An energy-efficient task scheduling algorithm for heterogeneous cloud computing systems

2018 ◽  
Vol 22 (2) ◽  
pp. 509-527 ◽  
Author(s):  
Sanjaya K. Panda ◽  
Prasanta K. Jana
Keyword(s):  
Cloud Computing ◽  
Task Scheduling ◽  
Energy Efficient ◽  
Scheduling Algorithm ◽  
Computing Systems ◽  
Task Scheduling Algorithm ◽  
Heterogeneous Cloud

scholarly journals SLA-DQTS: SLA Constrained Adaptive Online Task Scheduling Based on DDQN in Cloud Computing

2021 ◽  
Vol 11 (20) ◽  
pp. 9360
Author(s):  
Kaibin Li ◽  
Zhiping Peng ◽  
Delong Cui ◽  
Qirui Li
Keyword(s):  
Cloud Computing ◽  
Task Scheduling ◽  
Performance Optimization ◽  
Heuristic Algorithms ◽  
Scheduling Algorithm ◽  
Computing Systems ◽  
Q Learning ◽  
Reward Function ◽  
Task Scheduling Algorithm ◽  
Comparison Algorithms

Task scheduling is key to performance optimization and resource management in cloud computing systems. Because of its complexity, it has been defined as an NP problem. We introduce an online scheme to solve the problem of task scheduling under a dynamic load in the cloud environment. After analyzing the process, we propose a server level agreement constraint adaptive online task scheduling algorithm based on double deep Q-learning (SLA-DQTS) to reduce the makespan, cost, and average overdue time under the constraints of virtual machine (VM) resources and deadlines. In the algorithm, we prevent the change of the model input dimension with the number of VMs by taking the Gaussian distribution of related parameters as a part of the state space. Through the design of the reward function, the model can be optimized for different goals and task loads. We evaluate the performance of the algorithm by comparing it with three heuristic algorithms (Min-Min, random, and round robin) under different loads. The results show that the algorithm in this paper can achieve similar or better results than the comparison algorithms at a lower cost.

A Novel Task Scheduling Algorithm in Heterogeneous Cloud Environment Using Equi-Depth Binning Method

2019 ◽  
pp. 115-127
Author(s):  
Roshni Pradhan ◽  
Amiya Kumar Dash
Keyword(s):  
Task Scheduling ◽  
Large Scale ◽  
Heterogeneous Computing ◽  
Scheduling Algorithm ◽  
Computing Systems ◽  
Task Scheduling Algorithm ◽  
Np Complete ◽  
Heterogeneous Cloud ◽  
Heterogeneous Computing Systems ◽  
Modern Tool

Cloud computing is modern tool for large-scale distributed computing and parallel processing. It has become a growing technology to deliver highly scalable service to the user. Task scheduling is one of the essential strategies to expeditiously utilize the potential of heterogeneous computing systems. In heterogeneous framework mapping, a task to a machine is a NP complete problem. This issue can be comprehended just utilizing heuristic approach. There are various heuristic approaches that were proposed to deal with scheduling of independent tasks. Different scheduling measures can be utilized for measuring the potency of scheduling algorithms. The most essential of them are makespan, flow-time, and overall resource utilization. Cloud generally is a single machine or combination of machines. Applications in the form of set of tasks are processed by the cloud.

A Novel Task Scheduling Algorithm in Heterogeneous Cloud Environment Using Equi-Depth Binning Method

2021 ◽  
pp. 1303-1316
Author(s):  
Roshni Pradhan ◽  
Amiya Kumar Dash
Keyword(s):  
Task Scheduling ◽  
Large Scale ◽  
Heterogeneous Computing ◽  
Scheduling Algorithm ◽  
Computing Systems ◽  
Task Scheduling Algorithm ◽  
Np Complete ◽  
Heterogeneous Cloud ◽  
Heterogeneous Computing Systems ◽  
Modern Tool

Cloud computing is modern tool for large-scale distributed computing and parallel processing. It has become a growing technology to deliver highly scalable service to the user. Task scheduling is one of the essential strategies to expeditiously utilize the potential of heterogeneous computing systems. In heterogeneous framework mapping, a task to a machine is a NP complete problem. This issue can be comprehended just utilizing heuristic approach. There are various heuristic approaches that were proposed to deal with scheduling of independent tasks. Different scheduling measures can be utilized for measuring the potency of scheduling algorithms. The most essential of them are makespan, flow-time, and overall resource utilization. Cloud generally is a single machine or combination of machines. Applications in the form of set of tasks are processed by the cloud.

A low-power task scheduling algorithm for heterogeneous cloud computing

2020 ◽  
Vol 76 (9) ◽  
pp. 7290-7314 ◽  
Author(s):  
Bin Liang ◽  
Xiaoshe Dong ◽  
Yufei Wang ◽  
Xingjun Zhang
Keyword(s):  
Cloud Computing ◽  
Low Power ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Task Scheduling Algorithm ◽  
Heterogeneous Cloud

scholarly journals Multijob Associated Task Scheduling for Cloud Computing Based on Task Duplication and Insertion

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Lei Shi ◽  
Jing Xu ◽  
Lunfei Wang ◽  
Jie Chen ◽  
Zhifeng Jin ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Computing Systems ◽  
Use Of Resources ◽  
Task Scheduling Algorithm ◽  
Task Duplication ◽  
Insertion Algorithm ◽  
Speed Up ◽  
Execution Order

With the emergence and development of various computer technologies, many jobs processed in cloud computing systems consist of multiple associated tasks which follow the constraint of execution order. The task of each job can be assigned to different nodes for execution, and the relevant data are transmitted between nodes to complete the job processing. The computing or communication capabilities of each node may be different due to processor heterogeneity, and hence, a task scheduling algorithm is of great significance for job processing performance. An efficient task scheduling algorithm can make full use of resources and improve the performance of job processing. The performance of existing research on associated task scheduling for multiple jobs needs to be improved. Therefore, this paper studies the problem of multijob associated task scheduling with the goal of minimizing the jobs’ makespan. This paper proposes a task Duplication and Insertion algorithm based on List Scheduling (DILS) which incorporates dynamic finish time prediction, task replication, and task insertion. The algorithm dynamically schedules tasks by predicting the completion time of tasks according to the scheduling of previously scheduled tasks, replicates tasks on different nodes, reduces transmission time, and inserts tasks into idle time slots to speed up task execution. Experimental results demonstrate that our algorithm can effectively reduce the jobs’ makespan.

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