Abstract
Mobile cloud computing is an emerging technology in recent years. This technology reduces battery consumption and execution time by executing mobile applications in remote cloud server. The virtual machine (VM) load balancing among cloudlets in MCC improves the performance of application in terms of response time. Genetic algorithm (GA) is popular for providing optimal solution for load balancing problems. GA can perform well in both homogeneous and heterogeneous environments. In this paper, the authors consider multi-objective genetic algorithm for load balancing in MCC (MOGALMCC) environment. In MOGALMCC, they consider distance, bandwidth, memory, and cloudlet server load to find optimal cloudlet before scheduling VM in another cloudlet. The framework MOGALMCC aims to improve response time as well as minimizes VM failure rate. The experiment result shows that proposed model performed well by reducing execution time and task waiting time at server.
Highlights
Cloud Computing is popular resource platform where user can offload their applications for executing and get result back in order to overcome the limitations of mobile device
We have evaluated proposed MOGALMCC algorithm in NS-3 Simulation Environment
It is clearly noticeable that the MOGALMCC consumes less time compared with other existing algorithms
Summary
Cloud Computing is popular resource platform where user can offload their applications for executing and get result back in order to overcome the limitations of mobile device. In order to improve the performance of the Mobile device, MCC has introduced a Novel concept called Offloading, which can offload resource intensive application into the Cloud. In MCC, mobile devices can offload intense application to the remote Cloud for faster execution(Chun et al, 2011)(Cuervo et al, 2010)(Gkatzikis & Koutsopoulos, 2013)(R. The distance between mobile device and Cloud remote server increases the response time. The previous works focused on static task execution In this proposed method, we consider bandwidth, load and distance as constraints to select Cloudlet for scheduling among Cloudlets.
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