pMapper

pMapper: Power and Migration Cost Aware Application Placement in Virtualized Systems. Workload placement on servers has been traditionally driven by mainly performance objectives. In this work, we investigate the design, implementation, and evaluation of a power-aware application placement controller in the context of an environment with heterogeneous virtualized server clusters. The placement component of the application management middleware takes into account the power and migration costs in addition to the performance benefit while placing the application containers on the physical servers. The contribution of this work is two-fold: first, we present multiple ways to capture the cost-aware application placement problem that may be applied to various settings. For each formulation, we provide details on the kind of information required to solve the problems, the model assumptions, and the practicality of the assumptions on real servers. In the second part of our study, we present the pMapper architecture and placement algorithms to solve one practical formulation of the problem: minimizing power subject to a fixed performance requirement. We present comprehensive theoretical and experimental evidence to establish the efficacy of pMapper.


References in zbMATH (referenced in 9 articles )

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  1. Berndt, Sebastian; Jansen, Klaus; Klein, Kim-Manuel: Fully dynamic bin packing revisited (2020)
  2. Brandt, Felix; Speck, Jochen; Völker, Markus: Constraint-based large neighborhood search for machine reassignment. A solution approach to the ROADEF/EURO challenge 2012 (2016)
  3. Hameed, Abdul; Khoshkbarforoushha, Alireza; Ranjan, Rajiv; Jayaraman, Prem Prakash; Kolodziej, Joanna; Balaji, Pavan; Zeadally, Sherali; Malluhi, Qutaibah Marwan; Tziritas, Nikos; Vishnu, Abhinav; Khan, Samee U.; Zomaya, Albert: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems (2016) ioport
  4. Li, Hongjian; Zhu, Guofeng; Cui, Chengyuan; Tang, Hong; Dou, Yusheng; He, Chen: Energy-efficient migration and consolidation algorithm of virtual machines in data centers for cloud computing (2016)
  5. Velayudhan Kumar, Mohan Raj; Raghunathan, Shriram: Heterogeneity and thermal aware adaptive heuristics for energy efficient consolidation of virtual machines in infrastructure clouds (2016) ioport
  6. Zhou, Xiaobo; Jiang, Chang-Jun: Autonomic performance and power control on virtualized servers: survey, practices, and trends (2014) ioport
  7. Gao, Yongqiang; Guan, Haibing; Qi, Zhengwei; Hou, Yang; Liu, Liang: A multi-objective ant colony system algorithm for virtual machine placement in cloud computing (2013)
  8. Csorba, Máté J.; Meling, Hein; Heegaard, Poul E.: A bio-inspired method for distributed deployment of services (2011) ioport
  9. Verma, Akshat; Ahuja, Puneet; Neogi, Anindya: pMapper: Power and migration cost aware application placement in virtualized systems (2008) ioport