Now showing 1 - 8 of 8
  • Publication
    Towards a Multi-Objective VM Reassignment for Large Decentralised Data Centres
    Optimising the IT infrastructure of large, often geographically distributed, organisations goes beyond the classical virtual machine reassignment problem, for two reasons: (i) the data centres of these organisations are composed of a number of hosting departments which have different preferences on what to host and where to host it; (ii) the top-level managers in these data centres make complex decisions and need to manipulate possible solutions favouring different objectives to find the right balance. This challenge has not yet been comprehensively addressed in the literature and in this paper we demonstrate that a multi-objective VM reassignment is feasible for large decentralised data centres. We show on a realistic data set that our solution outperforms other classical multi-objective algorithms for VM reassignment in terms of quantity of solutions (by about 15% on average) and quality of the solutions set (by over 6% on average). 
    Scopus© Citations 10  585
  • Publication
    Towards the Automatic Detection of Efficient Computing Assets in a Heterogeneous Cloud Environment
    (Institute of Electrical and Electronic Engineers (IEEE), 2013-06-03) ; ; ; ;
    In a heterogeneous cloud environment, the manual grading of computing assets is the first step in the process of configuring IT infrastructures to ensure optimal utilization of resources. Grading the efficiency of computing assets is however, a difficult, subjective and time consuming manual task. Thus, an automatic efficiency grading algorithm is highly desirable. In this paper, we compare the effectiveness of the different criteria used in the manual grading task for automatically determining the efficiency grading of a computing asset. We report results on a dataset of 1,200 assets from two different data centers in IBM Toronto. Our preliminary results show that electrical costs (associated with power and cooling) appear to be even more informative than hardware and age based criteria as a means of determining the efficiency grade of an asset. Our analysis also indicates that the effectiveness of the various efficiency criteria is dependent on the asset demographic of the data centre under consideration.
      343
  • Publication
    Exact and Hybrid Solutions for the Multi-objective VM Reassignment Problem
    Machine Reassignment is a challenging problem for constraint programming (CP) and mixed integer linear programming (MILP) approaches, especially given the size of data centres. Hybrid solutions mixing CP and heuristic algorithms, such as, large neighbourhood search (CBLNS), also struggle to address the problem given its size and number of constraints. The multi-objective version of the Machine Reassignment Problem is even more challenging and it seems unlikely for CP, MILP or hybrid solutions to obtain good results in this context. As a result, the first approaches to address this problem have been based on other optimisation methods, including metaheuristics. In this paper we study three things: (i) under which conditions a mixed integer optimisation solver, such as IBM ILOG CPLEX, can be used for the Multi-objective Machine Reassignment Problem; (ii) how much of the search space can a well-known hybrid method such as CBLNS explore; and (iii) can we find a better hybrid approach combining MILP or CBLNS and another recent metaheuristic proposed for the problem (GeNePi). We show that MILP can handle only small or medium scale data centres, and with some relaxations, such as, an optimality tolerance gap and a limited number of directions explored in the search space. CBLNS on the other hand struggles with the problem in general but achieves reasonable performance for large instances of the problem. However, we show that our hybridisation improves both the quality of the set of solutions (CPLEX+GeNePi and CBLNS+GeNePi improve the solutions by +17.8% against CPLEX alone and +615% against CBLNS alone) and number of solutions (8.9 times more solutions than CPLEX alone and 56.76 times more solutions than CBLNS alone), while the processing time of CPLEX+GeNePi and CBLNS+GeNePi increases only by 6% and 16.4% respectively. Overall, the study shows that CPLEX+GeNePi is the best algorithm for small instances (CBLNS+GeNePi only gets 45.2% of CPLEX+GeNePi’s hypervolume) while CBLNS+GeNePi is better than the others on large instances (that CPLEX+GeNePi cannot address).
    Scopus© Citations 13  573
  • Publication
    VM reassignment in hybrid clouds for large decentralised companies: A multi-objective challenge
    Optimising the data centres of large IT organisations is complex as (i) they are composed of various hosting departments with their own preferences and (ii) reassignment solutions can be evaluated from various independent dimensions. But in reality, the problem is even more challenging as companies can now choose from a pool of cloud services to host some of their workloads. This hybrid search space seems intractable, as each workload placement decision (seen as running in a virtual machine on a server) is required to answer many questions: can we host it internally? In which hosting department? Are the capital allocators of this hosting department ok with this placement? How much does it save us and is it safe? Is there a better option in the Cloud? Etc. In this paper, we define the multi-objective VM reassignment problem for hybrid and decentralised data centres. We also propose H2¿D2, a solution that uses a multi-layer architecture and a metaheuristic algorithm to suggest reassignment solutions that are evaluated by the various hosting departments (according to their preferences). We compare H2¿D2 against state-of-the-art multi-objective algorithms and find that H2¿D2 outperforms them both in terms of quantity (approx 30% more than the second-best algorithm on average) and quality of solutions (19% better than the second-best on average).
      555Scopus© Citations 31
  • 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.
    Scopus© Citations 11  473
  • 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.
      412Scopus© Citations 6
  • 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.
    Scopus© Citations 8  549
  • Publication
    MILP for the Multi-objective VM Reassignment Problem
    Machine Reassignment is a challenging problem for constraint programming (CP) and mixed integer linear pro- gramming (MILP) approaches, especially given the size of data centres. The multi-objective version of the Machine Reassignment Problem is even more challenging and it seems unlikely for CP or MILP to obtain good results in this context. As a result, the first approaches to address this problem have been based on other optimisation methods, including metaheuristics. In this paper we study under which conditions a mixed integer optimisation solver, such as IBM ILOG CPLEX, can be used for the Multi-objective Machine Reassignment Problem. We show that it is useful only for small or medium scale data centres and with some relaxations, such as an optimality tolerance gap and a limited number of directions explored in the search space. Building on this study, we also investigate a hybrid approach, feeding a metaheuristic with the results of CPLEX, and we show that the gains are important in terms of quality of the set of Pareto solutions (+126.9% against the metaheuristic alone and +17.8% against CPLEX alone) and number of solutions (8.9 times more than CPLEX), while the processing time increases only by 6% in comparison to CPLEX for execution times larger than 100 seconds.
    Scopus© Citations 13  633