Mobile cloud storage (MCS) is being extensively used nowadays toprovide data access services to various mobile platforms such assmart phones and tablets. For cross-platform mobile apps, MCS is afoundation for sharing and accessing user data as well as supportingseamless user experience in a mobile cloud computing environment. However, the mobile usage of smart phones or tablets is quite differentfrom legacy desktop computers, in the sense that each user hashis/her own mobile usage pattern. Therefore, it is challenging todesign an efficient MCS that is optimized for individual users. Inthis paper, we investigate a distributed MCS system whoseperformance is optimized by exploiting the fine-grained contextinformation of every mobile user. In this distributed system,lightweight storage servers are deployed pervasively, such that datacan be stored closer to its user. We systematically optimize thedata access efficiency of such a distributed MCS by exploiting threetypes of user context information: mobility pattern, networkcondition, and data access pattern. We propose two optimizationformulations: a centralized one based on mixed-integer linearprogramming (MILP), and a distributed one based on stable matching. We then develop solutions to both formulations. Comprehensivesimulations are performed to evaluate the effectiveness of theproposed solutions by comparing them against their counterpartsunder various network and context conditions.
|Original language||English (US)|
|Title of host publication||Proceedings - IEEE 37th International Conference on Distributed Computing Systems, ICDCS 2017|
|Editors||Kisung Lee, Ling Liu|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||11|
|State||Published - Jul 13 2017|
|Event||37th IEEE International Conference on Distributed Computing Systems, ICDCS 2017 - Atlanta, United States|
Duration: Jun 5 2017 → Jun 8 2017
|Name||Proceedings - International Conference on Distributed Computing Systems|
|Other||37th IEEE International Conference on Distributed Computing Systems, ICDCS 2017|
|Period||6/5/17 → 6/8/17|
Bibliographical noteFunding Information:
The work of T. Shu was supported in part by NSF under grants CNS-1659962 and CNS-1659965. The work of L. Yang was supported in part by NSF under grants CNS-1343189 and DMS-1521746. The work of S. Cui was supported in part by DoD with grant HDTRA1-13-1-0029, by grant NSFC- 61328102, and by NSF with grants DMS-1622433, AST- 1547436, ECCS-1508051, CNS-1343155.
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