Real-time Virtual Machine Energy-Efficient Allocation in Cloud Data Centers Using Interval-packing Methods

Abstract

The reduction of power consumption, which can lower the operation costs of Cloud providers, lengthen the useful life of a machine, as well as lessen the environmental effect caused by power consumption, is one of the critical concerns for large-scale Cloud applications. To satisfy the needs of various clients, Virtual Machines (VMs) as resources (Infrastructure as a Service (IaaS)) can be dynamically allocated in cloud data centers. In this research, we study the energy-efficient scheduling of real-time VMs by taking set processing intervals into account, with the providers' goal of lowering power consumption. Finding the best solutions is an NP-complete problem when virtual machines (VMs) share arbitrary amounts of a physical machine's (PM) total capacity, as demonstrated in numerous open-source resources. Our strategy treats the issue as a modified interval partitioning problem and takes into account configurations with dividable capacities to make the problem formulation easier and assist save energy. There are presented both exact and approximate solutions. The proposed systems consume 8–30% less power than the existing algorithms, according to simulation data.