2016 46th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN) (2016)
June 28, 2016 to July 1, 2016
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/DSN.2016.17
Cloud-based systems get changed more frequently than traditional systems. These frequent changes involve sporadic operations such as installation and upgrade. Sporadic operations may fail due to the uncertainty of cloud platforms. Each sporadic operation manipulates a number of cloud resources. The accessibility of resources manipulated makes it possible to build an accurate process model of the correct behavior for an operation and its desired effects. This paper proposes a non-intrusive recovery approach for sporadic operations on cloud, called POD-Recovery. POD-Recovery utilizes the above-mentioned process model of the operation. When needed, it triggers recovery actions based on the model through non-intrusive means, i.e., without modifying the code which implements the sporadic operation. POD-Recovery employs an efficient artificial intelligence (AI) planning technique for generating recovery plans. We implement POD-Recovery and evaluate it by recovering from faults injected into 920 runs of five representative sporadic operations.
Cloud computing, Runtime, Artificial intelligence, Monitoring, Virtual machining, Training, Australia
M. Fu, L. Zhu, I. Weber, L. Bass, A. Liu and X. Xu, "Process-Oriented Non-intrusive Recovery for Sporadic Operations on Cloud," 2016 46th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN), Toulouse, France, 2016, pp. 85-96.