Journal
IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS
Volume 30, Issue 11, Pages 2408-2421Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TPDS.2019.2917900
Keywords
Clouds; Fault tolerance; Fault tolerant systems; Quality of service; Cloud computing; Software reliability; Cloud application; criticality measure; fault tolerance; reliability; sensitivity analysis; system architecture
Funding
- NSFC project [61672152]
- Humanity and Social Science Youth Fund of Ministry of Education of China [18YJCZH170]
- Youth Innovation Fund of NJFU [CX2016031]
- Jiangsu Overseas Visiting Scholar Program for University Prominent Young & Middle-aged Teachers and Presidents
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The widespread adoption of service computing allows software to be developed by outsourcing open cloud services (i.e., SOAP-based or RESTful Web APIs) through mashup or service composition techniques. Fault tolerance for the purpose of assuring the stable execution for cloud-based software (or CBS) application has attracted great attention in coping with a loosely coupled CBS operating under dynamic and uncertain running environments. It is too expensive to rent massively redundant cloud services for CBS fault tolerance application. To reduce budget but guarantee the effectiveness of CBS fault tolerance, identifying critical components within a CBS composite system is of significant importance. We integrate CBS composite system architecture analysis and reliability sensitivity analysis approaches and propose an Architecture-based Reliability-sensitive Criticality Measure (or ARCMeas) method in this paper. We verify ARCMeas application through a cost-effective fault tolerance CBS by presenting a particle swarm optimization (PSO)-based cost-effective fault tolerance strategy determination (or PSO-CFTD) algorithm. Experimental results suggest the effectiveness of the approach.
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