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Reliability-Ensured Efficient Scheduling With Replication in Cloud Environment

delete2022-06-01
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PRE
AI
C
Chinmaya Kumar Swain *
A
Aryabartta Sahu
DOI:10.1109/JSYST.2021.3112098delete
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摘要

摘要

En 中文
Server consolidation in a virtualized cloud environment improves resource utilization by hosting many virtual machines (VMs) on a single machine. The host server failure induces a serious risk of the interruption of services provided through VMs hosted on the failed server. Ensuring high reliability and availability from the unpredicted host server failures is a challenging task, and this assurance guarantee is the quality of service to the users. Executing latency-sensitive workloads within a time frame, multireplica strategy-based reliability assurance is used in general. In this article, we propose two reliability-ensured scheduling approaches by strategically allocating the jobs to machines to satisfy their reliability and deadline requirements. The proposed approaches Replication with equal failure rate and Efficient reliability replication method are used to schedule the jobs to machines with equal and different failure rates, respectively, while satisfying both the deadline and reliability requirements of the jobs. Both the scheduling approaches dynamically select the machines for the jobs to minimize the number of replicas based on job characteristics and failure rate of machines. Also, we adopt an overlapping mechanism to further minimize the resource utilization. The extensive simulation results on randomly generated and real-world data at different scales show that the proposed approaches perform better than other state-of-the-art approaches.
Keyword:
Reliability
Task analysis
Servers
Cloud computing
Resource management
Quality of service
Fault tolerant systems
Cloud computing
reliability
replication
scheduling
virtualization

期刊

I
IEEE Open Journal of Circuits and Systems
IF:
2.4
论文数:
4.5K
被引数:
387

机构

S
srm university-ap
学者数:
1.0K
论文数: 880
被引数: 0
I
indian institute of technology system (iit system)
学者数:
9.5W
论文数: 9.9W
被引数: 93
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