References of "Cluster Computing"
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See detailEnergy-efficient Data Replication in Cloud Computing Datacenters
Boru, Dejene; Kliazovich, Dzmitry UL; Granelli, Fabrizio et al

in Cluster Computing (2015), 18(1), 385-402

Cloud computing is an emerging paradigm that provides computing, communication and storage resources as a service over a network. Communication resources often become a bottleneck in service provisioning ... [more ▼]

Cloud computing is an emerging paradigm that provides computing, communication and storage resources as a service over a network. Communication resources often become a bottleneck in service provisioning for many cloud applications. Therefore, data replication which brings data (e.g., databases) closer to data consumers (e.g., cloud applications) is seen as a promising solution. It allows minimizing network delays and bandwidth usage. In this paper we study data replication in cloud computing data centers. Unlike other approaches available in the literature, we consider both energy efficiency and bandwidth consumption of the system. This is in addition to the improved quality of service QoS obtained as a result of the reduced communication delays. The evaluation results, obtained from both mathematical model and extensive simulations, help to unveil performance and energy efficiency tradeoffs as well as guide the design of future data replication solutions. [less ▲]

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See detailThe sandpile scheduler: How self-organized criticality may lead to dynamic load-balancing
Jimenez Laredo, Juan Luis UL; Bouvry, Pascal UL; Guinand, Frederic et al

in Cluster Computing (2014)

This paper studies a self-organized criticality model called sandpile for dynamically load-balancing tasks arriving in the form of Bag-of-Tasks in large-scale decentralized system. The sandpile is ... [more ▼]

This paper studies a self-organized criticality model called sandpile for dynamically load-balancing tasks arriving in the form of Bag-of-Tasks in large-scale decentralized system. The sandpile is designed as a decentralized agent system characterizing a cellular automaton, which works in a critical state at the edge of chaos. Depending on the state of the cellular automaton, different responses may occur when a new task is assigned to a resource: it may change nothing or generate avalanches that reconfigure the state of the system. The abundance of such avalanches is in power-law relation with their sizes, a scale-invariant behavior that emerges without requiring tuning or control parameters. That means that large—catastrophic—avalanches are very rare but small ones occur very often. Such emergent pattern can be efficiently adapted for non-clairvoyant scheduling, where tasks are load balanced in computing resources trying to maximize the performance but without assuming any knowledge on the tasks features. The algorithm design is experimentally validated showing that the sandpile is able to find near-optimal schedules by reacting differently to different conditions of workloads and architectures. [less ▲]

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See detailSolving the three dimensional quadratic assignment problem on a computational grid
Mezmaz, Mohand; Mehdi, Malika UL; Bouvry, Pascal UL et al

in Cluster Computing (2014), 17(2), 205-217

The exact resolution of large instances of combinatorial optimization problems, such as three dimensional quadratic assignment problem (Q3AP), is a real challenge for grid computing. Indeed, it is ... [more ▼]

The exact resolution of large instances of combinatorial optimization problems, such as three dimensional quadratic assignment problem (Q3AP), is a real challenge for grid computing. Indeed, it is necessary to reconsider the resolution algorithms and take into account the characteristics of such environments, especially large scale and dynamic availability of resources, and their multi-domain administration. In this paper, we revisit the design and implementation of the branch and bound algorithm for solving large combinatorial optimization problems such as Q3AP on the computational grids. Such gridification is based on new ways to effi- ciently deal with some crucial issues, mainly dynamic adaptive load balancing and fault tolerance. Our new approach allowed the exact resolution on a nation-wide grid of a dif- ficult Q3AP instance. To solve this instance, an average of 1,123 computing cores were used for less than 12 days with a peak of around 3,427 computing cores. [less ▲]

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See detailDENS: data center energy-efficient network-aware scheduling
Kliazovich, Dzmitry UL; Bouvry, Pascal UL; Khan, Samee Ullah

in Cluster Computing (2013), 16(1), 65-75

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See detailGreen Flexible Opportunistic Computing with task consolidation and virtualization
Castro, Harold; Villamizar, Mario; Sotelo, German et al

in Cluster Computing (2012)

Energy efficiency and high computing power are basic design considerations across modern-day computing solutions due to different concerns such as system perfor- mance, operational cost, and environmental ... [more ▼]

Energy efficiency and high computing power are basic design considerations across modern-day computing solutions due to different concerns such as system perfor- mance, operational cost, and environmental issues. Desktop Grid and Volunteer Computing System (DGVCS) so called opportunistic infrastructures offer computational power at low cost focused on harvesting idle computing cycles of ex- isting commodity computing resources. Other than allow- ing to customize the end user offer, virtualization is consid- ered as one key techniques to reduce energy consumption in large-scale systems and contributes to the scalability of the system. This paper presents an energy efficient approach for opportunistic infrastructures based on task consolidation and customization of virtual machines. The experimental re- sults with single desktops and complete computer rooms show that virtualization significantly improves the energy-efficiency of opportunistic grids compared with dedicated computing systems without disturbing the end-user. [less ▲]

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See detailFinding Scalable Configurations for AEDB Broadcasting Protocol using Multi-objective Evolutionary
Ruiz, Patricia UL; Dorronsoro, Bernabe; Bouvry, Pascal UL

in Cluster Computing (2012), 16

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See detailAn overview of energy efficiency techniques in cluster computing systems
Valentini, Giorgio Luigi; Lassonde, Walter; Khan, Samee Ullah et al

in Cluster Computing (2011), 16(1), 3-15

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