Scalable Key Parameter Yield of Resources Model for Performance Enhancement in Mobile Cloud Computing

Scalable Key Parameter Yield of Resources Model for Performance Enhancement in Mobile Cloud... Cloud computing is a model which facilitate ubiquitous, convenient and on demand access to a shared pool of scalable and configurable computing resources. Cloud computing has made great impact on the information technology industries but still it faces lots of challenges like mobility in devices, load balancing, energy consumption, security and performance of cloud etc. Future prospect of cloud computing convinced and motivated us to do research on cloud computing framework which uses cloudlet as a service provider. So in the existing framework for mobile cloudlet center, we find three main problems. First problem is that the existing framework does not have concrete mechanism to consider the feedback given to the cloudlet for the task they performed for various other mobile devices. Second problem is that there is no method or framework available which can fetch dynamic parameter of mobile devices and manipulate the information for evaluation of the performance of cloudlets and potential mobile devices which applies to work as cloudlet. Third and the last problem says that there is no measure by which root server can decide whether to scale up or scale down the cloud–cloudlet system. In the proposed model, we have considered the feedback sent by the mobile device and stored it in a directory maintained by the root server at cloud. The root server refers this directory while allocating the task to cloudlets. For the second problem we have used the Gabriel architecture and crowd sensing framework collectively. The combination of these two quite efficiently processes the sensed information at the local level and passes the processed information to root server for decision making. For the last problem we have proposed metrics of yield factor for various parameters which will be calculated by the root server and based on those yield factor values root server can decide whether to scale up the cloud–cloudlet system or not. The proposed scalable key-parameter yield of resources model executed all three solutions on Cloudsim simulator and the results for various parameters are compared with the existing framework of mobile cloudlet center system. These comparisons clearly depict the better performance of our proposed scalable key-parameter yield of resources model over existing framework. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Wireless Personal Communications Springer Journals

Scalable Key Parameter Yield of Resources Model for Performance Enhancement in Mobile Cloud Computing

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Publisher
Springer US
Copyright
Copyright © 2017 by Springer Science+Business Media New York
Subject
Engineering; Communications Engineering, Networks; Signal,Image and Speech Processing; Computer Communication Networks
ISSN
0929-6212
eISSN
1572-834X
D.O.I.
10.1007/s11277-017-4035-4
Publisher site
See Article on Publisher Site

Abstract

Cloud computing is a model which facilitate ubiquitous, convenient and on demand access to a shared pool of scalable and configurable computing resources. Cloud computing has made great impact on the information technology industries but still it faces lots of challenges like mobility in devices, load balancing, energy consumption, security and performance of cloud etc. Future prospect of cloud computing convinced and motivated us to do research on cloud computing framework which uses cloudlet as a service provider. So in the existing framework for mobile cloudlet center, we find three main problems. First problem is that the existing framework does not have concrete mechanism to consider the feedback given to the cloudlet for the task they performed for various other mobile devices. Second problem is that there is no method or framework available which can fetch dynamic parameter of mobile devices and manipulate the information for evaluation of the performance of cloudlets and potential mobile devices which applies to work as cloudlet. Third and the last problem says that there is no measure by which root server can decide whether to scale up or scale down the cloud–cloudlet system. In the proposed model, we have considered the feedback sent by the mobile device and stored it in a directory maintained by the root server at cloud. The root server refers this directory while allocating the task to cloudlets. For the second problem we have used the Gabriel architecture and crowd sensing framework collectively. The combination of these two quite efficiently processes the sensed information at the local level and passes the processed information to root server for decision making. For the last problem we have proposed metrics of yield factor for various parameters which will be calculated by the root server and based on those yield factor values root server can decide whether to scale up the cloud–cloudlet system or not. The proposed scalable key-parameter yield of resources model executed all three solutions on Cloudsim simulator and the results for various parameters are compared with the existing framework of mobile cloudlet center system. These comparisons clearly depict the better performance of our proposed scalable key-parameter yield of resources model over existing framework.

Journal

Wireless Personal CommunicationsSpringer Journals

Published: Feb 6, 2017

References

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