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Energy performance of heuristics and meta-heuristics for real-time joint resource scaling and consolidation in virtualized networked data centers

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In this paper, we explore on a comparative basis the performance suitability of meta-heuristic, sometime denoted as random search algorithms, and greedy-type heuristics for the energy-saving joint dynamic scaling and consolidation of the network-plus-computing resources hosted by networked virtualized data centers when the target is the support of real-time streaming-type applications. For this purpose, the energy and delay performances of Tabu Search (TS), Simulated Annealing (SA) and Evolutionary Strategy (ES) meta-heuristics are tested and compared with the corresponding ones of Best-Fit Decreasing-type heuristics, in order to give insight on the resulting performance-versus-implementation complexity trade-offs. In principle, the considered meta-heuristics and heuristics are general formal approaches that can be applied to large classes of (typically, non-convex and mixed integer) optimization problems. However, specially for the meta-heuristics, a main challenge is to design them to properly address the real-time joint computing-plus-networking resource consolidation and scaling optimization problem. To this purpose, the aim of this paper is: (i) introduce a novel Virtual Machine Allocation (VMA) scheme that aims at choosing a suitable set of possible Virtual Machine placements among the (possibly, non-homogeneous) set of available servers; (ii) propose a new class of random search algorithms (RSAs) denoted as consolidation meta-heuristic, considering the VMA problem in RSAs. In particular, the design of novel variants of meta-heuristics, namely TS-RSC, SA-RSC and ES-RSC, is particularized to the resource scaling and consolidation (RSC) problem; (iii) compare the results of the obtained new RSAs class against some state-of-the-art heuristic approaches. A set of experimental results, both simulated and real-world ones, support the effectiveness of the proposed approaches against the traditional ones.

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This work has been supported by the project: “GAUChO—A Green Adaptive Fog Computing and networking Architectures” funded by the MIUR Progetti di Ricerca di Rilevante Interesse Nazionale (PRIN) Bando 2015—Grant 2015YPXH4W_004, and by the projects: V-Fog and V-Fog2 “Vehicular Fog energy-efficient QoS mining and dissemination of multimedia Big Data streams” funded by Sapienza University of Rome, Bando 2016 and 2017.

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Correspondence to Michele Scarpiniti.

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Scarpiniti, M., Baccarelli, E., Naranjo, P.G.V. et al. Energy performance of heuristics and meta-heuristics for real-time joint resource scaling and consolidation in virtualized networked data centers. J Supercomput 74, 2161–2198 (2018). https://doi.org/10.1007/s11227-018-2244-6

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  • Resource consolidation
  • Energy saving
  • Meta-heuristics optimization
  • Tabu Search
  • Simulated Annealing
  • Genetic Algorithms
  • TCP/IP virtualized data centers
  • Real-time streaming applications