EAMA: Efficient Adaptive Migration Algorithm for Cloud Data Centers (CDCs)

The rapid demand for Cloud services resulted in the establishment of large-scale Data Centers (CDCs), which ultimately consume a large amount energy. An enormous energy consumption eventually leads to high operating costs and carbon emissions. To reduce with efficient resource utilization, various dynamic Virtual Machine (VM) consolidation approaches (i.e., Predictive Anti-Correlated Placement Algorithm (PACPA), Resource-Utilization-Aware Energy Efficient (RUAEE), Memory-bound Pre-copy Live Migration (MPLM), m Mixed migration strategy, Memory/disk operation aware VM (MLLM), etc.) have been considered. Most these techniques do aggressive that results performance degradation CDCs terms utilization consumption. In this paper, an Adaptive (EAMA) is proposed effective placement VMs on Physical Machines (PMs) dynamically. approach has two distinct features: first, selection PM locations optimum access delay where are required be migrated, second, reduces number migrations. Extensive simulation experiments conducted using CloudSim toolkit. compared PACPA RUAEE algorithms Service-Level Agreement (SLA) violation, hosts shut down, Results show EAMA significantly migrations by 16% 24%, SLA violation 20% 34%, increases 8% 17% increased down from 10% 13% as RUAEE, respectively. Moreover, improvement also observed.