Energy-aware resource allocation heuristics for efficient management of data centers for Cloud computing

By Anton Beloglazov, Jemal H. Abawajy, Rajkumar Buyya. Future generations computer systems, 2012

2,857 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems

View Paper: https://www.semanticscholar.org/paper/04ab0c05680d496b99cd7f56125b2ce32a6584ec

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Citations in the corpus

The Internet of Things, Fog and Cloud Continuum: Integration and Challenges (2018, background): “eration in a variety of mapping and resource allocation techniques [71, 72, 52], with some work focusing on resource management for specific applications or services [73, 74, 75, 76].”
Challenges and Opportunities in Edge Computing (2016, background): “There is a significant body of research that has investigated the energy consumption of cloud data center [14], [15].”
The making of cloud applications: an empirical study on software development for the cloud (2014, background): “, relating to server management [4, 5] or performance measurement [6]).”
Dynamic energy-aware cloudlet-based mobile cloud computing model for green computing (2016, background): “The principle of DECM can enable green computing because the model is designed for mainly reducing energy consumptions, which matches one of the characteristics of green computing as an energyaware feature (Beloglazov et al., 2012; Mukherjee et al., 2014).”
A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems (2016, methodology): “A similar research direction is also reported in [29–31,33].”
Imperfect Information Dynamic Stackelberg Game Based Resource Allocation Using Hidden Markov for Cloud Computing (2018, background): “Virtual resources attracted more attention after virtualization technology matured [6].”
A review of metaheuristic scheduling techniques in cloud computing (2015, methodology): “The algorithm is compared with First Fit (FF), Best Fit (BF) and MBFD [86] algorithm.”
A Survey on Mobile Edge Computing: The Communication Perspective (2017, methodology): “The other model is based on an observation in recent works [89]–[91] that the”
Fog of Everything: Energy-Efficient Networked Computing Architectures, Research Challenges, and a Case Study (2017, background): “, computing servers, network infrastructures, applications, and services), that can be rapidly provisioned and released with minimal management effort or service provider interaction [52], [53], [54].”
Information-Centric Network-Based Vehicular Communications: Overview and Research Opportunities (2018, background): “Because of the hardware and/or software constraints or reformation of security issues, it is indispensable to use data centers for storing records [25,26].”
Using Ant Colony System to Consolidate VMs for Green Cloud Computing (2015, methodology): “In [18], two static thresholds were used to indicate the time of VM presented adaptive thresholds that can be derived based on the statistical analysis of the historical data.”
Data Center Energy Consumption Modeling: A Survey (2016, methodology): “[6][111][112] Widely used linear power model.”