A Taxonomy and Survey of Energy-Efficient Data Centers and Cloud Computing Systems

By Anton Beloglazov, Rajkumar Buyya, Young Choon Lee, Albert Y. Zomaya. Advances in Computing, 2010

821 citations recorded by the source corpus.

Abstract Traditionally, the development of computing systems has been focused on performance improvements driven by the demand of applications from consumer, scientific, and business domains. However, the ever-increasing energy consumption of computing systems has started to limit further performance growth due to overwhelming electricity bills and carbon dioxide footprints. Therefore, the goal of the computer system design has been shifted to power and energy efficiency. To identify open challenges in the area and facilitate future advancements, it is essential to synthesize and classify the research on power- and energy-efficient design conducted to date. In this study, we discuss causes and problems of high power/energy consumption, and present a taxonomy of energy-efficient design of computing systems covering the hardware, operating system, virtualization, and data center levels. We survey various key works in the area and map them onto our taxonomy to guide future design and development efforts. This chapter concludes with a discussion on advancements identified in energy-efficient computing and our vision for future research directions.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering, Information Systems

View Paper: https://www.semanticscholar.org/paper/331f64b7f1c4e50eaf03ca4b0ffff0d47d1fbb90

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Citations in the corpus

QoE and power efficiency tradeoff for fog computing networks with fog node cooperation (2017, background): “It is known that the total amount of power consumed by a node depends on the power usage effectiveness (PUE) as well as the static and dynamic power consumption [11].”
A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems (2016, background): “Thermal-aware management predicts the thermal effects of a task placement and the resource allocation is based on the predicted thermal effect [30,58].”
A Survey on Green-Energy-Aware Power Management for Datacenters (2014, background): “2013], studying the energy-efficient design of computing systems [Beloglazov et al. 2011], and mainly addressing cost-aware power management for geographically distributed datacenters [Rahman et al.”
Greening geographical load balancing (2011, background): “Speci.cally, we explore whether geographical load balancing can encourage use of green renewable energy and reduce use of brown fossil fuel energy.”
Dynamic Right-Sizing for Power-Proportional Data Centers (2011, neutral): “A recent survey is [19].”
Managing Overloaded Hosts for Dynamic Consolidation of Virtual Machines in Cloud Data Centers under Quality of Service Constraints (2013, methodology): “org/ One method to improve the utilization of data center resources, which has been shown to be efficient [3]–[14], is dynamic consolidation of Virtual Machines (VMs).”
Using Ant Colony System to Consolidate VMs for Green Cloud Computing (2015, background): “Dynamic VM consolidation is another effective way to improve the utilization of resources and their energyefficacy [6], [8].”
Network Function Virtualization: State-of-the-Art and Research Challenges (2015, background): “While some progress on energy efficient cloud computing has been made, the fast growing energy needs of data centers continue to receive a lot of attention [144], [145].”
Data Center Energy Consumption Modeling: A Survey (2016, background): “, active) power (Pvar) [75][99][100] which can be expressed as,”
Inter‐Cloud architectures and application brokering: taxonomy and survey (2014, background): “Another issue is the huge amount of data centre power consumption [1, 16].”