An approach to reduce energy consumption and performance losses on heterogeneous servers using power capping

Abstract

Rapid growth of demand for remote computational power, along with high energy costs and infrastructure limits, has led to treating power usage as a primary constraint in data centers. Especially, recent challenges related to development of exascale systems or autonomous edge systems require tools that will limit power usage and energy consumption. This paper presents a power capping method that allows operators to quickly adjust the power usage to external conditions and, at the same time, to reduce energy consumption and negative impact on performance of applications. We propose an optimization model and both heuristic and exact methods to solve this problem. We present an evaluation of power capping approaches supported by results of application benchmarks and experiments performed on new heterogeneous servers.

Intelligent task processing using mobile edge computing: processing time optimization

“…In our paper, knowing that mobile devices have limited computing resources, the decision mechanism is based on setting a limited processing time in the mobile device in order to deliver an output result within a reasonable response time, as well as balancing the usage of these resources by setting a CPU usage threshold. The CPU usage in a utilization-based approach affects directly the consumed power [32], and it is considered that energy consumption and CPU utilization increase linearly [27], [33], [34].…”

Framework for the Development and Implementation of Sustainable Information Systems for the Digitalization of Small Businesses in South Africa

“…To fully account for the environmental impact of these resources the materials, manufacture, and transportation of the servers themselves should also be considered. Large, centralized data centers can offset the emissions of manufacturing by distributing the required compute over many servers and using algorithmic control of power supplies, minimizing idle time, and maximizing resource utilization [24,25]. For many small businesses, the capital involved in this is prohibitive.…”

Assessing Power Needs to Run a Workload with Quality of Service on Green Datacenters

“…The applications are composed of a front-end and a back-end, and the front-end produces the data consumed by the back-end. A similar method is also proposed for heterogeneous nodes of a datacenter [7]. In [25], different online algorithms are introduced to minimize the performance degradation and the total energy consumed: LmsReg, based on regression, which detects overloaded servers and migrates virtual machines, and MuP, which addresses the trade-off between power consumption, number of migrations, server performance and the total number of servers that have been switched-off in the selection of virtual machines.…”