ABSTRACT
This paper addresses the load information collection problem for load balancing the cloud data center. This work models Cloud data center as a graph with vertices denoting the servers hosting Virtual Machines and the edges corresponding to communication links among the servers. As Virtual Machines are created and released over time, a load balancer must keep track the load of the servers in cloud data center in order to distribute them uniform among the servers so as to have a load balanced cloud data center. This work harnesses mobile agent concept in cloud data center for load information collection, since both the mobile agent and cloud computing technologies are promising and commercially useful. The idea is to securely explore the cloud data center network quickly with mobile agents to collect load information from the servers and reporting them to load balancer as fast as possible. The goal is to minimize the cover time of the network and minimize the space requirement during load data collection. This paper proposes a secure network exploration algorithm for load data collection that decreases the time taken for exploration and space requirement. The theoretical analysis shows that the proposed approach takes O(logdn) time for network exploration, where as other deterministic approaches used for comparison take more time.
- Jidong Xiao, Lei Lu, Hai Huang, Haining Wang. 2018.Virtual Machine Extrospection: A Reverse Information Retrieval in Clouds. IEEE Transactions on Cloud Computing, 11--13.Google ScholarCross Ref
- Chubo Liu, Kenli Li, and Keqin Li. 2018. A Game Approach to Multi-servers Load Balancing with Load-Dependent Server Availability Consideration. IEEE Transactions on Cloud Computing.1--14.Google ScholarCross Ref
- Kwang Mong Sim. 2018. Agent-based Approaches for Intelligent InterCloud Resource Allocation. IEEE Transactions on Cloud Computing.1--14.Google Scholar
- Octavio Gutierrez-Garcia J., and Adrian Ramirez-Nafarrate. 2015. Collaborative Agents for Distributed Load Management in Cloud Data Centers using Live Migration of Virtual Machines. IEEE Transactions on services computing, 1939--1374.Google Scholar
- Zolt_anBalogh, Emil Gatial, and Ladislav Hluchy.2014. Agent-based cloud resource management for secure cloud infrastructures. Computing and Informatics, vol. 33. 1333--1355.Google Scholar
- Sergio Gonzalez-Valenzuela and Son T. Vuong. 2002. Evaluation of Migration Strategies for Mobile Agents in Network Routing. Proceedings of International Workshop on Mobile Agents for Telecommunication Applications, LNCS vol.2521.141--150. Google ScholarDigital Library
- Oyediran M., Fagbola T. M, Olabiyisi S., Omidiora E., Fawole A.2016. A Survey on Migration Process of Mobile Agent. Proceedings of the World Congress on Engineering and Computer Science 2016. vol. I. 1--5.Google Scholar
- PanagiotaFatouro. 2013. Mobile Agents in Distributed Computing: Network Exploration. Bulletin of the EATCS no 109. 54--69.Google Scholar
- Nicolas Hanusse, EvangelosKranakis, and Danny Krizanc. 2004. Searching with mobile agents in networks with liars. Discrete Applied Mathematics. 137. 69--85.Google Scholar
- Paola Flocchini, Matthew Kelletty, Peter Masony, and Nicola Santoro. 2009. Map Construction and Exploration by Mobile Agents Scattered in a Dangerous Network. Proceedings of the IEEE International Symposium on Parallel & Distributed Processing.1--8. Google ScholarDigital Library
- BehroozSafarinejadian, and KazemHasanpour. 2016. Distributed Data Clustering Using Mobile Agents and EM Algorithm. IEEE Systems journal, 10(1). 1--9.Google Scholar
- Yongsheng Ding, and Lei Gao. 2011.Macrodynamics Analysis of Migration Behaviors in Large-Scale Mobile Agent Systems for the Future Internet, IEEE transactions on systems, man, and cybernetics-Part a: Systems and Humans.41(5).1032--1036. Google ScholarDigital Library
- FukuhitoOoshita, Shinji Kawai, HirotsuguKakugawa, and ToshimitsuMasuzawa. 2014. Randomized Gathering of Mobile Agents in Anonymous Unidirectional Ring Networks, IEEE transactions on parallel and distributed systems, 25(5). 1289--1296. Google ScholarDigital Library
- Shehla Abbas, Mohamed Mosbah, and AkkaZemmari. 2008. Merging Time of Random Mobile Agents. Dynamics in Logistics. 179--190.Google Scholar
- JérémieChalopin, Shantanu Das, and PeterWidmayer. 2010. Rendezvous of Mobile Agents in Directed Graphs, Proceedings of the International Symposium on Distributed Computing. LNCS vol.6343. 282--296. Google ScholarDigital Library
- JurekCzyzowicz, Andrzej Pelc, Arnaud Labourel. 2012. How to meet asynchronously (almost) everywhere, ACM Transactions on Algorithms (TALG). 8(4). Google ScholarDigital Library
- Raji F, and TorkLadani B. 2010. Anonymity and security for autonomous mobile agents. IET Information Security. 4(4). 397--410.Google ScholarCross Ref
- Geetha G, and Jayakumar C. 2015. Implementation of Trust and Reputation Management for Free-Roaming Mobile Agent Security. IEEE Systems Journal. 9(2). 1--11.Google ScholarCross Ref
- Murali G, Anusha K, Shirisha A, Sravya A. 2011. Remote Procedure Calls implementing using Distributed Algorithm. International Journal of Computer Technology and Applications. 2(6). 1742--1746.Google Scholar
Index Terms
- Mobile Agent-based Secure Cloud Data Center Exploration for Load Data Retrieval Using Graph Theory
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