Cost Effective and Deadline Constrained Scientific Workflow Scheduling for Commercial Clouds

2015 ◽  
Author(s):  
Vahid Arabnejad ◽  
Kris Bubendorfer
Keyword(s):  
Cost Effective ◽  
Scientific Workflow ◽  
Workflow Scheduling

Cost Effective Heuristic workflow scheduling algorithm in Cloud under Deadline Constraint

2019 ◽  
Vol 12 ◽  
Author(s):  
Jasraj Meena ◽  
Manu Vardhan
Keyword(s):  
Cloud Computing ◽  
Scheduling Algorithm ◽  
Cost Effective ◽  
Scientific Workflow ◽  
Scientific Workflows ◽  
Workflow Scheduling ◽  
Performance Variation ◽  
Acquisition Delay ◽  
Very High ◽  
Deadline Constraint

Cloud computing is used to deliver IT resources over the internet. Due to the popularity of cloud computing, nowadays, most of the scientific workflows are shifted towards this environment. There are lots of algorithms has been proposed in the literature to schedule scientific workflows in the cloud, but their execution cost is very high as well as they are not meeting the user-defined deadline constraint. This paper focuses on satisfying the userdefined deadline of a scientific workflow while minimizing the total execution cost. So, to achieve this, we have proposed a Cost-Effective under Deadline (CEuD) constraint workflow scheduling algorithm. The proposed CEuD algorithm considers all the essential features of Cloud and resolves the major issues such as performance variation, and acquisition delay. We have compared the proposed CEuD algorithm with the existing literature algorithms for scientific workflows (i.e., Montage, Epigenomics, and CyberShake) and getting better results for minimizing the overall execution cost of the workflow while satisfying the user-defined deadline.

scholarly journals Dynamic Fault-Tolerant Workflow Scheduling with Hybrid Spatial-Temporal Re-Execution in Clouds

Information ◽  
2019 ◽  
Vol 10 (5) ◽  
pp. 169 ◽  
Author(s):  
Na Wu ◽  
Decheng Zuo ◽  
Zhan Zhang
Keyword(s):  
Large Scale ◽  
Fault Tolerant ◽  
Critical Path ◽  
High Reliability ◽  
Low Cost ◽  
Cost Effective ◽  
Scientific Workflow ◽  
Workflow Scheduling ◽  
Workflow Execution ◽  
Computing Environments

Improving reliability is one of the major concerns of scientific workflow scheduling in clouds. The ever-growing computational complexity and data size of workflows present challenges to fault-tolerant workflow scheduling. Therefore, it is essential to design a cost-effective fault-tolerant scheduling approach for large-scale workflows. In this paper, we propose a dynamic fault-tolerant workflow scheduling (DFTWS) approach with hybrid spatial and temporal re-execution schemes. First, DFTWS calculates the time attributes of tasks and identifies the critical path of workflow in advance. Then, DFTWS assigns appropriate virtual machine (VM) for each task according to the task urgency and budget quota in the phase of initial resource allocation. Finally, DFTWS performs online scheduling, which makes real-time fault-tolerant decisions based on failure type and task criticality throughout workflow execution. The proposed algorithm is evaluated on real-world workflows. Furthermore, the factors that affect the performance of DFTWS are analyzed. The experimental results demonstrate that DFTWS achieves a trade-off between high reliability and low cost objectives in cloud computing environments.

Deadline-Constrained Cost-Effective Load-Balanced Improved Genetic Algorithm for Workflow Scheduling

2021 ◽  
Vol 16 (4) ◽  
pp. 1-34
Author(s):  
Sandeep Kumar Bothra ◽  
Sunita Singhal ◽  
Hemlata Goyal
Keyword(s):  
Genetic Algorithm ◽  
Virtual Machines ◽  
Cost Effective ◽  
Critical Issue ◽  
Scientific Workflow ◽  
Workflow Scheduling ◽  
Improved Genetic Algorithm ◽  
Greedy Strategy ◽  
Mutation Operators ◽  
Load Balanced

Resource scheduling in a cloud computing environment is noteworthy for scientific workflow execution under a cost-effective deadline constraint. Although various researchers have proposed to resolve this critical issue by applying various meta-heuristic and heuristic approaches, no one is able to meet the strict deadline conditions with load-balanced among machines. This article has proposed an improved genetic algorithm that initializes the population with a greedy strategy. Greedy strategy assigns the task to a virtual machine that is under loaded instead of assigning the tasks randomly to a machine. In general workflow scheduling, task dependency is tested after each crossover and mutation operators of genetic algorithm, but here the authors perform after the mutation operation only which yield better results. The proposed model also considered booting time and performance variation of virtual machines. The authors compared the algorithm with previously developed heuristics and metaheuristics both and found it increases hit rate and load balance. It also reduces execution time and cost.

scholarly journals A Hybrid Algorithm for Multi-Objective Scientific Workflow Scheduling in IaaS Cloud

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 125783-125795 ◽  
Author(s):  
Yongqiang Gao ◽  
Shuyun Zhang ◽  
Jiantao Zhou
Keyword(s):  
Hybrid Algorithm ◽  
Scientific Workflow ◽  
Workflow Scheduling ◽  
Multi Objective

A Novel Approach to Location-Aware Scheduling of Workflows Over Edge Computing Resources

2020 ◽  
Vol 17 (3) ◽  
pp. 56-68
Author(s):  
Yin Li ◽  
Yuyin Ma ◽  
Ziyang Zeng
Keyword(s):  
Real World ◽  
Service Level Agreement ◽  
Service Level ◽  
Scientific Workflow ◽  
Edge Computing ◽  
Workflow Scheduling ◽  
Location Aware ◽  
Novel Approach ◽  
Negative Impacts ◽  
Multiple Metrics

Edge computing is pushing the frontier of computing applications, data, and services away from centralized nodes to the logical extremes of a network. A major technological challenge for workflow scheduling in the edge computing environment is cost reduction with service-level-agreement (SLA) constraints in terms of performance and quality-of-service requirements because real-world workflow applications are constantly subject to negative impacts (e.g., network congestions, unexpected long message delays, shrinking coverage, range of edge servers due to battery depletion. To address the above concern, we propose a novel approach to location-aware and proximity-constrained multi-workflow scheduling with edge computing resources). The proposed approach is capable of minimizing monetary costs with user-required workflow completion deadlines. It employs an evolutionary algorithm (i.e., the discrete firefly algorithm) for the generation of near-optimal scheduling decisions. For the validation purpose, the authors show that our proposed approach outperforms traditional peers in terms multiple metrics based on a real-world dataset of edge resource locations and multiple well-known scientific workflow templates.

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