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

Abstract

With the widespread usage of cloud computing to benefit from its services, cloud service providers have invested in constructing large scale data centers. Consequently, a tremendous increase in energy consumption has arisen in conjunction with its results, including a remarkable rise in costs of operating and cooling servers. Besides, increasing energy consumption has a significant impact on the environment due to emissions of carbon dioxide. Dynamic consolidation of Virtual Machines (VMs) into the minimal number of Physical Machines (PMs) is considered as one of the magic solutions to manage power consumption. The virtual machine placement problem is a critical issue for good VM consolidation. This paper proposes a Power-Aware technique depending on Particle Swarm Optimization (PAPSO) to determine the near-optimal placement for the migrated VMs. A discrete version of Particle Swarm Optimization (PSO) is adopted based on a decimal encoding to map the migrated VMs to the best appropriate PMs. Furthermore, an effective minimization fitness function is employed to reduce power consumption without violating the Service Level Agreement (SLA). Specifically, PAPSO consolidates the migrated VMs into the minimum number of PMs with a major constraint to decrease the number of overloaded hosts as much as possible. Therefore, the number of VM migrations can be reduced drastically by taking into consideration the main sources for VM migrations; overloaded hosts and underloaded ones. PAPSO is implemented in CloudSim and the experimental results on random workloads with different sizes of VMs and PMs show that PAPSO does not violate SLA and outperforms the Power-Aware Best Fit Decreasing algorithm (PABFD). It can reduce about 8.01%, 39.65%, 66.33%, and 11.87% on average in terms of consumed energy, number of VM migrations, number of host shutdowns and the combined metric Energy SLA Violation (ESV), respectively.

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

“…Experiments have verified that this model can adapt to different datacenter loads and significantly reduce datacenter energy consumption and SLA violation rates. Ibrahim et al [21] used the particle swarm algorithm to solve the problem of VM consolidation. The particle swarm algorithm can overcome continuity problems, so discretization is needed when using the algorithm to solve the box-packing problem.…”

“…Discretization can lead to an insufficient number of particles in the population, and the algorithm may fall into a local optimal solution. Compared with [21], this paper adds a mutation subalgorithm to the algorithm, which mitigates the particle abundance issue. In addition, we introduce a local optimization algorithm to improve the particle movement direction.…”

“…Particle swarm optimization is a biology-based metaheuristic evolutionary computational method. The algorithm is derived from studies of bird predation behavior, and the basic objective is to find the optimal solution through collaboration and information sharing among individuals in a group [21]. Particle swarm optimization simulates the birds in a swarm as massless particles.…”

“…Traditional consolidation algorithms [6], [15] are often based on greedy models, and the algorithm complexity is low; however, it is difficult to obtain the global optimal solution with these models. The particle swarm algorithm is a metaheuristic algorithm that can search for a solution that meets the requirements in the global scope and optimizes some aspects of the placement strategy approach [21]. The particle swarm algorithm is usually used to solve continuous problems, so it needs to be coded to solve discrete problems.…”

Breast Cancer Segmentation From Thermal Images Based on Chaotic Salp Swarm Algorithm

“…Metaheuristics can often find solutions with less computational effort than simple heuristics or iterative methods when searching for a large set of possible solutions. Those algorithms include Particle Swarm Optimization (PSO) [19], Artificial Bee Colony (ABC) [20], Ant Colony Algorithm (ACO) [21], Harris Hawks Optimization (HHO) [22], Whale optimization algorithm (WOA) [23], Grey Wolf Optimization (GWO) [18], [24], Moth-flame optimization (MFO) [25], Slime Mould Algorithm (SMA) [26], Bacterial Foraging Optimization (BFO) [27], and Slap Swarm Algorithm (SSA) [28]- [31]. Several comparative studies have investigated various metaheuristic techniques to compare their accuracy and effectiveness [32], [33].…”