The Experts below are selected from a list of 34281 Experts worldwide ranked by ideXlab platform
Moshe Zukerman - One of the best experts on this subject based on the ideXlab platform.
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asymptotically optimal job assignment for energy efficient Processor sharing server farms
IEEE Journal on Selected Areas in Communications, 2016Co-Authors: Bill Moran, Jun Guo, Eric Wong, Moshe ZukermanAbstract:We study the problem of job assignment in a large-scale realistically dimensioned server farm comprising Multiple Processor-sharing servers with different service rates, energy consumption rates, and buffer sizes. Our aim is to optimize the energy efficiency of such a server farm by effectively controlling carried load on networked servers. To this end, we propose a job assignment policy, called Most energy-efficient available server first Accounting for Idle Power (MAIP), which is both scalable and near optimal. MAIP focuses on reducing the productive power used to support the processing service rate. Using the framework of semi-Markov decision process, we show that, with exponentially distributed job sizes, MAIP is equivalent to the well-known Whittle’s index policy. This equivalence and the methodology of Weber and Weiss enable us to prove that, in server farms where a loss of jobs happens if and only if all buffers are full, MAIP is asymptotically optimal, as the number of servers tends to infinity under certain conditions associated with the large number of servers, as we have in a real server farm. Through extensive numerical simulations, we demonstrate the effectiveness of MAIP and its robustness to different job-size distributions, and observe that significant improvement in energy efficiency can be achieved by utilizing the knowledge of energy consumption rate of idle servers.
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insensitive job assignment with throughput and energy criteria for Processor sharing server farms
IEEE ACM Transactions on Networking, 2014Co-Authors: Zvi Rosberg, Yu Peng, Jun Guo, Eric Wong, Moshe ZukermanAbstract:We study the problem of stochastic job assignment in a server farm comprising Multiple Processor-sharing servers with various speeds and finite buffer sizes. We consider two types of assignment policies: without jockeying, where an arriving job is assigned only once to an available server, and with jockeying, where a job may be reassigned at any time. We also require that the underlying Markov process under each policy is insensitive. Namely, the stationary distribution of the number of jobs in the system is independent of the job size distribution except for its mean. For the case without jockeying, we derive two insensitive heuristic policies: One aims at maximizing job throughput, and the other trades off job throughput for energy efficiency. For the case with jockeying, we formulate the optimal assignment problem as a semi-Markov decision process and derive optimal policies with respect to various optimization criteria. We further derive two simple insensitive heuristic policies with jockeying: One maximizes job throughput, and the other aims at maximizing energy efficiency. Numerical examples demonstrate that, under a wide range of system parameters, the latter policy performs very close to the optimal policy. Numerical examples also demonstrate energy/throughput tradeoffs for the various policies and, in the case with jockeying, they show a potential of substantial energy savings relative to a policy that optimizes throughput.
Bill Moran - One of the best experts on this subject based on the ideXlab platform.
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asymptotically optimal job assignment for energy efficient Processor sharing server farms
IEEE Journal on Selected Areas in Communications, 2016Co-Authors: Bill Moran, Jun Guo, Eric Wong, Moshe ZukermanAbstract:We study the problem of job assignment in a large-scale realistically dimensioned server farm comprising Multiple Processor-sharing servers with different service rates, energy consumption rates, and buffer sizes. Our aim is to optimize the energy efficiency of such a server farm by effectively controlling carried load on networked servers. To this end, we propose a job assignment policy, called Most energy-efficient available server first Accounting for Idle Power (MAIP), which is both scalable and near optimal. MAIP focuses on reducing the productive power used to support the processing service rate. Using the framework of semi-Markov decision process, we show that, with exponentially distributed job sizes, MAIP is equivalent to the well-known Whittle’s index policy. This equivalence and the methodology of Weber and Weiss enable us to prove that, in server farms where a loss of jobs happens if and only if all buffers are full, MAIP is asymptotically optimal, as the number of servers tends to infinity under certain conditions associated with the large number of servers, as we have in a real server farm. Through extensive numerical simulations, we demonstrate the effectiveness of MAIP and its robustness to different job-size distributions, and observe that significant improvement in energy efficiency can be achieved by utilizing the knowledge of energy consumption rate of idle servers.
Eric Wong - One of the best experts on this subject based on the ideXlab platform.
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asymptotically optimal job assignment for energy efficient Processor sharing server farms
IEEE Journal on Selected Areas in Communications, 2016Co-Authors: Bill Moran, Jun Guo, Eric Wong, Moshe ZukermanAbstract:We study the problem of job assignment in a large-scale realistically dimensioned server farm comprising Multiple Processor-sharing servers with different service rates, energy consumption rates, and buffer sizes. Our aim is to optimize the energy efficiency of such a server farm by effectively controlling carried load on networked servers. To this end, we propose a job assignment policy, called Most energy-efficient available server first Accounting for Idle Power (MAIP), which is both scalable and near optimal. MAIP focuses on reducing the productive power used to support the processing service rate. Using the framework of semi-Markov decision process, we show that, with exponentially distributed job sizes, MAIP is equivalent to the well-known Whittle’s index policy. This equivalence and the methodology of Weber and Weiss enable us to prove that, in server farms where a loss of jobs happens if and only if all buffers are full, MAIP is asymptotically optimal, as the number of servers tends to infinity under certain conditions associated with the large number of servers, as we have in a real server farm. Through extensive numerical simulations, we demonstrate the effectiveness of MAIP and its robustness to different job-size distributions, and observe that significant improvement in energy efficiency can be achieved by utilizing the knowledge of energy consumption rate of idle servers.
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insensitive job assignment with throughput and energy criteria for Processor sharing server farms
IEEE ACM Transactions on Networking, 2014Co-Authors: Zvi Rosberg, Yu Peng, Jun Guo, Eric Wong, Moshe ZukermanAbstract:We study the problem of stochastic job assignment in a server farm comprising Multiple Processor-sharing servers with various speeds and finite buffer sizes. We consider two types of assignment policies: without jockeying, where an arriving job is assigned only once to an available server, and with jockeying, where a job may be reassigned at any time. We also require that the underlying Markov process under each policy is insensitive. Namely, the stationary distribution of the number of jobs in the system is independent of the job size distribution except for its mean. For the case without jockeying, we derive two insensitive heuristic policies: One aims at maximizing job throughput, and the other trades off job throughput for energy efficiency. For the case with jockeying, we formulate the optimal assignment problem as a semi-Markov decision process and derive optimal policies with respect to various optimization criteria. We further derive two simple insensitive heuristic policies with jockeying: One maximizes job throughput, and the other aims at maximizing energy efficiency. Numerical examples demonstrate that, under a wide range of system parameters, the latter policy performs very close to the optimal policy. Numerical examples also demonstrate energy/throughput tradeoffs for the various policies and, in the case with jockeying, they show a potential of substantial energy savings relative to a policy that optimizes throughput.
Jun Guo - One of the best experts on this subject based on the ideXlab platform.
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asymptotically optimal job assignment for energy efficient Processor sharing server farms
IEEE Journal on Selected Areas in Communications, 2016Co-Authors: Bill Moran, Jun Guo, Eric Wong, Moshe ZukermanAbstract:We study the problem of job assignment in a large-scale realistically dimensioned server farm comprising Multiple Processor-sharing servers with different service rates, energy consumption rates, and buffer sizes. Our aim is to optimize the energy efficiency of such a server farm by effectively controlling carried load on networked servers. To this end, we propose a job assignment policy, called Most energy-efficient available server first Accounting for Idle Power (MAIP), which is both scalable and near optimal. MAIP focuses on reducing the productive power used to support the processing service rate. Using the framework of semi-Markov decision process, we show that, with exponentially distributed job sizes, MAIP is equivalent to the well-known Whittle’s index policy. This equivalence and the methodology of Weber and Weiss enable us to prove that, in server farms where a loss of jobs happens if and only if all buffers are full, MAIP is asymptotically optimal, as the number of servers tends to infinity under certain conditions associated with the large number of servers, as we have in a real server farm. Through extensive numerical simulations, we demonstrate the effectiveness of MAIP and its robustness to different job-size distributions, and observe that significant improvement in energy efficiency can be achieved by utilizing the knowledge of energy consumption rate of idle servers.
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insensitive job assignment with throughput and energy criteria for Processor sharing server farms
IEEE ACM Transactions on Networking, 2014Co-Authors: Zvi Rosberg, Yu Peng, Jun Guo, Eric Wong, Moshe ZukermanAbstract:We study the problem of stochastic job assignment in a server farm comprising Multiple Processor-sharing servers with various speeds and finite buffer sizes. We consider two types of assignment policies: without jockeying, where an arriving job is assigned only once to an available server, and with jockeying, where a job may be reassigned at any time. We also require that the underlying Markov process under each policy is insensitive. Namely, the stationary distribution of the number of jobs in the system is independent of the job size distribution except for its mean. For the case without jockeying, we derive two insensitive heuristic policies: One aims at maximizing job throughput, and the other trades off job throughput for energy efficiency. For the case with jockeying, we formulate the optimal assignment problem as a semi-Markov decision process and derive optimal policies with respect to various optimization criteria. We further derive two simple insensitive heuristic policies with jockeying: One maximizes job throughput, and the other aims at maximizing energy efficiency. Numerical examples demonstrate that, under a wide range of system parameters, the latter policy performs very close to the optimal policy. Numerical examples also demonstrate energy/throughput tradeoffs for the various policies and, in the case with jockeying, they show a potential of substantial energy savings relative to a policy that optimizes throughput.
U C Singh - One of the best experts on this subject based on the ideXlab platform.
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vector and parallel algorithms for the molecular dynamics simulation of macromolecules on shared memory computers
Journal of Computational Chemistry, 1991Co-Authors: John E Mertz, Douglas J Tobias, Charles L Brooks, U C SinghAbstract:A detailed description of vector/parallel algorithms for the molecular dynamics (MD) simulation of macromolecular systems on Multiple Processor, shared-memory computers is presented. The algorithms encompass three computationally intensive portions of typical MD programs: (1) the evaluation of the potential energies and forces, (2) the generation of the nonbonded neighbor list, and (3) the satisfaction of holonomic constraints. We implemented the algorithms into two standard programs; CHARMM and AMBER, and obtained near linear speedups on eight Processors of a Cray Y-MP for cases (1) and (2). For case (3) the SHAKE method demonstrated a speedup of 6.0 on eight Processors while the matrix inversion method demonstrated 6.4. For a system of water molecules the performance improvement over the standard scalar SHAKE subroutine in AMBER ranged from a factor of 165 to greater than 2000.
