Search results for "Cluster computing"

showing 10 items of 120 documents

Combined IT and power supply infrastructure sizing for standalone green data centers

2021

International audience; In this work, we propose a two-step methodology for designing and sizing a data center solely powered by local renewable energy. The first step consists in determining the necessary IT equipment for processing a given IT workload composed of batch and service tasks. We propose an adapted binary search algorithm and prove its optimality to find the minimum number of servers to handle the IT workload. When the IT sizing is computed, the second step consists in defining the supplying electrical infrastructure using wind turbines and photovoltaic panels as primary sources. Batteries and a hydrogen system are added as secondary sources for short- and long-term energy stor…

Renewable energyBinary search algorithmWind powerGeneral Computer Sciencebusiness.industryComputer scienceInfrastructure sizing020209 energyReal-time computingPhotovoltaic systemSustainable Computing: Informatics and Systems Renewable energy020206 networking & telecommunicationsWorkload02 engineering and technology7. Clean energyGreen data centerSizingEnergy storageServer0202 electrical engineering electronic engineering information engineeringData center[INFO.INFO-OS]Computer Science [cs]/Operating Systems [cs.OS][INFO.INFO-DC]Computer Science [cs]/Distributed Parallel and Cluster Computing [cs.DC]Electrical and Electronic EngineeringbusinessSustainable Computing: Informatics and Systems
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Checkpointing Workflows for Fail-Stop Errors

2017

International audience; We consider the problem of orchestrating the exe- cution of workflow applications structured as Directed Acyclic Graphs (DAGs) on parallel computing platforms that are subject to fail-stop failures. The objective is to minimize expected overall execution time, or makespan. A solution to this problem consists of a schedule of the workflow tasks on the available processors and of a decision of which application data to checkpoint to stable storage, so as to mitigate the impact of processor failures. For general DAGs this problem is hopelessly intractable. In fact, given a solution, computing its expected makespan is still a difficult problem. To address this challenge,…

ScheduleComputer scienceworkflowDistributed computing[INFO.INFO-DS]Computer Science [cs]/Data Structures and Algorithms [cs.DS]010103 numerical & computational mathematics02 engineering and technologyParallel computing[INFO.INFO-SE]Computer Science [cs]/Software Engineering [cs.SE]01 natural sciencesTheoretical Computer Science[INFO.INFO-IU]Computer Science [cs]/Ubiquitous Computing[INFO.INFO-CR]Computer Science [cs]/Cryptography and Security [cs.CR]checkpointfail-stop error0202 electrical engineering electronic engineering information engineeringOverhead (computing)[INFO]Computer Science [cs]0101 mathematicsresilienceClass (computer programming)020203 distributed computingJob shop schedulingProbabilistic logic020206 networking & telecommunications[INFO.INFO-MO]Computer Science [cs]/Modeling and SimulationDynamic programmingTask (computing)[INFO.INFO-PF]Computer Science [cs]/Performance [cs.PF]WorkflowComputational Theory and MathematicsHardware and Architecture[INFO.INFO-MA]Computer Science [cs]/Multiagent Systems [cs.MA]Task analysis[INFO.INFO-ET]Computer Science [cs]/Emerging Technologies [cs.ET][INFO.INFO-DC]Computer Science [cs]/Distributed Parallel and Cluster Computing [cs.DC]Software
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Serial In-network Processing for Large Stationary Wireless Sensor Networks

2017

International audience; In wireless sensor networks, a serial processing algorithm browses nodes one by one and can perform different tasks such as: creating a schedule among nodes, querying or gathering data from nodes, supplying nodes with data, etc. Apart from the fact thatserial algorithms totally avoid collisions, numerous recent works have confirmed that these algorithms reduce communications andconsiderably save energy and time in large-dense networks. Yet, due to the path construction complexity, the proposed algorithmsare not optimal and their performances can be further enhanced. To do so, in the present paper, we propose a new serial processing algorithm that, in most of the case…

ScheduleVisual sensor networkbusiness.industryComputer science020206 networking & telecommunications02 engineering and technology[INFO.INFO-SE]Computer Science [cs]/Software Engineering [cs.SE][INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation020202 computer hardware & architectureSerial memory processing[INFO.INFO-IU]Computer Science [cs]/Ubiquitous ComputingKey distribution in wireless sensor networks[INFO.INFO-CR]Computer Science [cs]/Cryptography and Security [cs.CR][INFO.INFO-MA]Computer Science [cs]/Multiagent Systems [cs.MA]Sensor nodeScalability0202 electrical engineering electronic engineering information engineeringMobile wireless sensor network[INFO.INFO-ET]Computer Science [cs]/Emerging Technologies [cs.ET][INFO.INFO-DC]Computer Science [cs]/Distributed Parallel and Cluster Computing [cs.DC]businessWireless sensor networkComputer network
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PROLISEAN: A New Security Protocol for Programmable Matter

2021

The vision for programmable matter is to create a material that can be reprogrammed to have different shapes and to change its physical properties on demand. They are autonomous systems composed of a huge number of independent connected elements called particles. The connections to one another form the overall shape of the system. These particles are capable of interacting with each other and take decisions based on their environment. Beyond sensing, processing, and communication capabilities, programmable matter includes actuation and motion capabilities. It could be deployed in different domains and will constitute an intelligent component of the IoT. A lot of applications can derive fro…

Self-reconfiguring modular robot0209 industrial biotechnologySecurity AlgorithmsComputer Networks and CommunicationsComputer scienceDistributed computingHash functionSecurity Protocol02 engineering and technology[INFO.INFO-SE]Computer Science [cs]/Software Engineering [cs.SE]EncryptionLightweight Cryptography[INFO.INFO-IU]Computer Science [cs]/Ubiquitous Computing[INFO.INFO-CR]Computer Science [cs]/Cryptography and Security [cs.CR]020901 industrial engineering & automationComponent (UML)0202 electrical engineering electronic engineering information engineeringModular RobotsProgrammable MatterProtocol (object-oriented programming)IOTbusiness.industry020206 networking & telecommunicationsCryptographic protocolSupercomputer[INFO.INFO-MO]Computer Science [cs]/Modeling and SimulationProgrammable matter[INFO.INFO-MA]Computer Science [cs]/Multiagent Systems [cs.MA][INFO.INFO-ET]Computer Science [cs]/Emerging Technologies [cs.ET]Amoebots[INFO.INFO-DC]Computer Science [cs]/Distributed Parallel and Cluster Computing [cs.DC]businessDistributed Computing
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Experimental Performance Evaluation of Cloud-Based Analytics-as-a-Service

2016

An increasing number of Analytics-as-a-Service solutions has recently seen the light, in the landscape of cloud-based services. These services allow flexible composition of compute and storage components, that create powerful data ingestion and processing pipelines. This work is a first attempt at an experimental evaluation of analytic application performance executed using a wide range of storage service configurations. We present an intuitive notion of data locality, that we use as a proxy to rank different service compositions in terms of expected performance. Through an empirical analysis, we dissect the performance achieved by analytic workloads and unveil problems due to the impedance…

Service (business)FOS: Computer and information sciencesDistributed databaseComputer sciencebusiness.industryReliability (computer networking)Distributed computingRank (computer programming)020206 networking & telecommunicationsCloud computing02 engineering and technologyData modelingperformance evaluationstorageComputer Science - Distributed Parallel and Cluster ComputingAnalytics0202 electrical engineering electronic engineering information engineeringDistributed Parallel and Cluster Computing (cs.DC)business
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A Spatial-Temporal Correlation Approach for Data Reduction in Cluster-Based Sensor Networks

2019

International audience; In a resource-constrained Wireless Sensor Networks (WSNs), the optimization of the sampling and the transmission rates of each individual node is a crucial issue. A high volume of redundant data transmitted through the network will result in collisions, data loss, and energy dissipation. This paper proposes a novel data reduction scheme, that exploits the spatial-temporal correlation among sensor data in order to determine the optimal sampling strategy for the deployed sensor nodes. This strategy reduces the overall sampling/transmission rates while preserving the quality of the data. Moreover, a back-end reconstruction algorithm is deployed on the workstation (Sink)…

Signal Processing (eess.SP)FOS: Computer and information sciencesAdaptive samplingGeneral Computer ScienceComputer sciencespatial-temporal correlationReal-time computing02 engineering and technologyData loss[INFO.INFO-SE]Computer Science [cs]/Software Engineering [cs.SE]data reconstructionQA76Computer Science - Networking and Internet Architecture[INFO.INFO-IU]Computer Science [cs]/Ubiquitous Computing[INFO.INFO-CR]Computer Science [cs]/Cryptography and Security [cs.CR]FOS: Electrical engineering electronic engineering information engineering0202 electrical engineering electronic engineering information engineeringGeneral Materials ScienceElectrical Engineering and Systems Science - Signal ProcessingNetworking and Internet Architecture (cs.NI)General EngineeringSampling (statistics)020206 networking & telecommunicationsReconstruction algorithmDissipation[INFO.INFO-MO]Computer Science [cs]/Modeling and SimulationWireless sensor networks[INFO.INFO-MA]Computer Science [cs]/Multiagent Systems [cs.MA]data reduction020201 artificial intelligence & image processing[INFO.INFO-ET]Computer Science [cs]/Emerging Technologies [cs.ET]lcsh:Electrical engineering. Electronics. Nuclear engineering[INFO.INFO-DC]Computer Science [cs]/Distributed Parallel and Cluster Computing [cs.DC]lcsh:TK1-9971Wireless sensor networkData reduction
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SWAPHI: Smith-Waterman Protein Database Search on Xeon Phi Coprocessors

2014

The maximal sensitivity of the Smith-Waterman (SW) algorithm has enabled its wide use in biological sequence database search. Unfortunately, the high sensitivity comes at the expense of quadratic time complexity, which makes the algorithm computationally demanding for big databases. In this paper, we present SWAPHI, the first parallelized algorithm employing Xeon Phi coprocessors to accelerate SW protein database search. SWAPHI is designed based on the scale-and-vectorize approach, i.e. it boosts alignment speed by effectively utilizing both the coarse-grained parallelism from the many co-processing cores (scale) and the fine-grained parallelism from the 512-bit wide single instruction, mul…

Smith–Waterman algorithmFOS: Computer and information sciencesMulti-core processorCoprocessorSpeedupSequence databaseComputer scienceParallel computingIntrinsicsComputer Science - Distributed Parallel and Cluster ComputingScalabilitySIMDDistributed Parallel and Cluster Computing (cs.DC)Xeon Phi
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PTNet: An efficient and green data center network

2017

International audience; In recent years, data centers have witnessed an exponential growth for hosting hundreds of thousands of servers as well as to accommodating a very large demand for resources. To fulfill the required level of demand, some approaches tackled network aspects so to host a huge number of servers while others focused on delivering rapid services to the clients by minimizing the path length between any two servers. In general, network devices are often designed to achieve 1:1 oversubscription. Alternatively, in a realistic data center environment, the average utilization of a network could vary between 5% and 25%, and thus the energy consumed by idle devices is wasted. This…

[ INFO ] Computer Science [cs]Computer Networks and CommunicationsComputer scienceDistributed computing02 engineering and technologyNetwork topology[ INFO.INFO-AO ] Computer Science [cs]/Computer ArithmeticTheoretical Computer Science03 medical and health sciences[INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI]0302 clinical medicineArtificial IntelligenceRobustness (computer science)Energy savingServerArchitecture0202 electrical engineering electronic engineering information engineering[INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO][INFO]Computer Science [cs]ComputingMilieux_MISCELLANEOUSAverage path lengthInterconnectionNetwork topologyEnergy[INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB]business.industry[INFO.INFO-AO]Computer Science [cs]/Computer ArithmeticScalability020206 networking & telecommunicationsData center networkAverage path lengthNetworking hardware[INFO.INFO-GR]Computer Science [cs]/Graphics [cs.GR]Hardware and Architecture030220 oncology & carcinogenesis[INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV]ScalabilityData center[INFO.INFO-DC]Computer Science [cs]/Distributed Parallel and Cluster Computing [cs.DC]businessSoftwareComputer network
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Extending CSG with projections: Towards formally certified geometric modeling

2015

We extend traditional Constructive Solid Geometry (CSG) trees to support the projection operator. Existing algorithms in the literature prove various topological properties of CSG sets. Our extension readily allows these algorithms to work on a greater variety of sets, in particular parametric sets, which are extensively used in CAD/CAM systems. Constructive Solid Geometry allows for algebraic representation which makes it easy for certification tools to apply. A geometric primitive may be defined in terms of a characteristic function, which can be seen as the zero-set of a corresponding system along with inequality constraints. To handle projections, we exploit the Disjunctive Normal Form,…

[ INFO ] Computer Science [cs]Disjoint setsDisjunctive normal formIndustrial and Manufacturing EngineeringProjection (linear algebra)Interval arithmeticConstructive solid geometryConstructive solid geometry[INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI][INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO]Homotopy equivalenceGeometric primitiveBinary expression tree[INFO]Computer Science [cs]ProjectionComputingMilieux_MISCELLANEOUSMathematicsDiscrete mathematics[INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB]HomotopyFormal methodsDisjunctive normal formComputer Graphics and Computer-Aided Design[INFO.INFO-GR]Computer Science [cs]/Graphics [cs.GR]Computer Science ApplicationsAlgebra[INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV][INFO.INFO-DC]Computer Science [cs]/Distributed Parallel and Cluster Computing [cs.DC]
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NoC based virtualized FPGA as cloud Services

2016

International audience; Web-based applications are increasingly demanding many computationally intensive services. On the other hand, FPGA-based hardware accelerators(HwAcc) provide good performance in accelerating computationally intensive applications. In addition, some FPGAs support a dynamic partial reconfig-uration (DPR) techniques to virtualize and share the FPGA underlying hardware resources in time multiplexing during run-time to save resource and power consumption. Integrating FPGA in a cloud environment is an indispensable way to improve efficiency and provide acceleration services to demanding users. More importantly, in recent years it was proved that FPGA resources deployed in …

[ INFO.INFO-DC ] Computer Science [cs]/Distributed Parallel and Cluster Computing [cs.DC][INFO.INFO-DC] Computer Science [cs]/Distributed Parallel and Cluster Computing [cs.DC]Virtualized FPGA[INFO.INFO-ES]Computer Science [cs]/Embedded Systems[ INFO.INFO-ES ] Computer Science [cs]/Embedded Systems[INFO.INFO-DC]Computer Science [cs]/Distributed Parallel and Cluster Computing [cs.DC]Cloud ComputingNetwork-on-Chip[INFO.INFO-ES] Computer Science [cs]/Embedded SystemsHard- ware accelerators
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