Improving the Scalability of Data Center Networks with Traffic-aware Virtual Machine Placement

By Xiaoqiao Meng, Vasileios Pappas, Li Zhang. 2010 Proceedings IEEE INFOCOM, 2010

1,064 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems

View Paper: https://www.semanticscholar.org/paper/9f51b01697729e046632d9575968524eaef00920

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Citations in the corpus

DENS: data center energy-efficient network-aware scheduling (2010, background): “network and traffic characteristics for developing energyefficient data center schedulers [1, 26, 31].”
Towards predictable datacenter networks (2011, background): “Instead, a lot of cloud bound applications are structured in the form of components with more intra-component communication than inter-component communication [16,25].”
Joint VM placement and routing for data center traffic engineering (2012, methodology): “Maximizing the network bisection bandwidth could be viewed as a global optimization problem [6] — servers from all applications must be placed with great care to ensure the sum of their traffic does not saturate any link.”
Consolidating virtual machines with dynamic bandwidth demand in data centers (2011, background): “At the chassis/rack level, the aggregate rate must not exceed the capacity of the end-of-row/top-of-rack switch.”
Cloud computing: state-of-the-art and research challenges (2010, background): “Additionally, dependencies among VMs, such as communication requirements, have also been considered recently [34].”
The Internet of Things, Fog and Cloud Continuum: Integration and Challenges (2018, background): “Perspective (i) is often referred to as a VM placement problem, which is an optimization problem that aims at distributing virtual machines in a data center [51, 52].”
Joint Optimization of Task Scheduling and Image Placement in Fog Computing Supported Software-Defined Embedded System (2016, background): “[20] improve the network scalability by optimizing the traffic-aware VM placement according to the traffic patterns among VMs.”
Knowledge-Defined Networking (2016, background): “The problem of optimal Virtual Machine (VM) placement has been widely studied for Data Center (DC) scenarios (see [15] and the references therein), where the network topology is mostly static.”
Resource Management in Clouds: Survey and Research Challenges (2015, background): “[155] formulate heuristics that seek to minimize the aggregate traffic rates perceived by switches in the data center network by placing VMs with large mutual bandwidth in close proximity to each other.”
Stochastic models of load balancing and scheduling in cloud computing clusters (2012, background): “There are many design issues associated with such systems [7], [8], [9], [10], [11], [12].”