Dynamic Placement of Virtual Machines for Managing SLA Violations

By Norman Bobroff, Andrzej Kochut, Kirk A. Beaty. 2007 10th IFIP/IEEE International Symposium on Integrated Network Management, 2007

944 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems

View Paper: https://www.semanticscholar.org/paper/b70283748f5edb07efed762f1645481e7b0f88ca

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Citations in the corpus

Optimal virtual machine placement across multiple cloud providers (2009, background): “As virtualization is a core of cloud computing, the problem of VM placement becomes crucial [13]–[16].”
Resource management for Infrastructure as a Service (IaaS) in cloud computing: A survey (2014, positive): “In Bobro et al. (2010), a dynamic server migration and consolidation algorithm is introduced.”
Server-storage virtualization: Integration and load balancing in data centers (2008, background): “[24], [25] describe other resource management and consolidation techniques for servers using virtualization.”
Workload characterization and prediction in the cloud: A multiple time series approach (2012, methodology): “[15] used regression models to forecast workload variations, in order to dynamically place virtual machines.”
Optimization of Resource Provisioning Cost in Cloud Computing (2012, background): “For example, the reservation plan offered by Amazon EC2 can reduce the total provisioning cost up to 49 percent when the reserved resource is fully utilized (i.e, steady-state usage) [4].”
pMapper: Power and Migration Cost Aware Application Placement in Virtualized Systems (2008, background): “[2] describe a runtime application placement and migration algorithm in a virtualized environment.”
Consolidating virtual machines with dynamic bandwidth demand in data centers (2011, background): “In the research literature, most work [1][18][26][28][34] formulate VM consolidation as an optimization problem with the number of required servers as the objective.”
Resource Management in Clouds: Survey and Research Challenges (2015, background): “[31] propose a first-fit heuristic that dynamically remaps VMs to PMs in a manner that minimizes the number of PMs required to support a workload at a specified allowable rate of SLA violations; they present an extensive evaluation of the approach based on production traces.”
Autonomic multi-agent management of power and performance in data centers (2008, methodology): “Methods for exploiting these controls at the server level are being implemented in firmware [12] and in operating systems (e.g. the Linux On-Demand Governor).”
Shares and utilities based power consolidation in virtualized server environments (2009, methodology): “In the researc h community as well, there are many proposed techniques [2], [3], [4] that analyze utilizations of physical servers and o utput a consolidation plan describing which combinations of phys ical servers can be virtualized into Virtual Machines (VMs) andplaced on each virtualized server.”
Resource Central: Understanding and Predicting Workloads for Improved Resource Management in Large Cloud Platforms (2017, methodology): “In fact, computing an optimal solution for such a bin-packing problem is infeasible, so prior works have used heuristics [2, 3, 24].”
Dynamic Resource Allocation Using Virtual Machines for Cloud Computing Environment (2013, methodology): “For green computing, should keep th e utilization of PMs reasonably high to make efficien cy in energy [7].”
Efficient resource provisioning in compute clouds via VM multiplexing (2010, background): “It is well known that the applications enclosed by VMs - and thus the VMs themselves - exhibit timevarying resource demand patterns with bursts of high demand periods, intermixed with low-utilization regions [3, 23, 26].”
Entropy: a consolidation manager for clusters (2009, methodology): “31 nodes per hour, consolidation based on the previously-used First Fit Decreasing (FFD) heuristic [3, 17, 18] uses 15.”
Managing Overloaded Hosts for Dynamic Consolidation of Virtual Machines in Cloud Data Centers under Quality of Service Constraints (2013, background): “Therefore, tm is the solution of the optimization problem (6)-(7), and Algorithm 1 is an OPT for the problem of host overload detection. tu”
Live Video Analytics at Scale with Approximation and Delay-Tolerance (2017, background): “Autonomic computing [24, 26,29,66,70,77] allocate resources to VMs and web applications to maximize their quality of service.”
AutoScale: Dynamic, Robust Capacity Management for Multi-Tier Data Centers (2012, background): “…2005; Horvath and Skadron 2008; Krioukov et al. 2010; Qin and Wang 2007] that aim to predict the future request rate, and mixed reactivepredictive approaches [Bobroff et al. 2007; Chen et al. 2005, 2008; Gandhi et al. 2011a; Gmach et al. 2008; Urgaonkar and Chandra 2005; Urgaonkar et al. 2005].”
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.”
Server Workload Analysis for Power Minimization using Consolidation (2009, background): “This paper does not deal with the problem of translating an application SLA to a resource value and assumes thatC(Ai, t) are available from monitored resource data.”