A Software Reliability Model for Cloud-Based Software Rejuvenation Using Dynamic Fault Trees

Author:

Rahme Jean1,Xu Haiping1

Affiliation:

1. Computer and Information Science Department, University of Massachusetts Dartmouth, North Dartmouth, MA 02747, USA

Abstract

Correctly measuring the reliability and availability of a cloud-based system is critical for evaluating its system performance. Due to the promised high reliability of physical facilities provided for cloud services, software faults have become one of the major factors for the failures of cloud-based systems. In this paper, we focus on the software aging phenomenon where system performance may be progressively degraded due to exhaustion of system resources, fragmentation and accumulation of errors. We use a proactive technique, called software rejuvenation, to counteract the software aging problem. The dynamic fault tree (DFT) formalism is adopted to model the system reliability before and during a software rejuvenation process in an aging cloud-based system. A novel analytical approach is presented to derive the reliability function of a cloud-based Hot SPare (HSP) gate, which is further verified using Continuous Time Markov Chains (CTMC) for its correctness. We use a case study of a cloud-based system to illustrate the validity of our approach. Based on the reliability analytical results, we show how cost-effective software rejuvenation schedules can be created to keep the system reliability consistently staying above a predefined critical level.

Publisher

World Scientific Pub Co Pte Lt

Subject

Artificial Intelligence,Computer Graphics and Computer-Aided Design,Computer Networks and Communications,Software

Cited by 11 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Real-Time rejuvenation scheduling for cloud systems with virtualized software spares;Journal of Systems and Software;2024-11

2. A novel multi-step-ahead approach for cloud server aging prediction based on hybrid deep learning model;Engineering Applications of Artificial Intelligence;2024-07

3. An Empirical Study on Software Aging of Long-Running Object Detection Algorithms;2022 IEEE 22nd International Conference on Software Quality, Reliability and Security (QRS);2022-12

4. Memory Degradation Analysis in Private and Public Cloud Environments;2021 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW);2021-10

5. A Comparative Analysis of Software Aging in Image Classifiers on Cloud and Edge;IEEE Transactions on Dependable and Secure Computing;2021

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