Resource and Cost Aware Glowworm Mapreduce Optimization Based Big Data Processing in Geo Distributed Data Center
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Handling large data in geographically distributed information centers with resource and cost optimization is a key challenge. With several approaches being designed, handling a large volume of data in multiple datacenters in an inappropriate manner yet is considered to be a time-consuming process. To address these issues, a Multivariate Metaphor based Metaheuristic Glowworm Swarm Map-Reduce Optimization (MM-MGSMO) technique is presented. Here, with search space and large data volume as input for geo-distributed datacenters, glowworm (i.e. virtual machine) population is initialized. With each glowworm possessing a certain amount of luciferin (i.e. objective function), multiple objective functions (i.e. bandwidth, storage capacity, energy and computation cost) are defined for each virtual machine. Next, the glowworm position is updated according to the neighboring factor by means of probability. Followed by this, MapReduce function identifies the optimal virtual machine and accordingly allocation is performed, therefore improving data allocation efficiency. Besides, the workload is assigned across datacenters, reduction in computation cost and storage capacity is guaranteed. Experimental evaluation of MM-MGSMO approach with existing methods attained improved performances with factors such as data allocation efficiency, false-positive rate, storage capacity compared with other cutting edge technologies such as Joint optimization algorithm and Game theory-based dynamic resource allocation strategy.
KeywordsBig data processing Geo-distributed data center Glowworm Swarm optimization MapReduce function
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Conflict of interest
The authors declare that they have no conflict of interest.
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