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Mapping on multi/manycore systems: survey of current and emerging trends
 In Proceedings of the 50th Annual Design Automation Conference, DAC ’13
, 2013
"... The reliance on multi/manycore systems to satisfy the high performance requirement of complex embedded software applications is increasing. This necessitates the need to realize efficient mapping methodologies for such complex computing platforms. This paper provides an extensive survey and cate ..."
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The reliance on multi/manycore systems to satisfy the high performance requirement of complex embedded software applications is increasing. This necessitates the need to realize efficient mapping methodologies for such complex computing platforms. This paper provides an extensive survey and categorization of stateoftheart mapping methodologies and highlights the emerging trends for multi/manycore systems. The methodologies aim at optimizing system’s resource usage, performance, power consumption, temperature distribution and reliability for varying application models. The methodologies perform designtime and runtime optimization for static and dynamic workload scenarios, respectively. These optimizations are necessary to fulfill the enduser demands. Comparison of the methodologies based on their optimization aim has been provided. The trend followed by the methodologies and open research challenges have also been discussed. Categories and Subject Descriptors C.3 [Specialpurpose and applicationbased systems]: Realtime systems and embedded systems
Approximation Algorithms for Energy Minimization in Cloud Service Allocation under Reliability Constraints
"... Abstract—We consider allocation problems that arise in the context of service allocation in Clouds. More specifically, we assume on the one part that each computing resource is associated with a capacity, that can be chosen using the Dynamic Voltage and Frequency Scaling (DVFS) method, and with a pr ..."
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Abstract—We consider allocation problems that arise in the context of service allocation in Clouds. More specifically, we assume on the one part that each computing resource is associated with a capacity, that can be chosen using the Dynamic Voltage and Frequency Scaling (DVFS) method, and with a probability of failure. On the other hand, we assume that the services run as a set of independent instances of identical Virtual Machines (VMs). Moreover, there exists a Service Level Agreement (SLA) between the Cloud provider and the client that can be expressed as follows: the client comes with a minimal number of service instances that must be alive at anytime, and the Cloud provider offers a list of pairs (price, compensation), the compensation having to be paid by the Cloud provider if it fails to keep alive the required number of services. On the Cloud provider side, each pair actually corresponds to a guaranteed reliability of fulfilling the constraint on the minimal number of instances. In this context, given a minimal number of instances and a probability of success, the question for the Cloud provider is to find the number of necessary resources, their clock frequency and an allocation of the instances (possibly using replication) onto machines. This solution should satisfy all types of constraints (both capacity and reliability constraints). Moreover, it should remain valid during a time period (with a given reliability in presence of failures) while minimizing the energy consumption of used resources. We assume in this paper that this time period, that typically takes place between two redistributions, is fixed and known in advance. We prove deterministic approximation ratios on the consumed energy for algorithms that provide guaranteed reliability and we provide an extensive set of simulations that prove that homogeneous solutions are close to optimal. Keywords—Cloud, reliability, approximation, energy savings
in Cloud Service Allocation under Reliability Constraints
"... Abstract: We consider allocation problems that arise in the context of service allocation in Clouds. More specifically, we assume on the one part that each computing resource is associated to a capacity constraint, that can be chosen using Dynamic Voltage and Frequency Scaling (DVFS) method, and to ..."
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Abstract: We consider allocation problems that arise in the context of service allocation in Clouds. More specifically, we assume on the one part that each computing resource is associated to a capacity constraint, that can be chosen using Dynamic Voltage and Frequency Scaling (DVFS) method, and to a probability of failure. On the other hand, we assume that the service runs as a set of independent instances of identical Virtual Machines. Moreover, there exists a Service Level Agreement (SLA) between the Cloud provider and the client that can be expressed as follows: the client comes with a minimal number of service instances which must be alive at the end of the day, and the Cloud provider offers a list of pairs (price, compensation), this compensation being paid by the Cloud provider if it fails to keep alive the required number of services. On the Cloud provider side, each pair corresponds actually to a guaranteed success probability of fulfilling the constraint on the minimal number of instances. In this context, given a minimal number of instances and a probability of success, the question for the Cloud provider is to find the number of necessary resources, their clock frequency and an allocation of the instances (possibly using replication) onto machines. This solution should satisfy all types of constraints during a given time period while minimizing the energy consumption of used resources. We consider two energy consumption models based on DVFS techniques, where the clock frequency of physical resources
Author manuscript, published in "HIgh Performance Computing (2013) 20" Approximation Algorithms for Energy Minimization in Cloud Service Allocation under Reliability Constraints
, 2013
"... Abstract—We consider allocation problems that arise in the context of service allocation in Clouds. More specifically, we assume on the one part that each computing resource is associated with a capacity, that can be chosen using the Dynamic Voltage and Frequency Scaling (DVFS) method, and with a pr ..."
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Abstract—We consider allocation problems that arise in the context of service allocation in Clouds. More specifically, we assume on the one part that each computing resource is associated with a capacity, that can be chosen using the Dynamic Voltage and Frequency Scaling (DVFS) method, and with a probability of failure. On the other hand, we assume that the services run as a set of independent instances of identical Virtual Machines (VMs). Moreover, there exists a Service Level Agreement (SLA) between the Cloud provider and the client that can be expressed as follows: the client comes with a minimal number of service instances that must be alive at anytime, and the Cloud provider offers a list of pairs (price, compensation), the compensation having to be paid by the Cloud provider if it fails to keep alive the required number of services. On the Cloud provider side, each pair actually corresponds to a guaranteed reliability of fulfilling the constraint
ReliabilityDriven EnergyEfficient Task Scheduling for Multiprocessor RealTime Systems
"... Abstract—This paper proposes a reliabilitydriven task scheduling scheme for multiprocessor realtime embedded systems that optimizes system energy consumption under stochastic fault occurrences. The task scheduling problem is formulated as an integer linear program where a novel fault adaptation va ..."
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Abstract—This paper proposes a reliabilitydriven task scheduling scheme for multiprocessor realtime embedded systems that optimizes system energy consumption under stochastic fault occurrences. The task scheduling problem is formulated as an integer linear program where a novel fault adaptation variable is introduced to model the uncertainties of fault occurrences. The proposed scheme, which considers both the dynamic power and the leakage power, is able to handle the scheduling of independent tasks and tasks with precedence constraints, and is capable of scheduling tasks with varying deadlines. Experimental results have demonstrated that the proposed reliabilitydriven parallel scheduling scheme achieves energy savings of more than 15 % when compared to the approach of designing for the corner case of fault occurrences. Index Terms—Energy efficient, multiprocessor system, realtime systems, reliability, task scheduling. I.
Particle Swarm Optimization With Composite Particles in Dynamic Environments
 IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART B: CYBERNETICS
, 2010
"... In recent years, there has been a growing interest in the study of particle swarm optimization (PSO) in dynamic environments. This paper presents a new PSO model, called PSO with composite particles (PSOCP), to address dynamic optimization problems. PSOCP partitions the swarm into a set of composi ..."
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In recent years, there has been a growing interest in the study of particle swarm optimization (PSO) in dynamic environments. This paper presents a new PSO model, called PSO with composite particles (PSOCP), to address dynamic optimization problems. PSOCP partitions the swarm into a set of composite particles based on their similarity using a “worst first ” principle. Inspired by the composite particle phenomenon in physics, the elementary members in each composite particle interact via a velocityanisotropic reflection scheme to integrate valuable information for effectively and rapidly finding the promising optima in the search space. Each composite particle maintains the diversity by a scattering operator. In addition, an integral movement strategy is introduced to promote the swarm diversity. Experiments on a typical dynamic test benchmark problem provide a guideline for setting the involved parameters and show that PSOCP is efficient in comparison with several stateoftheart PSO algorithms for dynamic optimization problems.