Robust optimization for energy-efficient virtual machine consolidation in modern datacenters

Abstract

Energy efficient virtual machine (VM) consolidation in modern data centers is typically optimized using methods such as Mixed Integer Programming, which typically require precise input to the model. Unfortunately, many parameters are uncertain or very difficult to predict precisely in the real world. As a consequence, a once calculated solution may be highly infeasible in practice. In this paper, we use methods from robust optimization theory in order to quantify the impact of uncertainty in modern data centers. We study the impact of different parameter uncertainties on the energy efficiency and overbooking ratios such as e.g. VM resource demands, migration related overhead or the power consumption model of the servers used. We also show that setting aside additional resource to cope with uncertainty of workload influences the overbooking ration of the servers and the energy consumption. We show that, by using our model, Cloud operators can calculate a more robust migration schedule leading to higher total energy consumption. A more risky operator may well choose a more opportunistic schedule leading to lower energy consumption but also higher risk of SLA violation.

SLA-Aware and Energy-Efficient Virtual Machine Placement and Consolidation in Heterogeneous DVFS Enabled Cloud Datacenter

“…In [48], the authors present an algorithm as an ILP problem for reducing energy consumption along with optimizing SLAv and performance. A similar method is proposed in [49] where MILP algorithm is employed for reducing energy consumption, SLAv and the number of migrations with a more efficient use of CPU resources. As shown in Table 2, significant research is conducted to address these metrics and offer various solutions.…”

An efficient host overload detection algorithm for cloud data center based on exponential weighted moving average

“…Abdel‐Basset et al 27 used meta‐heuristic–based whale optimization algorithm for VM placement and compared the results with other meta‐heuristic algorithms. Nasim et al 28 implemented robust optimization techniques in DC for efficient utilization of resources, improved VM migration, and emphasized the trade‐off between energy consumption and SLA violation. Abadi et al 29 proposed a queuing theory‐based adaptive VM consolidation algorithm for VM placement, host overload, and under‐load detection.…”