Now showing 1 - 4 of 4
  • Publication
    iVMp: an Interactive VM Placement Algorithm for Agile Capital Allocation
    (Institute of Electrical and Electronic Engineers (IEEE), 2013-06-03) ; ; ; ;
    Server consolidation is an important problem in any enterprise, where capital allocators (CAs) must approve any cost saving plans involving the acquisition or allocation of new assets and the decommissioning of inefficient assets. Our paper describes iVMp an interactive VM placement algorithm, that allows CAs to become 'agile' capital allocators that can interactively propose and update constraints and preferences as placements are recommended by the system. To the best of our knowledge this is the first time that this interactive VM placement recommendation problem has been addressed in the academic literature. Our results show that the proposed algorithm finds near optimal solutions in a highly efficient manner.
  • Publication
    A Fair Comparison of VM Placement Heuristics and a More Effective Solution
    (Institute of Electrical and Electronic Engineers (IEEE), 2014-06-27) ; ; ;
    Data center optimization, mainly through virtual machine (VM) placement, has received considerable attention in the past years. A lot of heuristics have been proposed to give quick and reasonably good solutions to this problem. However it is difficult to compare them as they use different datasets, while the distribution of resources in the datasets has a big impact on the results. In this paper we propose the first benchmark for VM placement heuristics and we define a novel heuristic. Our benchmark is inspired from a real data center and explores different possible demographics of data centers, which makes it suitable when comparing the behaviour of heuristics. Our new algorithm, RBP, outperforms the state-of-the-art heuristics and provides close to optimal results quickly.
      396Scopus© Citations 10
  • Publication
    SOC: Satisfaction-Oriented Virtual Machine Consolidation in Enterprise Data Centers
    Server sprawl is a problem faced by data centers, which causes unnecessary waste of hardware resources, collateral costs of space, power and cooling systems, and administration. This is usually combated by virtualization based consolidation, and both industry and academia have put many efforts into solving the underlying virtual machine (VM) placement problem. However, IT managers’ preferences are seldom considered when making VM placement decisions. This paper proposes a satisfaction-oriented VM consolidation mechanism (SOC) to plan VM consolidation while taking IT managers’ preferences into consideration. In the mechanism, we propose: (1) an XML-based description language to express managers’ preferences and metrics to evaluate the satisfaction degree; (2) to apply matchmaking to locate entities [i.e., VMs and physical machines (PMs)] that best match each other’s preferences; (3) to employ the VM placement algorithm proposed in our previous work to minimize the number of hosts required and the resource wastage on allocated hosts. SOC is compared with two baselines: placement-only and matchmaking-only. The simulation results show that most of the VM-to-PM mappings output from placement-only violate given preferences, while SOC has a satisfaction degree close to matchmaking-only, without requiring too many PMs as matchmaking-only does, but only an amount close to placement-only. In brief, SOC is effective in minimizing the number of hosts required to support a certain set of VMs, while maximizing the satisfaction degree of both managers from the provider and requester side.
      470Scopus© Citations 8
  • Publication
    Scalable Correlation-aware Virtual Machine Consolidation Using Two-phase Clustering
    (Institute of Electrical and Electronic Engineers (IEEE), 2015-07-24) ; ; ;
    Server consolidation is the most common and effective method to save energy and increase resource utilization in data centers, and virtual machine (VM) placement is the usual way of achieving server consolidation. VM placement is however challenging given the scale of IT infrastructures nowadays and the risk of resource contention among co-located VMs after consolidation. Therefore, the correlation among VMs to be co-located need to be considered. However, existing solutions do not address the scalability issue that arises once the number of VMs increases to an order of magnitude that makes it unrealistic to calculate the correlation between each pair of VMs. In this paper, we propose a correlation-aware VM consolidation solution ScalCCon1, which uses a novel two-phase clustering scheme to address the aforementioned scalability problem. We propose and demonstrate the benefits of using the two-phase clustering scheme in comparison to solutions using one-phase clustering (up to 84% reduction of execution time when 17, 446 VMs are considered). Moreover, our solution manages to reduce the number of physical machines (PMs) required, as well as the number of performance violations, compared to existing correlation-based approaches.
      500Scopus© Citations 11