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Reliable computing service in massive-scale systems through rapid low-cost failover

Research output: Contribution to journalJournal article

Published
  • Renyu Yang
  • Yang Zhang
  • Peter Garraghan
  • Yihui Feng
  • Jin Ouyang
  • Jie Xu
  • Zhuo Zhang
  • Chao Li
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<mark>Journal publication date</mark>11/2017
<mark>Journal</mark>IEEE Transactions on Services Computing
Issue number6
Volume10
Number of pages15
Pages (from-to)969-983
Publication statusPublished
Early online date21/03/16
Original languageEnglish

Abstract

Large-scale distributed systems in Cloud datacenter are capable of provisioning service to consumers with diverse business requirements. Providers face pressure to provision uninterrupted reliable services while reducing operational costs due to significant software and hardware failures. A widely used means to achieve such a goal is using redundant system components to implement usertransparent failover, yet its effectiveness must be balanced carefully without incurring heavy overhead when deployed – an important practical consideration for complex large-scale systems. Failover techniques developed for Cloud systems often suffer serious limitations, including mandatory restart leading to poor cost-effectiveness, as well as solely focusing on crash failures, omitting other important types, e.g. timing failures and simultaneous failures. This paper addresses these limitations by presenting a new approach to user-transparent failover for massive-scale systems. The approach uses soft-state inference to achieve rapid failure recovery and avoid unnecessary restart, with minimal system resource overhead. It also copes with different failures, including correlated and simultaneous events. The proposed approach was implemented, deployed and evaluated within Fuxi system, the underlying resource management system used within Alibaba Cloud. Results demonstrate that our approach tolerates complex failure scenarios while incurring at worst 228.5 microsecond instance overhead with 1.71% additional CPU usage.

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© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.