Energy aware consolidation for cloud computing

By Shekhar Srikantaiah, Aman Kansal, Feng Zhao. IEEE International Conference on Cluster Computing, 2008

927 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/3e19046c665867bbe557685da60738a40738010a

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Citations in the corpus

Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in Cloud data centers (2012, methodology): “Like in [14], the authors have applied a heuristic for the bin packing problem with variable bin sizes and costs.”
Resource Management in Clouds: Survey and Research Challenges (2015, background): “bin packing problem, as for example in [23, 140, 204].”
Energy-aware resource allocation heuristics for efficient management of data centers for Cloud computing (2012, methodology): “Like in [13], the authors have applied a heuristic for the bin packing problem with variable bin sizes and costs.”
A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems (2016, methodology): “However, before shutting down a server, all the running services have to be consolidated and migrated to other nodes power and migration cost aware application placement algorithms in virtualized systems [15,63,64].”
On Global Electricity Usage of Communication Technology: Trends to 2030 (2015, background): “(i) cloud computing promises efficiency of scale, both in terms of capital and operational costs [9–12]; (ii) high-speed wireless access networks promise near-ubiquitous network access [13–15]; and (iii) thin-client solutions, such as smart-phones and tablet devices [16].”
Managing Overloaded Hosts for Dynamic Consolidation of Virtual Machines in Cloud Data Centers under Quality of Service Constraints (2013, background): “According to this definition, the control policy can be described by a vector of the probabilities of transitions from any nonabsorbing state to the absorbing state ðN þ 1Þ, i.e., the probabilities of VM migrations, which we denote mi, where i 2 S.”
Energy Efficient Allocation of Virtual Machines in Cloud Data Centers (2010, unknown): “The objective of the reallocation is to minimize the number of physical nodes serving current workload, whereas idle nodes are switched off in order to decrease power consumption.”
A multi-objective ant colony system algorithm for virtual machine placement in cloud computing (2013, background): “Various heuristics [20–24] have been proposed for this problem.”
Energy Efficient Resource Management in Virtualized Cloud Data Centers (2010, unknown): “Srikantaiah et al. [16] have studied the problem of requests scheduling for multi-tiered web-applications in virtualized heterogeneous systems in order to minimize energy consumption, while meeting performance requirements.”
Cloud computing: state-of-the-art and research challenges (2010, background): “Energy-aware job scheduling [50] and server consolidation [46] are two other ways to reduce power consumption by turning off unused machines.”
A survey on techniques for improving the energy efficiency of large-scale distributed systems (2014, background): “Even if virtualization adds a software layer that consumes energy [Torres et al. 2008], it allows for ﬁner load consolidation on a virtualized node [Srikantaiah et al. 2008] and provides live migration techniques [Travostino et al. 2006] to strengthen load aggregation.”
Energy-Aware Ant Colony Based Workload Placement in Clouds (2011, unknown): “placement approaches, which resort to the adaptation of simple greedy algorithms can be found in [22] and [20].”