Abstract
Infrastructure as a Service (IaaS) has become one of the most dominant features that cloud computing offers nowadays. IaaS enables datacenter's hardware to get virtualized which allows Cloud providers to create multiple Virtual Machine (VM) instances on a single physical machine, thus improving resource utilization and increasing the Return on Investment (ROI). VM consolidation includes issues like choosing appropriate algorithm for selection of VMs for migration and placement of VMs to suitable hosts. VMs need to be migrated from overutilized host to guarantee that demand for computer resources and performance requirements are accomplished. Besides, they need to be migrated from underutilized host to deactivate that host for saving power consumption. In order to solve the problem of energy and performance, efficient dynamic VM consolidation approach is introduced in literature. In this work, we have proposed multiple redesigned VM placement algorithms and introduced a technique by clustering VMs to migrate by taking account both CPU utilization and allocated RAM. We implement and study the performance of our algorithms on a cloud computing simulation toolkit known as CloudSim using PlanetLab workload data. Simulation results demonstrate that our proposed techniques outperform the default VM Placement algorithm designed in CloudSim.
Highlights
We are living in a world of data where data pervades and controls almost every aspect of our lives
In our work we have followed the heuristics that Beloglazov and Buyya [3] stated in their work for dynamic Virtual Machine (VM) consolidation, but instead of their modified best fit decreasing algorithm for VM placement, we proposed our algorithms based on other bin packing solutions for VM placement with custom modification
We could verify that our proposed algorithms perform significantly better than the built in Power Aware Best Fit Decreasing (PABFD) VM placement algorithm
Summary
We are living in a world of data where data pervades and controls almost every aspect of our lives. Beloglazov and Buyya [3] proposed adaptive heuristics for energy and performance efficient dynamic VM consolidation It includes many methods for host underload or overload detection to choose VMs to migrate from those underloaded and overloaded hosts. Dong et al [7] proposed most-efficient-server-first (MESF) task-scheduling algorithm for cloud computing data center They reduced the energy consumption by limiting the number of active servers and response time. The algorithm returns the migration map which has the combined information of new VM placement which is needed to be migrated from both overloaded and underloaded hosts They proposed a modified version of BFD (best fit decreasing) for VM placement solution. Modified techniques that we use for VM placement are discussed
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