A Novel Self-Adaptive VM Consolidation Strategy Using Dynamic Multi-Thresholds in IaaS Clouds

Abstract

With the rapid development of cloud computing, the demand for infrastructure resources in cloud data centers has further increased, which has already led to enormous amounts of energy costs. Virtual machine (VM) consolidation as one of the important techniques in Infrastructure as a Service clouds (IaaS) can help resolve energy consumption by reducing the number of active physical machines (PMs). However, the necessity of considering energy-efficiency and the obligation of providing high quality of service (QoS) to customers is a trade-off, as aggressive consolidation may lead to performance degradation. Moreover, most of the existing works of threshold-based VM consolidation strategy are mainly focused on single CPU utilization, although the resource request on different VMs are very diverse. This paper proposes a novel self-adaptive VM consolidation strategy based on dynamic multi-thresholds (DMT) for PM selection, which can be dynamically adjusted by considering future utilization on multi-dimensional resources of CPU, RAM and Bandwidth. Besides, the VM selection and placement algorithm of VM consolidation are also improved by utilizing each multi-dimensional parameter in DMT. The experiments show that our proposed strategy has a better performance than other strategies, not only in high QoS but also in less energy consumption. In addition, the advantage of its reduction on the number of active hosts is much more obvious, especially when it is under extreme workloads.

PAPSO: A Power-Aware VM Placement Technique Based on Particle Swarm Optimization

“…In contrast, other techniques do not rely on thresholds to identify overloaded or underloaded hosts such as [30], [31]. Although several kinds of research take CPU utilization of servers as the only parameter in their algorithms, some studies take additional resources into their consideration such as memory, bandwidth, and disk [30], [32]- [35].…”

“…After placing the first VM, PM with the least available computing resources will be selected as a destination host for the second VM and so on until completing the placement of all VMs. Modified Multi-weights Best fit (MW-BF) [35] has been applied based on a modification of the BF algorithm, where they consider multi-weights for VMs which need a placement.…”

“…The previous process is repeated to determine the best fitness function value among the swarm and the position that leads to this best value (lines [25][26][27][28]. The next step is to update particle positions (lines [29][30][31][32][33][34][35]. The inertia weight is calculated which relies on the current iteration number (line 30).…”

“…The inertia weight is calculated which relies on the current iteration number (line 30). A new speed and thus a new position are calculated for each particle using the particle swarm optimization equations (lines [34][35]. The iteration number is incremented before moving to the next iteration (line 36).…”

An adaptive threshold policy for host overload detection in cloud data centre

“…However, the scheme doesn't perform well in terms of QoS and leads to higher SLAVs. Xie et al [28] has proposed a linear regression-based predictive scheme that considers multiple resources for the computation of the upper threshold. The problem with the issues depending on linear regression is that linear regression looks only at the mean of the dependent variable.…”