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A control theoretical approach to non-intrusive geo-replication for cloud services

Author

Summary, in English

Complete data center failures may occur due to disastrous events such as earthquakes or fires. To attain robustness against such failures and reduce the probability of data loss, data must be replicated in another data center sufficiently geographically separated from the original data center. Implementing geo-replication is expensive as every data update operation in the original data center must be replicated in the backup. Running the application and the replication service in parallel is cost effective but creates a trade-off between potential replication consistency and data loss and reduced application performance due to network resource contention. We model this trade-off and provide a control-theoretical solution based on Model Predictive Control to dynamically allocate network bandwidth to accommodate the objectives of both replication and application data streams. We evaluate our control solution through simulations emulating the individual services, their traffic flows, and the shared network resource. The MPC solution is able to maintain a consistent performance over periods of persistent overload, and is quickly able to indiscriminately recover once the system return to a stable state. Additionally, the MPC balances the two objectives of consistency and performance according to the proportions specified in the objective function.

Publishing year

2016-12-27

Language

English

Pages

1649-1656

Publication/Series

2016 IEEE 55th Conference on Decision and Control, CDC 2016

Document type

Conference paper

Publisher

IEEE - Institute of Electrical and Electronics Engineers Inc.

Topic

  • Electrical Engineering, Electronic Engineering, Information Engineering

Conference name

55th IEEE Conference on Decision and Control 2016

Conference date

2016-09-12 - 2016-09-14

Conference place

Las Vegas, NV, United States

Status

Published

Research group

  • LCCC

ISBN/ISSN/Other

  • ISBN: 9781509018376