Energy Consumption and Quality of Service Optimization in Containerized Cloud Computing

Lightweight virtualization technology has emerged as an alternative to traditional hypervisor-based virtualization. Containers based on an operating system level virtualization have shown superior performance and more flexibility than virtual machines. Both factors encourage their fast adoption and wide use in cloud environments. Container technology guarantees efficient interaction with hardware through system calls and isolation of tasks execution. The portability and ease of deployment enable containers to execute a variety of jobs. Several techniques have been used to address the problem of job allocation on containers. In this paper, a set of allocation strategies is proposed to distribute the workload to multiple OS containers that are running on several servers. We address two objective scheduling problem: minimizing energy and a number of SLA violations. We propose seventy-two strategies and analyze their performance by comprehensive simulation. We demonstrate that proposed techniques outperform previous works in terms of both the energy savings and QoS.