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ORBilu is a project developed by
   Targeting a light-weight and multi-channel approach for distributed stream processing; Theobald, Martin  ; et alin Journal of Parallel and Distributed Computing (2022), 167 Detailed reference viewed: 30 (1 UL) Configurable Cost-Quality Optimization of Cloud-based VoIP; ; et al in Journal of Parallel and Distributed Computing (2019), 133 In this paper, we formulate configurable cloud-based VoIP call allocation problem as a special case of dynamic multi-objective bin-packing. We consider voice quality influenced by CPU stress, cost ... [more ▼] In this paper, we formulate configurable cloud-based VoIP call allocation problem as a special case of dynamic multi-objective bin-packing. We consider voice quality influenced by CPU stress, cost contributed by the number of billing hours for Virtual Machines (VMs) provisioning, and calls placed on hold due to under-provisioning resources. We distinguish call allocation strategies by the type and amount of information used for allocation: knowledge-free, utilization-aware, rental-aware, and loadaware. We propose and study a variety of strategies with static and dynamic policies of VM provisioning. To study realistic scenarios, we consider startup delays for VM provisioning, and three configurable parameters: utilization threshold, rental threshold, and prediction interval. They can be configured and dynamically adapted to cope with different objective preferences, workloads, and cloud properties. We conduct comprehensive simulation on the real workload of the MIXvoip company and show that the proposed strategies outperform ones currently in-use. [less ▲] Detailed reference viewed: 71 (0 UL)A Scalable Parallel Cooperative Coevolutionary PSO Algorithm for Multi-objective OptimizationAtashpendar, Arash ; ; Danoy, Grégoire et alin Journal of Parallel and Distributed Computing (2017) We present a parallel multi-objective cooperative coevolutionary variant of the Speed-constrained Multi-objective Particle Swarm Optimization (SMPSO) algorithm. The algorithm, called CCSMPSO, is the first ... [more ▼] We present a parallel multi-objective cooperative coevolutionary variant of the Speed-constrained Multi-objective Particle Swarm Optimization (SMPSO) algorithm. The algorithm, called CCSMPSO, is the first multi-objective cooperative coevolutionary algorithm based on PSO in the literature. SMPSO adopts a strategy for limiting the velocity of the particles that prevents them from having erratic movements. This characteristic provides the algorithm with a high degree of reliability. In order to demonstrate the effectiveness of CCSMPSO, we compare our work with the original SMPSO and three different state-of-the-art multi-objective CC metaheuristics, namely CCNSGA-II, CCSPEA2 and CCMOCell, along with their original sequential counterparts. Our experiments indicate that our proposed solution, CCSMPSO, offers significant computational speedups, a higher convergence speed and better or comparable results in terms of solution quality, when evaluated against three other CC algorithms and four state-of-the-art optimizers (namely SMPSO, NSGA-II, SPEA2, and MOCell), respectively. We then provide a scalability analysis, which consists of two studies. First, we analyze how the algorithms scale when varying the problem size, i.e., the number of variables. Second, we analyze their scalability in terms of parallelization, i.e., the impact of using more computational cores on the quality of solutions and on the execution time of the algorithms. Three different criteria are used for making the comparisons, namely the quality of the resulting approximation sets, average computational time and the convergence speed to the Pareto front. [less ▲] Detailed reference viewed: 321 (52 UL)Solving very large instances of the scheduling of independent tasks problem on the GPUPinel, Frédéric ; Dorronsoro, Bernabé ; Bouvry, Pascal in Journal of Parallel and Distributed Computing (2013), 73(1), 101-110 Detailed reference viewed: 160 (6 UL) |
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