Efficient content retrieval in fog zone using Nano‐Caches. (9th July 2019)

• Record Type: Journal Article
• Authors: Singh, Simar Preet; Kumar, Rajesh; Sharma, Anju
• Is Part Of: Concurrency and computation. Volume 32:Number 2(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-07-09
• Subjects: cache -- edge computing -- fog computing -- Nano‐Cache; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.5438
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: It is always desired to improve the response time from cloud servers, which deliver contents without buffering. As the penetration of mobile/fog devices is increasing, the limits of cellular ranges come under question. This question arises in spite of the fact that the current Internet Service Providers and data operators are adding cellular towers frequently to reduce delay and enhance performance. This performance can be improved by increasing Nano‐Cache(s) at the edges of the network for forwarding interrelated contents to remote corner of the earth. In this research work, Nano‐Caches are integrated for delivering contents efficiently, using search‐based optimization techniques, which are energy and response aware in nature. An algorithm, namely, Modified Teaching Learning‐Based Optimization(MTLBO), is devised and implemented in fog zone to find efficient route for forwarding contents using Nano‐Caches and subsequently to improve content retrieval time. Mathematical distribution model of traffic is used for simulation process. MTLBO is compared with existing algorithms, namely, Teaching Learning‐Based Optimization (TLBO) Algorithm and Simulated Annealing (SA) Algorithm. The design of experiments (DOE) was carried out to observe number of iterations, learning rate, and by changing the network size. Java library was used for observing values of memory and execution time. The results show that Modified Teaching Learning‐Based Optimization (MTLBO) approach is better than Teaching Learning‐Based Optimization (TLBO) approach as it has less overheads in terms of memory (considering number of fog caches) and network size for delivering contents at remote areas. In comparison to the Simulated Annealing (SA) algorithm, MTLBO performs better in terms of execution time, overhead in terms of memory, and scalability as function of network size.