Advances in Grid and Pervasive Computing

To run off-premise private cloud, consumer needs budget for public cloud data-out charge. This amount of expenditure can be considerable for data-intensive organization. Deploying web cache can prevent consumer from duplicated data loading out of their private cloud up to some extent. In present existence, however, there is no cache replacement strategy designed specifically for cloud computing. Devising a cache replacement strategy to truly suit cloud computing paradigm requires ground-breaking design perspective. This paper presents a novel cloud cache replacement policy that optimizes cloud data-out charge, the overall responsiveness of data loadings and the scalability of cloud infrastructure. The measurements demonstrate that the proposed policy achieves superior cost-saving, delay-saving and byte-hit ratios against the other well-known web cache replacement policies.

[1]  Rajkumar Buyya,et al.  CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms , 2011, Softw. Pract. Exp..

[2]  John E. Beasley,et al.  A Genetic Algorithm for the Multidimensional Knapsack Problem , 1998, J. Heuristics.

[3]  Chao-Tung Yang,et al.  Implementation of a dynamic adjustment strategy for parallel file transfer in co-allocation data grids , 2009, The Journal of Supercomputing.

[4]  Asser N. Tantawi,et al.  Dynamic placement for clustered web applications , 2006, WWW '06.

[5]  Ching-Hsien Hsu,et al.  Redundant Parallel File Transfer with Anticipative Recursively-Adjusting Scheme in Data Grids , 2007, ICA3PP.

[6]  Carl M. Harris,et al.  Fundamentals of queueing theory , 1975 .

[7]  Ninghui Li,et al.  On mutually-exclusive roles and separation of duty , 2004, CCS '04.

[8]  Christian Engelmann,et al.  Proactive fault tolerance for HPC with Xen virtualization , 2007, ICS '07.

[9]  Andrzej Kochut,et al.  Dynamic Placement of Virtual Machines for Managing SLA Violations , 2007, 2007 10th IFIP/IEEE International Symposium on Integrated Network Management.

[10]  Henk C. Tijms,et al.  Approximations for the waiting time distribution of the M/G/c queue , 1982, Perform. Evaluation.

[11]  Rajarshi Das,et al.  Utility functions in autonomic systems , 2004, International Conference on Autonomic Computing, 2004. Proceedings..

[12]  Yong Zhao,et al.  Cloud Computing and Grid Computing 360-Degree Compared , 2008, GCE 2008.

[13]  Rajkumar Buyya,et al.  Energy Efficient Allocation of Virtual Machines in Cloud Data Centers , 2010, 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing.

[14]  Rajarshi Das,et al.  Utility-Function-Driven Resource Allocation in Autonomic Systems , 2005, Second International Conference on Autonomic Computing (ICAC'05).

[15]  Paul Goodwin,et al.  The Holt-Winters Approach to Exponential Smoothing: 50 Years Old and Going Strong , 2010 .

[16]  Myron Hlynka,et al.  Queueing Networks and Markov Chains (Modeling and Performance Evaluation With Computer Science Applications) , 2007, Technometrics.

[17]  Ching-Hsien Hsu,et al.  A Recursively-Adjusting Co-allocation scheme with a Cyber-Transformer in Data Grids , 2009, Future generations computer systems.

[18]  Chao-Tung Yang,et al.  A Dynamic Adjustment Strategy for File Transformation in Data Grids , 2007, NPC.

[19]  Gautam Kar,et al.  Application Performance Management in Virtualized Server Environments , 2006, 2006 IEEE/IFIP Network Operations and Management Symposium NOMS 2006.

[20]  Malgorzata Steinder,et al.  Server virtualization in autonomic management of heterogeneous workloads , 2007, Integrated Network Management.

[21]  Chao-Tung Yang,et al.  Improvements on dynamic adjustment mechanism in co-allocation data grid environments , 2007, The Journal of Supercomputing.

[22]  Pedro Pla.  Drbd in a heartbeat , 2006 .