A Predictive Priority-Based Dynamic Resource Provisioning Scheme With Load Balancing in Heterogeneous Cloud Computing
Mayank Sohani,S. C. Jain +1 more
TL;DR: In this paper, a Predictive Priority-based Modified Heterogeneous Earliest Finish Time (PMHEFT) algorithm is proposed to minimize the makespan of a given workflow application by improving the load balancing across all the virtual machines.
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Abstract: In cloud computing, resource provisioning is a key challenging task due to dynamic resource provisioning for the applications. As per the workload requirements of the application’s resources should be dynamically allocated for the application. Disparities in resource provisioning produce energy, cost wastages, and additionally, it affects Quality of Service (QoS) and increases Service Level Agreement (SLA) violations. So, applications allocated resources quantity should match with the applications required resources quantity. Load balancing in cloud computing can be addressed through optimal scheduling techniques, whereas this solution belongs to the NP-Complete optimization problem category. However, the cloud providers always face resource management issues for variable cloud workloads in the heterogeneous system environment. This issue has been solved by the proposed Predictive Priority-based Modified Heterogeneous Earliest Finish Time (PMHEFT) algorithm, which can estimate the application’s upcoming resource demands. This research contributes towards developing the prediction-based model for efficient and dynamic resource provisioning in a heterogamous system environment to fulfill the end user’s requirements. Existing algorithms fail to meet the user’s Quality of Service (QoS) requirements such as makespan minimization and budget constraints satisfaction, or to incorporate cloud computing principles, i.e., elasticity and heterogeneity of computing resources. In this paper, we proposed a PMHEFT algorithm to minimize the makespan of a given workflow application by improving the load balancing across all the virtual machines. Experimental results show that our proposed algorithm’s makespan, efficiency, and power consumption are better than other algorithms.
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Citations
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CILP: Co-simulation based Imitation Learner for Dynamic Resource Provisioning in Cloud Computing Environments
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Scheduling Cloudlets in a Cloud Computing Environment: A Priority-based Cloudlet Scheduling Algorithm (PBCSA)
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- 16 Dec 2022
TL;DR: In this paper , a priority based cloudlet scheduling algorithm (PBCSA) is proposed to schedule the cloudlet according to the user priority in the cloud environment, which is based on the combination of two or more QoS and performance metrics like makespan, throughput, cost, power consumption, virtual machine or resource utilization and load balancing etc.
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References
Performance-effective and low-complexity task scheduling for heterogeneous computing
TL;DR: Two novel scheduling algorithms for a bounded number of heterogeneous processors with an objective to simultaneously meet high performance and fast scheduling time are presented, called the Heterogeneous Earliest-Finish-Time (HEFT) algorithm and the Critical-Path-on-a-Processor (CPOP) algorithm.
A Comparison of Eleven Static Heuristics for Mapping a Class of Independent Tasks onto Heterogeneous Distributed Computing Systems
Tracy D. Braun,Howard Jay Siegel,N.B. Beck,Ladislau Bölöni,Muthucumaru Maheswaran,Albert Reuther,James Patrick Robertson,Mitchell D. Theys,Bin Yao,Debra Hensgen,Richard F. Freund +10 more
TL;DR: It is shown that for the cases studied here, the relatively simple Min?min heuristic performs well in comparison to the other techniques, and one even basis for comparison and insights into circumstances where one technique will out-perform another.
1.9K
List Scheduling Algorithm for Heterogeneous Systems by an Optimistic Cost Table
Hamid Arabnejad,Jorge G. Barbosa +1 more
TL;DR: The analysis and experiments show that the PEFT algorithm outperforms the state-of-the-art list-based algorithms for heterogeneous systems in terms of schedule length ratio, efficiency, and frequency of best results.
WorkflowSim: A toolkit for simulating scientific workflows in distributed environments
Weiwei Chen,Ewa Deelman +1 more
- 08 Oct 2012
TL;DR: WorkflowSim as mentioned in this paper extends the existing CloudSim simulator by providing a higher layer of workflow management, which takes into consideration heterogeneous system overheads and failures, and it is shown that to ignore system overhead and failures in simulating scientific workflows could cause significant inaccuracies in the predicted workflow runtime.
521
Load balancing mechanisms and techniques in the cloud environments
TL;DR: A systematic literature review of the existing load balancing techniques proposed so far and the advantages and disadvantages associated with several load balancing algorithms have been discussed and the important challenges of these algorithms are addressed so that more efficientload balancing techniques can be developed in future.
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