A Review on Meta-heuristic Independent Task Scheduling Algorithms in Cloud Computing

Gade, Anup; Bhat, M. Nirupama; Thakare, Nita

doi:10.1007/978-3-030-41862-5_118

Cited by 2 publications

(2 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Meta heuristic algorithms are independent problem, and they used to handle different type of problems. [13] In cloud computing, heuristic algorithms are designed to resolve the problematic issues faster than meta-heuristic algorithms, when their performance is too slow. Also, heuristic algorithms are used to find an optimum solution, when meta-heuristic algorithms failed to discover the precise or optimal solution [14].…”

Section: Related Workmentioning

confidence: 99%

A Modified Workflow Scheduling Algorithm for Cloud Computing Environment

2022

IJIES

View full text Add to dashboard Cite

Cloud computing has gained many attentions. Workflow scheduling one of the most important issues in cloud computing. It involves mapping tasks onto cloud resources -Virtual machines (VMs), to improve scheduling performance. Because the existing heterogeneous earliest finish time (HEFT) algorithm is considered one of the best algorithm, so the work in this paper propose a new algorithm based on HEFT algorithm; called modified heterogeneous earliest finish time (M-HEFT); to reduce the tradeoff among make span, resource utilization, and load balance. The proposed M-HEFT consists of two phases; task prioritization and task-VM mapping. In Task prioritization phase, a priority will be provided to each task in directed acyclic graph (DAG) as in the original HEFT algorithm. According to task-VM phase, tasks allocate to resources according to length of tasks and the load of available VMs with considering load balance. To evaluate the performance of the proposed algorithm, a comparative study has been done among the proposed algorithm and three existed algorithms. The experimental results show that the proposed algorithm outperforms the other algorithms by minimizing make span by 29%, improve resource utilization by 53% and load balance by 18% in average.

show abstract

Section: Related Workmentioning

confidence: 99%

A Modified Workflow Scheduling Algorithm for Cloud Computing Environment

2022

IJIES

View full text Add to dashboard Cite

show abstract

“…The proposed algorithm has been implemented using WorkflowSim1.0 toolkit integrated into Net Beans IDE 8.0.2 with the configurations shown in Table 2. WorkflowSim is an extension of the CloudSim framework [39]. The experiments have done using two type of workflows; Ligo, and Epigenomics.…”

Section: Experimental Environmentmentioning

confidence: 99%

An Enhanced Workflow Scheduling Algorithm for Cloud Computing Environment

2022

IJIES

View full text Add to dashboard Cite

Cloud computing has gained many attentions worldwide. Workflow systems become a significant method for develop scientific applications. Therefore, workflow scheduling is considered one of the most important issues in cloud computing. It concerns about mapping tasks on cloud resources (i.e., Virtual machines (VMs)), to improve scheduling performance. Because the existing heterogeneous earliest finish time (HEFT) algorithm is considered one of the best and simplest algorithms, many algorithms have been proposed to improve the performance of the HEFT algorithm. According to our previous work, a modification has been done to HEFT algorithm to enhance the performance, called modified heterogeneous earliest finish time (M-HEFT). The goal of M-HEFT algorithm is to reduce make span, maximize resource utilization and increase load balance. According to the work in this paper, an enhancement has been added to our previous M-EFT algorithm to reduce the tradeoff among make span, resource utilization, and load balance, called enhanced modified heterogeneous earliest finish time (EM-HEFT). The enhanced EM-HEFT algorithm consists of two phases; task prioritization and task-VM mapping. In task prioritization phase, a priority will be provided to each task in directed acyclic graph (DAG) by introducing new factors in priority value to be more aware about task requirements. According to task-VM phase, tasks are allocated to resources as in our previous M-HEFT algorithm. To evaluate the performance of the proposed EM-HEFT algorithm, a comparative study has been done among the proposed algorithm and four existed algorithms (HEFT, Efficient scheduling algorithm use critical path and static level attribute, Optimized Min-Min (OMin-Min) and our previous M-HEFT). The experimental results show that the proposed algorithm outperforms other algorithms by minimizing make span by 25%, improving resource utilization by 43% and load balance by 14% in average.

show abstract

A Review on Meta-heuristic Independent Task Scheduling Algorithms in Cloud Computing

Cited by 2 publications

References 33 publications

A Modified Workflow Scheduling Algorithm for Cloud Computing Environment

A Modified Workflow Scheduling Algorithm for Cloud Computing Environment

An Enhanced Workflow Scheduling Algorithm for Cloud Computing Environment

Contact Info

Product

Resources

About