Profile-Based Ant Colony Optimization for Energy-Efficient Virtual Machine Placement

Evidence-Efficient Affinity Propagation Scheme for Virtual Machine Placement in Data Center

“…Its optimization objective is to minimize total energy consumption of running PMs. To obtain the optimal solution, the authors utilized Ant Colony System (ACS) to solve the optimization problem, and compared it with that of FFD algorithm [6] and ACS-based VM placement algorithm [7]. Li z et al [8] considered the optimization of energy consumption with multi-resource constraints for VM placement and proposed differential evolution based method to solve it, named as Discrete-differential-evolution-based VM Placement (DDE-VMP).…”

“…ESV denotes the comprehensive performance of energy consumption, VMMs and service quality. ESV = EC  SLAV (19) where EC represents the energy consumption of data centers on a single day，which is determined by formula (7). A lower value of ESV implies that the presented scheme can save more energy and guarantee QoS.…”

“…In recent years, VM placement has received extensive attention [2][3][4][5][6][7][8][9][10]. Researchers have studied various VM placement models and algorithms to optimize the mapping between VMs and PMs to obtain a higher resource utilization of PMs, better load balance among all running hosts, and lower energy consumption of the data center.…”

“…However, since their solutions can easily fall into local optimization, these algorithms negatively affect QoS and energy utilization of data centers. In [5][6][7][8][9][10], VM placement is modeled as a combinatorial optimization problem, and intelligent heuristic algorithms were proposed used to the model. These algorithms can obtain the global optimization solution, but it involves high time complexity and large time overhead.…”