Research Area:  Cloud Computing
Service migration between datacenters can reduce the network overhead within a cloud infrastructure; thereby, also improving the quality of service for the clients. Most of the algorithms in the literature assume that the client access pattern remains stable for a sufficiently long period so as to amortize such migrations. However, if such an assumption does not hold, these algorithms can take arbitrarily poor migration decisions that can substantially degrade system performance. In this paper, we approach the issue of performing service migrations for an unknown and dynamically changing client access pattern. We propose an online algorithm that minimizes the inter-datacenter network, taking into account the network load of migrating a service between two datacenters, as well as the fact that the client request pattern may change “quickly”, before such a migration is amortized. We provide a rigorous mathematical proof showing that the algorithm is 3.8-competitive for a cloud network structured as a tree of multiple datacenters. We briefly discuss how the algorithm can be modified to work on general graph networks with an O(log|V|) probabilistic approximation of the optimal algorithm. Finally, we present an experimental evaluation of the algorithm based on extensive simulations.
Author(s) Name:  Nikos Tziritas; Samee U. Khan; Thanasis Loukopoulos; Spyros Lalis; Cheng-Zhong Xu; Keqin Li and Albert Y. Zomaya
Journal name:   IEEE Transactions on Cloud Computing
Publisher name:  IEEE
Volume Information:  Volume: 8, Issue: 4, Oct.-Dec. 1 2020,Page(s): 1054 - 1068
Paper Link:   https://ieeexplore.ieee.org/abstract/document/7875130