, Muhammad; Halim, Zahid; Lebbah, Mustapha; Waqas, Muhammad; Tu, Shanshan

doi:10.1007/s10723-021-09552-4

Cited by 43 publications

(13 citation statements)

References 41 publications

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“…As a result, the swarm may be directed to a practical search region [10]. y j = max min y j , y j , y j (17) G(y) = g(y) + EE ee=1 z 1 ee . max gg ee (y) , 0…”

Section: The Operator Of Internal Competitionmentioning

confidence: 99%

Optimization Task Scheduling Using Cooperation Search Algorithm for Heterogeneous Cloud Computing Systems

Hamed¹,

Elnahary²,

Alsubaei³

et al. 2023

Computers, Materials &Amp; Continua

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Cloud computing has taken over the high-performance distributed computing area, and it currently provides on-demand services and resource polling over the web. As a result of constantly changing user service demand, the task scheduling problem has emerged as a critical analytical topic in cloud computing. The primary goal of scheduling tasks is to distribute tasks to available processors to construct the shortest possible schedule without breaching precedence restrictions. Assignments and schedules of tasks substantially influence system operation in a heterogeneous multiprocessor system. The diverse processes inside the heuristic-based task scheduling method will result in varying makespan in the heterogeneous computing system. As a result, an intelligent scheduling algorithm should efficiently determine the priority of every subtask based on the resources necessary to lower the makespan. This research introduced a novel efficient scheduling task method in cloud computing systems based on the cooperation search algorithm to tackle an essential task and schedule a heterogeneous cloud computing problem. The basic idea of this method is to use the advantages of meta-heuristic algorithms to get the optimal solution. We assess our algorithm's performance by running it through three scenarios with varying numbers of tasks. The findings demonstrate that the suggested technique beats existing methods New Genetic Algorithm (NGA), Genetic Algorithm (GA), Whale Optimization Algorithm (WOA), Gravitational Search Algorithm (GSA), and Hybrid Heuristic and Genetic (HHG) by 7.9%, 2.1%, 8.8%, 7.7%, 3.4% respectively according to makespan.

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“…As a result, the swarm may be directed to a practical search region [10]. y j = max min y j , y j , y j (17) G(y) = g(y) + EE ee=1 z 1 ee . max gg ee (y) , 0…”

Section: The Operator Of Internal Competitionmentioning

confidence: 99%

Optimization Task Scheduling Using Cooperation Search Algorithm for Heterogeneous Cloud Computing Systems

Hamed¹,

Elnahary²,

Alsubaei³

et al. 2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

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“…These challenges may imbalance the VMs load and can degrade the overall cloud performance [21,48]. Load balanced [18,21] cloud task scheduling [49,50] plays a key role to enable efficient use of the Cloud resources. Cloud task scheduling approaches (as shown in Figure 2) are categorized into three types.…”

Section: Introductionmentioning

confidence: 99%

RADL: a resource and deadline-aware dynamic load-balancer for cloud tasks

et al. 2022

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Cloud service providers acquire the computing resources and allocate them to their clients. To effectively utilize the resources and achieve higher user satisfaction, efficient task scheduling algorithms play a very pivotal role.A number of task scheduling technique have been proposed in the literature. However, majority of these scheduling algorithms fail to achieve efficient resource utilization that causes them to miss tasks deadlines. This is because these algorithms are not resource and deadline-aware. In this research, a Resource and deadline Aware Dynamic Load-balancer (RADL) for Cloud tasks has been presented. The proposed scheduling scheme evenly distribute the incoming workload of compute-intensive and independent tasks at run-time. In addition, RADL approach has the capability to accommodate the newly arrived tasks (with shorter deadlines) efficiently and reduce task rejection. The proposed scheduler monitors/updates the task and VM status at run-time. Experimental results shows that the proposed technique has attained up to 67.74%, 303.57%, 259.2%, 146.13%, 405.06%, and 259.14% improvement for average resource utilization, meeting tasks deadlines, lower makespan, task response time, penalty cost, and task execution cost respec-

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“…Third, another goal of task scheduling is to maintain the load balance among machines. However, task scheduling problem with resource constraints in HCE has been proven to be NP‐complete 2–4 . Hence the task scheduling problem in cross‐domain HCE is still faced with great challenges.…”

Section: Introductionmentioning

confidence: 99%

DDDQN‐TS: A task scheduling and load balancing method based on optimized deep reinforcement learning in heterogeneous computing environment

Sun

Yang

Lei

2022

Int J of Intelligent Sys

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Task scheduling and load balancing problem of heterogeneous computing environment (HCE) is getting more and more attention these days and has become a research hotspot in this field. The task scheduling and load balancing problem of heterogeneous environment, which refers to assigning a set of tasks to a specific set of machines with different hardware and different computing performance with the goal of minimizing task processing time and keeping load balance among machines, has been proved to be an NP-complete problem. The development of artificial intelligence provides new ideas to solve this problem. In this paper, we propose a novel task scheduling and load balancing method based on optimized deep reinforcement learning in HCE. First, we formulate task scheduling problem as a Markov decision process and then adopt a dueling double deep Q-learning network to search the optimal task allocation solution. Then we use two well-known large-scale cluster data sets Google Cloud Jobs data set and Alibaba Cluster Trace data set to validate our approach. The experimental results show that compared with other existing solutions, our proposed method can achieve much shorter task response time and better load balancing effect.

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An Evolutionary Computing-Based Efficient Hybrid Task Scheduling Approach for Heterogeneous Computing Environment

Cited by 43 publications

References 41 publications

Optimization Task Scheduling Using Cooperation Search Algorithm for Heterogeneous Cloud Computing Systems

Optimization Task Scheduling Using Cooperation Search Algorithm for Heterogeneous Cloud Computing Systems

RADL: a resource and deadline-aware dynamic load-balancer for cloud tasks

DDDQN‐TS: A task scheduling and load balancing method based on optimized deep reinforcement learning in heterogeneous computing environment

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