• GowriPrakash R
• Shankar R
• Duraisamy S

In cloud storage systems, Virtual Machine (VM) migration approaches play a major function in minimizing the power usage and balancing the tasks in the data centers. Owing to the massive amount of clients and applications trying to gain from cloud service, it makes it a complex process for the edge cloud servers to operate in an energy-saving mode. Also, while maximizing the bandwidth between VMs, the chance of traffic congestion presence is increased. To solve this problem, this article proposes an Osmotic Hybrid artificial Bee and Ant Colony with Future Utilization Prediction (OH-BAC-FUP) approach to lessen the number of VM migrations and enhance the load-balancing. First, both ongoing and upcoming resource consumption of PMs and VMs are determined using Linear Regression (LR) and Optimal Piecewise LR (OPLR) schemes. The determined values are given to the OH-BAC which computes the fitness value for choosing the optimal VM to be migrated to the optimal PM. Also, this is further enhanced by proposing a Multipath Traffic Routing (MTR) called OH-BAC-FUP-MTR approach which prevents the chance of congestion while migrating VM to the PM. If any congestion exists because of high bandwidth or traffic flows, then the flows are split into many segments and transfer them via multiple link-disjoint routes. Moreover, Merge-and-Split-based Coalitional Game-theoretic (MSCG) scheme is proposed with OH-BAC-FUP-MTR approach to achieve a proper tradeoff between task reliability and power conservation in heterogeneous data centers. This MSCG is used for splitting the PMs into many sets and choosing the members from those sets to generate effective coalitions. For each coalition, OH-BAC-FUP-MTR is executed which enhances the payoff of each coalition and sustains PMs to operate in a high energy-efficient state. Finally, the simulation results reveal the OH-BAC-FUP-MTR-MSCG accomplishes better efficacy than the classical VM migration approaches.

Cloud computing, VM migration, Load-balancing, Resource consumption, Flow congestion, Osmotic computing.

"Optimized Load-balancing in Cloud Computing based on Traffic and Workload-aware VM Migration", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 4, page no.77-93, April-2021, Available :http://www.jetir.org/papers/JETIR2104013.pdf

"Optimized Load-balancing in Cloud Computing based on Traffic and Workload-aware VM Migration", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 4, page no. pp77-93, April-2021, Available at : http://www.jetir.org/papers/JETIR2104013.pdf