Reliability Aware Cost Optimization for Memory Constrained Cloud Workflows

Cao, E; Musa, Saira; Zhang, Jianning; Chen, Mingsong; Wei, Tongquan; Fu, Xin; Qiu, Meikang

doi:10.1007/978-3-030-38961-1_13

Cited by 1 publication

(1 citation statement)

References 19 publications

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“…Wu et al [16] proposed a soft error-aware and energy-efficient task scheduling method for workflow applications in DVFS-enabled cloud infrastructures under reliability and completion time constraints. Cao et al [17] proposed a soft error-aware VM selection and the task scheduling approach to minimize the execution cost of cloud workflows under makespan, reliability, and memory constraints while considering soft errors in cloud data centers. Garg et al [18] proposed a new scheduling algorithm called the reliability and energy-efficient workflow scheduling algorithm, which jointly optimized lifetime reliability of application and energy consumption and guaranteed the userspecified QoS constraint.…”

Section: Related Workmentioning

confidence: 99%

A Novel Coevolutionary Approach to Reliability Guaranteed Multi-Workflow Scheduling upon Edge Computing Infrastructures

Wang

Zheng

Chen

et al. 2020

Security and Communication Networks

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Recently, mobile edge computing (MEC) is widely believed to be a promising and powerful paradigm for bringing enterprise applications closer to data sources such as IoT devices or local edge servers. It is capable of energizing novel mobile applications, especially the ultra-latency-sensitive ones, by providing powerful local computing capabilities and lower end-to-end delays. Nevertheless, various challenges, especially the reliability-guaranteed scheduling of multitask business processes in terms of, e.g., workflows, upon distributed edge resources and servers, are yet to be carefully addressed. In this paper, we propose a novel edge-environment-based multi-workflow scheduling method, which incorporates a reliability estimation model for edge-workflows and a coevolutionary algorithm for yielding scheduling decisions. The proposed approach aims at maximizing the reliability, in terms of success rates, of services deployed upon edge infrastructures while minimizing service invocation cost for users. We conduct simulative experimental case studies based on multiple well-known scientific workflow templates and a well-known dataset of edge resource locations as well. Simulative results clearly suggest that our proposed approach outperforms traditional ones in terms of workflow success rate and monetary cost.

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Section: Related Workmentioning

confidence: 99%