Server Consolidation Algorithms for Cloud Computing

Abstract

In recent years, companies and researchers have hosted and rented computer resources over ‎the ‎‎internet due to cloud computing, which led to an increase in the energy consumed by ‎data centers. This ‎‎consumption is considered one of the world's highest, ‎which pushed many ‎researchers to propose ‎several techniques such as server ‎consolidation (SC) to solve the‎‏ ‏trade‏-‏off‏ ‏‏‎between energy saving and ‎quality of service ‎‎(QoS). SC requires maintaining service level ‎agreements (SLA) violations and ‎minimizing ‎the number of active physical machines (PMs). ‎Furthermore, to achieve this balance and ‎‎avoid ‎increasing hardware costs, the SC challenge targets ‎placing new virtual machines ‎‎(VMs) in ‎suitable PMs. This work explored the existing SC algorithms ‎that include ‎CloudSim as a simulator ‎environment and PlanetLab as a dataset. The authors compared ‎the well-known optimization methods ‎and extracted the weaknesses of the main three deployed ‎‎approaches involved in the consolidation ‎process: bin-packing model, metaheuristics, ‎and machine ‎learning-based solutions.‎