Consolidation of virtual machines using simulated annealing algorithm

Energy-efficient and quality-aware VM consolidation method

“…So far, a large number of studies [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16] have addressed the VM consolidation involved in the different phases, and heuristic algorithms have been implemented for VM consolidation, owing to their outstanding performance in resolving the complex multi-objective optimization model. Several researches [10,11,[13][14][15] have explored this issue, and their performance was relatively benchmark. Telenyk et al [14] aimed to improve QoS in terms of reducing energy consumption and proposed to minimize the imbalance between each resource of the PMs with a simulated annealing algorithm to find the optimal VM placement.…”

“…Several researches [10,11,[13][14][15] have explored this issue, and their performance was relatively benchmark. Telenyk et al [14] aimed to improve QoS in terms of reducing energy consumption and proposed to minimize the imbalance between each resource of the PMs with a simulated annealing algorithm to find the optimal VM placement. However, the presented SA-VMC method did not consider stochastic demands, which could result in new overloading risk for further load imbalance.…”

“…In this section, we compare the proposed EQ-VMC method with four VM consolidation methods, i.e., ACS-VMC [10], SA-VMC [14], EC-VMC [13], COFFGA [11]. The comparison is examined using different data trace on the same indices respectively.…”

“…The VM consolidation problem has been receiving significant attention in recent years. Many methods [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16] have been proposed with addressing several aspects involved in VM consolidation, including host overloading detection, VM selection, VM placement, and under-loaded PMs detection. The methods [1][2][3] determine hotspots by comparing the current resource utilization measurements with the given thresholds.…”

“…Many intelligence-related VM consolidation methods [3,4,15,[17][18][19][20][21][22][23][24][25] also have been developed, but they easily trap in local optimal regime and are difficult to obtain an ideal balance between energy consumption, resource utilization and QoS. Some VM consolidation methods [10][11][12][13][14][15] use heuristic algorithms to search the optimal solution of VM placement for reducing energy consumption but they may suffer easily premature convergence, which leads to sub-optimal solutions.…”