References of "nesmachnow, sergio"
     in
Bookmark and Share    
Full Text
Peer Reviewed
See detailSingle and Multiobjective Evolutionary Algorithms for Clustering Biomedical Information with Unknown Number of Clusters
Curi, María Eugenia; Carozzi, Lucía; Massobrio, Renzo et al

in Bioinspired Optimization Methods and Their Applications (2018)

This article presents single and multiobjective evolutionary approaches for solving the clustering problem with unknown number of clusters. Simple and ad-hoc operators are proposed, aiming to keep the ... [more ▼]

This article presents single and multiobjective evolutionary approaches for solving the clustering problem with unknown number of clusters. Simple and ad-hoc operators are proposed, aiming to keep the evolutionary search as simple as possible in order to scale up for solving large instances. The experimental evaluation is performed considering a set of real problem instances, including a real-life problem of analyzing biomedical information in the Parkinson's disease map project. The main results demonstrate that the proposed evolutionary approaches are able to compute accurate trade-off solutions and efficiently handle the problem instance involving biomedical information. [less ▲]

Detailed reference viewed: 132 (13 UL)
Full Text
Peer Reviewed
See detailOnline Bi-Objective Scheduling for IaaS Clouds Ensuring Quality of Service
Tchernykh, Andrei; Lozano, Luz; Schwiegelshohn, Uwe et al

in Journal of Grid Computing (2016), 14

This paper focuses on a bi-objective experimental evaluation of online scheduling in the Infrastructure as a Service model of Cloud computing regarding income and power consumption objectives. In this ... [more ▼]

This paper focuses on a bi-objective experimental evaluation of online scheduling in the Infrastructure as a Service model of Cloud computing regarding income and power consumption objectives. In this model, customers have the choice between different service levels. Each service level is associated with a price per unit of job execution time, and a slack factor that determines the maximal time span to deliver the requested amount of computing resources. The system, via the scheduling algorithms, is responsible to guarantee the corresponding quality of service for all accepted jobs. Since we do not consider any optimistic scheduling approach, a job cannot be accepted if its service guarantee will not be observed assuming that all accepted jobs receive the requested resources. In this article, we analyze several scheduling algorithms with different cloud configurations and workloads, considering the maximization of the provider income and minimization of the total power consumption of a schedule. We distinguish algorithms depending on the type and amount of information they require: knowledge free, energy-aware, and speed-aware. First, to provide effective guidance in choosing a good strategy, we present a joint analysis of two conflicting goals based on the degradation in performance. The study addresses the behavior of each strategy under each metric. We assess the performance of different scheduling algorithms by determining a set of nondominated solutions that approximate the Pareto optimal set. We use a set coverage metric to compare the scheduling algorithms in terms of Pareto dominance. [less ▲]

Detailed reference viewed: 44 (4 UL)
See detailForeword
Danoy, Grégoire UL; Jourdan, Laetitia; Talbi, El-Ghazali et al

in International (2016)

Detailed reference viewed: 16 (2 UL)
Full Text
Peer Reviewed
See detailMetaheuristics for the Virtual Machine Mapping Problem in Clouds
Nesmachnow, Sergio; Dorronsoro, Bernabé UL; Talbi, El-Ghazali et al

in Informatica, Lith. Acad. Sci. (2015), 26(1), 111-134

This article presents sequential and parallel metaheuristics to solve the virtual machines subletting problem in cloud systems, which deals with allocating virtual machine requests into prebooked ... [more ▼]

This article presents sequential and parallel metaheuristics to solve the virtual machines subletting problem in cloud systems, which deals with allocating virtual machine requests into prebooked resources from a cloud broker, maximizing the broker profit. Three metaheuristic are studied: Simulated Annealing, Genetic Algorithm, and hybrid Evolutionary Algorithm. The experimental evaluation over instances accounting for workloads and scenarios using real data from cloud providers, indicates that the parallel hybrid Evolutionary Algorithm is the best method to solve the problem, computing solutions with up to 368.9% profit improvement over greedy heuristics results while accounting for accurate makespan and flowtime values. [less ▲]

Detailed reference viewed: 72 (0 UL)
Full Text
Peer Reviewed
See detailVoIP Service Model for Multi-objective Scheduling in Cloud Infrastructure
Cortés-Mendoza, Jorge M.; Tchernykh; Simionovici, Ana-Maria UL et al

in International Journal of Metaheuristics (2015), 4(2), 185-203

Voice over IP (VoIP) is very fast growing technology for the delivery of voice communications and multimedia data over internet with lower cost. Early technical solutions mirrored the architecture of the ... [more ▼]

Voice over IP (VoIP) is very fast growing technology for the delivery of voice communications and multimedia data over internet with lower cost. Early technical solutions mirrored the architecture of the legacy telephone network. Now, they have adopted the concept of distributed cloud VoIP. These solutions typically allow dynamic interconnection between users on any domains. However, providers face challenges to use infrastructure in the best efficient and cost-effective ways. Hence, efficient scheduling and load balancing algorithms are a fundamental part of this approach, especially in presence of the uncertainty of a very dynamic and unpredictable environment. In this paper, we formulate the problem of dynamic scheduling of VoIP services in distributed cloud environments and propose a model for bi-objective optimisation. We consider it as the special case of the bin packing problem, and discuss solutions for provider cost optimisation while ensuring quality of service. [less ▲]

Detailed reference viewed: 105 (16 UL)
Full Text
Peer Reviewed
See detailEnergy Efficient Scheduling in Heterogeneous Systems with a Parallel Multiobjective Local Search
Iturriaga, Santiago; Nesmachnow, Sergio; Dorronsoro, Bernabe et al

in Computing and Informatics (2013), 32(2), 273-294

This article introduces ME-MLS, an e cient multithreading local search algorithm for solving the multiobjective scheduling problem in heterogeneous com- puting systems. We consider the minimization of ... [more ▼]

This article introduces ME-MLS, an e cient multithreading local search algorithm for solving the multiobjective scheduling problem in heterogeneous com- puting systems. We consider the minimization of both the makespan and energy consumption objectives. The proposed method follows a fully multiobjective ap- proach, applying a Pareto-based dominance search that is executed in parallel by using several threads. The experimental analysis demonstrates that the new multi- threading algorithm outperforms a set of fast and accurate two-phases deterministic heuristics based on the traditional MinMin. The new ME-MLS method is able to achieve signi cant improvements in both makespan and energy consumption objec- tives in reduced execution times for a large set of testbed instances, while exhibiting a near linear speedup behavior when using up to 24 threads. [less ▲]

Detailed reference viewed: 33 (1 UL)
Full Text
Peer Reviewed
See detailA parallel hybrid evolutionary algorithm for the optimization of broker virtual machines subletting in cloud systems
iturriaga, Santiago; Nesmachnow, Sergio; Dorronsoro, Bernabe et al

in International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (2013)

This article presents a new parallel hybrid evolutionary algorithm to solve the problem of virtual machines subletting in cloud systems. The problem deals with the efficient allocation of a set of virtual ... [more ▼]

This article presents a new parallel hybrid evolutionary algorithm to solve the problem of virtual machines subletting in cloud systems. The problem deals with the efficient allocation of a set of virtual machine requests from customers into available pre-booked resources from a cloud broker, in order to maximize the broker profit. The proposed parallel algorithm uses a distributed subpopulations model, and a Simulated Annealing operator. The experimental evaluation analyzes the profit and makespan results of the proposed methods over a set of problem instances that account for realistic workloads and scenarios using real data from cloud providers. A comparison with greedy heuristics indicates that the proposed method is able to compute solutions with up to 133.8% improvement in the profit values, while accounting for accurate makespan results. [less ▲]

Detailed reference viewed: 75 (0 UL)
Full Text
Peer Reviewed
See detailList scheduling heuristics for virtual machine mapping in cloud systems
nesmachnow, sergio; iturriaga, santiago; dorronsoro, bernabe et al

in VI Latin American Symposium on High Performance Computing (HPCLatam) (2013)

This article introduces the formulation of the VirtualMachine Planning Problem in cloud computing systems. It deals with the efficient allocation of a set of virtual machine requests from customers into ... [more ▼]

This article introduces the formulation of the VirtualMachine Planning Problem in cloud computing systems. It deals with the efficient allocation of a set of virtual machine requests from customers into the available pre-booked resources the broker has in a number of cloud providers, maximizing the broker profit. Eight list scheduling heuristics are proposed to solve the problem, by taking into account different criteria for mapping request to available virtual machines. The experimental evaluation analyzes the profit, makespan, and flowtime results of the proposed methods over a set of 400 problem instances that account for realistic workloads and scenarios using real data from cloud providers. [less ▲]

Detailed reference viewed: 68 (0 UL)
Full Text
Peer Reviewed
See detailA Parallel Multi-objective Local Search for AEDB Protocol Tuning
Iturriaga, Santiago; Ruiz, Patricia UL; Nesmachnow, Sergio et al

in IEEE International Parallel and Distributed Processing Simposium (2013)

Detailed reference viewed: 63 (0 UL)
Full Text
Peer Reviewed
See detailEnergy-Aware Scheduling on Multicore Heterogeneous Grid Computing Systems
Nesmachnow, sergio; Dorronsoro, bernabe; Pecero, Johnatan UL et al

in Journal of Grid Computing (2013), 11

We address a multicriteria nonpreemptive energy-aware scheduling problem for computationalGrid systems. This work introduces a new formulation of the scheduling problem for multicore heterogeneous ... [more ▼]

We address a multicriteria nonpreemptive energy-aware scheduling problem for computationalGrid systems. This work introduces a new formulation of the scheduling problem for multicore heterogeneous computational Grid systems in which the minimization of the energy consumption, along with the makespan metric, is considered. We adopt a two-level model, in which a meta-broker agent (level 1) receives all user tasks and schedules them on the available resources, belonging to different local providers (level 2). The computing capacity and energy consumption of resources are taken from real multi-core processors from the main current vendors. Twenty novel list scheduling methods for the problem are proposed, and a comparative analysis of all of them over a large set of problem instances is presented. Additionally, a scalability study is performed in order to analyze the contribution of the best new bi-objective list scheduling heuristics when the problem dimension grows. We conclude after the experimental analysis that accurate trade-off schedules are computed by using the new proposed methods. [less ▲]

Detailed reference viewed: 77 (0 UL)
Full Text
Peer Reviewed
See detailA Multithreading Local Search For Multiobjective Energy-Aware Scheduling In Heterogeneous Computing Systems
Iturriaga, Santiago; Nesmachnow, Sergio; Dorronsoro, Bernabé UL

in European Conference on Modelling and Simulation (ECMS) (2012)

This article introduces an efficient multithreading local search algorithm for solving the multiobjective scheduling problem in heterogeneous computing systems considering the makespan and energy ... [more ▼]

This article introduces an efficient multithreading local search algorithm for solving the multiobjective scheduling problem in heterogeneous computing systems considering the makespan and energy consumption objectives. The proposed method follows a fully multiobjective approach using a Pareto-based dominance search executed in parallel. The experimental analysis demonstrates that the new multithreading algorithm outperforms a set of deterministic heuristics based on Min-Min. The new method is able to achieve significant improvements in both objectives in reduced execution times for a broad set of testbed instances. [less ▲]

Detailed reference viewed: 38 (0 UL)