pMapper: Power and Migration Cost Aware Application Placement in Virtualized Systems

By Akshat Verma, Puneet Ahuja, A. Neogi. International Middleware Conference, 2008

1,023 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems

View Paper: https://www.semanticscholar.org/paper/2e7647a07fe21c18ab5b7037de3038157338f1db

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In Computer Networks and Communications

In Information Systems

Citations in the corpus

A survey on techniques for improving the energy efficiency of large-scale distributed systems (2014, background): “Workload consolidation [Chase and Doyle 2001; Chase et al. 2001], investigated in following works [Doyle et al. 2003; Urgaonkar et al. 2008; Verma et al. 2008; Kalyvianaki et al. 2009; Kusic et al. 2008; Jung et al. 2009; Srikantaiah et al. 2008], consists in running multiple tasks on the same…”
Mistral: Dynamically Managing Power, Performance, and Adaptation Cost in Cloud Infrastructures (2010, methodology): “The pMapper system [5] tackles power-cost tradeoffs under a fixed performance constraint by minimizing migration costs while packing VMs in a small number of machines.”
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-aware resource allocation heuristics for efficient management of data centers for Cloud computing (2012, background): “Recent studies [11, 12, 15, 16] have shown that the application of DVFS on the CPU results in almost linear power-to-frequency relationship for a server.”
A Taxonomy and Survey of Energy-Efficient Data Centers and Cloud Computing Systems (2010, background): “[62] have investigated the problem of dynamic placement of applications in virtualized systems, while minimizing the power consumption and maintaining the SLA.”
Adaptive threshold-based approach for energy-efficient consolidation of virtual machines in cloud data centers (2010, background): “Verma et al. [12] have applied a heuristic for the bin packing problem to tackle the dynamic placement of applications in virtualized heterogeneous systems.”
A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems (2016, background): “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].”
Shares and utilities based power consolidation in virtualized server environments (2009, methodology): “[2], [18] describe architectures using collaborative mana gers and arbitrators to handle multiple objectives like power an d performance during VM placement.”
A survey on virtual machine migration and server consolidation frameworks for cloud data centers (2015, methodology): “, 2013) DVFSenabled migration schemes using a prototype while considering a single-core (Verma et al., 2008; Von Laszewski et al., 2009) or multicore (Von Laszewski et al.”
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].”
EnaCloud: An Energy-Saving Application Live Placement Approach for Cloud Computing Environments (2009, methodology): “PMapper [19] proposes a power-aware placement algorithm based a power model and a migration cost model.”
Performance and energy modeling for live migration of virtual machines (2011, methodology): “This is complementary to other issues like minimizing physical resource consumption without compromising service level agreements (SLA) [24, 35], and the issue of when a VM should be migrated and to which host [32, 37].”
Energy Efficient Resource Management in Virtualized Cloud Data Centers (2010, neutral): “Verma et al. [17] have formulated the problem of dynamic placement of applications in virtualized heterogeneous systems as continuous optimization: at each time frame the placement of VMs is optimized to minimize power consumption and maximize performance.”
Energy-Efficient Management of Data Center Resources for Cloud Computing: A Vision, Architectural Elements, and Open Challenges (2010, background): “Another assumption is that due to unknown types of applications running on VMs, it is not possible to build the exact model of such a mixed workload [31].”
Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in Cloud data centers (2012, background): “[16] have formulated the problem of power-aware dynamic placement of applications in virtualized heterogeneous systems as continuous optimization: at each time frame, the placement of VMs is optimized to minimize power consumption and maximize performance.”
Managing Overloaded Hosts for Dynamic Consolidation of Virtual Machines in Cloud Data Centers under Quality of Service Constraints (2013, background): “Index Terms—Distributed systems, cloud computing, virtualization, dynamic consolidation, energy efficiency, host overload detection Ç”