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Cooperative Virtual Machine Placement

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Service-Oriented and Cloud Computing (ESOCC 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14183))

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Abstract

Server virtualisation has played a preponderant role in cloud computing success todate. It controls hardware resource access and management for computing, storage and networking in cloud environments. There have been several approaches for virtual machine placement based on reinforcement learning, bin packing, game theory, multi-objective nonlinear optimisation and other heuristics. This paper proposes a cooperative virtual machine (VM) placement approach based on commitments made in a prior coalition formation phase. Based on these commitments and the availability of resources, we use a heuristic to place new VMs. Using the coalition structure, we narrow the space for candidates during a placement, reducing the computation cost of a VM placement. We evaluated our approach and compared it to existing methods.

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Notes

  1. 1.

    This is due to how the placement and migration algorithms are currently set up.

  2. 2.

    Although these bilateral negotiations run independently, there is a limit to the resources they can commit to sharing.

  3. 3.

    Note that we use a physical clock value for the sake of simplicity in this example. Actually, due to the distributed nature of our algorithm, we use a time limit and a vector clock (logical clock).

  4. 4.

    \( v \) assigns a utility value to a coalition of agents in \(\mathcal {A}\).

  5. 5.

    Although data centres may employ multiple hypervisors, their number is not as high as the number of agents used in a typical coalition formation problem.

  6. 6.

    The three dimensions represent the attributes of a request: cpu, memory and storage.

  7. 7.

    In fact, the non coalition part is processed in parallel to the coalition part and will be cancelled when a coalition member is found.

  8. 8.

    In the case of a migration an additional storage cost should be factored in.

  9. 9.

    This follows from the computational complexity of IDP.

  10. 10.

    https://julialang.org.

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Authors and Affiliations

  1. Namibia University of Science and Technology, Windhoek, Namibia

    José G. Quenum

  2. Université Claude Bernard - Lyon 1, Lyon, France

    Samir Aknine

Authors
  1. José G. Quenum
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  2. Samir Aknine
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Correspondence to José G. Quenum .

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Editors and Affiliations

  1. University of Cyprus, Nicosia, Cyprus

    George A. Papadopoulos

  2. RWTH Aachen University, Aachen, Germany

    Florian Rademacher

  3. University of Pisa, Pisa, Italy

    Jacopo Soldani

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Quenum, J.G., Aknine, S. (2023). Cooperative Virtual Machine Placement. In: Papadopoulos, G.A., Rademacher, F., Soldani, J. (eds) Service-Oriented and Cloud Computing. ESOCC 2023. Lecture Notes in Computer Science, vol 14183. Springer, Cham. https://doi.org/10.1007/978-3-031-46235-1_9

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  • DOI: https://doi.org/10.1007/978-3-031-46235-1_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-46234-4

  • Online ISBN: 978-3-031-46235-1

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