An energy, performance efficient resource consolidation scheme for heterogeneous cloud datacenters

A Dynamic Virtual Machine Resource Consolidation Strategy Based on a Gray Model and Improved Discrete Particle Swarm Optimization

“…The energy consumption associated with VM migration mainly includes the energy consumption of the host and network bandwidth during migration [41], and the model can be expressed as:…”

Optimization of energy consumption in cloud computing datacenters

“…The cloud datacentre infrastructure contains three resource types: compute, storage, and network resources. Our aim in the work presented here is to focus on the computing component of the resources used since it is shown to consume about 26% of the total energy of a datacentre [17]. The main computing units are the servers.…”

“…They also proposed a processor-level migration algorithm to reschedule remaining tasks among processors on an individual server and dynamically balance the workloads and lower the total ECR on a given server. In [17], researchers propose a consolidation algorithm which favors the most effective migration among Virtual Machines, containers and applications; and investigate how migration decisions should be made to save energy without any negative impact on the service performance.…”

An Energy-Aware Host Resource Management Framework for Two-Tier Virtualized Cloud Data Centers

“…For dynamic host resource management, Khan et al studies it from the perspective of application workload migration. In [24], they model the heterogeneity of cloud applications and resources, and propose dynamic integration strategies that select the most efficient migration among virtual machines, containers, or specific applications running within containers to save energy without affecting service performance. In addition, in their latest research [25], a resource coordinator for hybrid heterogeneous cloud computing environments is proposed, which can optimize the resource usage of hosts by properly placing and migrating workloads.…”

“…When determining whether a host is underloaded, while considering its ℎ , it should also take its ℎ as a reference factor, because for the host with low ℎ and ℎ , its load is not only low at present, but also not likely to increase in a short time, so it should be shut down to save energy consumption. Based on this consideration, a host sorting index ℎ is given as (24), for host ℎ ,…”