Intelligent Workload Scheduling in Distributed Computing Environment for Balance between Energy Efficiency and Performance

Abstract

Global digital transformation requires more productive large-scale distributed systems. Such systems should meet lots of requirements, such as high availability, low latency and reliability. However, new challenges become more and more important nowadays. One of them is energy efficiency of large-scale computing systems. Many service providers prefer to use cheap commodity servers in their distributed infrastructure, which makes the problem of energy efficiency even harder because of hardware inhomogeneity. In this chapter an approach to finding balance between performance and energy efficiency requirements within inhomogeneous distributed computing environment is proposed. The main idea of the proposed approach is to use each node’s individual energy consumption models in order to generate distributed system scaling patterns based on the statistical daily workload and then adjust these patterns to match the current workload while using energy-aware Power Consumption and Performance Balance (PCPB) scheduling algorithm. An approach is tested using Matlab modeling. As a result of applying the proposed approach, large-scale distributed computing systems save energy while maintaining a fairly high level of performance and meeting the requirements of the service-level agreement (SLA)