The Experts below are selected from a list of 216 Experts worldwide ranked by ideXlab platform
Dmitriy Katz - One of the best experts on this subject based on the ideXlab platform.
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improved bounds for speed scaling in devices obeying the Cube Root rule
International Colloquium on Automata Languages and Programming, 2009Co-Authors: Nikhil Bansal, Holeung Chan, Kirk Pruhs, Dmitriy KatzAbstract:Speed scaling is a power management technique that involves dynamically changing the speed of a processor. This gives rise to dual-objective scheduling problems, where the operating system both wants to conserve energy and optimize some Quality of Service (QoS) measure of the resulting schedule. In the most investigated speed scaling problem in the literature, the QoS constraint is deadline feasibility, and the objective is to minimize the energy used. The standard assumption is that the power consumption is the speed to some constant power *** . We give the first non-trivial lower bound, namely e *** *** 1/*** , on the competitive ratio for this problem. This comes close to the best upper bound which is about 2e *** + 1. We analyze a natural class of algorithms called qOA, where at any time, the processor works at q *** 1 times the minimum speed required to ensure feasibility assuming no new jobs arrive. For CMOS based processors, and many other types of devices, *** = 3, that is, they satisfy the Cube-Root rule. When *** = 3, we show that qOA is 6.7-competitive, improving upon the previous best guarantee of 27 achieved by the algorithm Optimal Available (OA). So when the Cube-Root rule holds, our results reduce the range for the optimal competitive ratio from [1.2, 27] to [2.4, 6.7]. We also analyze qOA for general *** and give almost matching upper and lower bounds.
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ICALP (1) - Improved Bounds for Speed Scaling in Devices Obeying the Cube-Root Rule
Automata Languages and Programming, 2009Co-Authors: Nikhil Bansal, Holeung Chan, Kirk Pruhs, Dmitriy KatzAbstract:Speed scaling is a power management technique that involves dynamically changing the speed of a processor. This gives rise to dual-objective scheduling problems, where the operating system both wants to conserve energy and optimize some Quality of Service (QoS) measure of the resulting schedule. In the most investigated speed scaling problem in the literature, the QoS constraint is deadline feasibility, and the objective is to minimize the energy used. The standard assumption is that the power consumption is the speed to some constant power *** . We give the first non-trivial lower bound, namely e *** *** 1/*** , on the competitive ratio for this problem. This comes close to the best upper bound which is about 2e *** + 1. We analyze a natural class of algorithms called qOA, where at any time, the processor works at q *** 1 times the minimum speed required to ensure feasibility assuming no new jobs arrive. For CMOS based processors, and many other types of devices, *** = 3, that is, they satisfy the Cube-Root rule. When *** = 3, we show that qOA is 6.7-competitive, improving upon the previous best guarantee of 27 achieved by the algorithm Optimal Available (OA). So when the Cube-Root rule holds, our results reduce the range for the optimal competitive ratio from [1.2, 27] to [2.4, 6.7]. We also analyze qOA for general *** and give almost matching upper and lower bounds.
Nikhil Bansal - One of the best experts on this subject based on the ideXlab platform.
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improved bounds for speed scaling in devices obeying the Cube Root rule
International Colloquium on Automata Languages and Programming, 2009Co-Authors: Nikhil Bansal, Holeung Chan, Kirk Pruhs, Dmitriy KatzAbstract:Speed scaling is a power management technique that involves dynamically changing the speed of a processor. This gives rise to dual-objective scheduling problems, where the operating system both wants to conserve energy and optimize some Quality of Service (QoS) measure of the resulting schedule. In the most investigated speed scaling problem in the literature, the QoS constraint is deadline feasibility, and the objective is to minimize the energy used. The standard assumption is that the power consumption is the speed to some constant power *** . We give the first non-trivial lower bound, namely e *** *** 1/*** , on the competitive ratio for this problem. This comes close to the best upper bound which is about 2e *** + 1. We analyze a natural class of algorithms called qOA, where at any time, the processor works at q *** 1 times the minimum speed required to ensure feasibility assuming no new jobs arrive. For CMOS based processors, and many other types of devices, *** = 3, that is, they satisfy the Cube-Root rule. When *** = 3, we show that qOA is 6.7-competitive, improving upon the previous best guarantee of 27 achieved by the algorithm Optimal Available (OA). So when the Cube-Root rule holds, our results reduce the range for the optimal competitive ratio from [1.2, 27] to [2.4, 6.7]. We also analyze qOA for general *** and give almost matching upper and lower bounds.
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ICALP (1) - Improved Bounds for Speed Scaling in Devices Obeying the Cube-Root Rule
Automata Languages and Programming, 2009Co-Authors: Nikhil Bansal, Holeung Chan, Kirk Pruhs, Dmitriy KatzAbstract:Speed scaling is a power management technique that involves dynamically changing the speed of a processor. This gives rise to dual-objective scheduling problems, where the operating system both wants to conserve energy and optimize some Quality of Service (QoS) measure of the resulting schedule. In the most investigated speed scaling problem in the literature, the QoS constraint is deadline feasibility, and the objective is to minimize the energy used. The standard assumption is that the power consumption is the speed to some constant power *** . We give the first non-trivial lower bound, namely e *** *** 1/*** , on the competitive ratio for this problem. This comes close to the best upper bound which is about 2e *** + 1. We analyze a natural class of algorithms called qOA, where at any time, the processor works at q *** 1 times the minimum speed required to ensure feasibility assuming no new jobs arrive. For CMOS based processors, and many other types of devices, *** = 3, that is, they satisfy the Cube-Root rule. When *** = 3, we show that qOA is 6.7-competitive, improving upon the previous best guarantee of 27 achieved by the algorithm Optimal Available (OA). So when the Cube-Root rule holds, our results reduce the range for the optimal competitive ratio from [1.2, 27] to [2.4, 6.7]. We also analyze qOA for general *** and give almost matching upper and lower bounds.
Lorenz Ratke - One of the best experts on this subject based on the ideXlab platform.
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Effect of fraction solid on coarsening of secondary dendrite arms
International Journal of Cast Metals Research, 2009Co-Authors: Lorenz RatkeAbstract:AbstractCoarsening of secondary dendrite arms in mushy zones has been investigated theoretically and experimentally for many decades and simple phenomenological models have been developed describing the Cube Root dependence of the spacing on the solidification time. These models neglect in their description of mass transport between dendrite arms, that the finite volume fraction modifies the transport fluxes considerably. This paper describes an extension of conventional theoretical models for the secondary arm spacing based on finite volume fraction effect models for Ostwald ripening of spherical or cylindrical ensembles. The model predicts a much larger coarsening rate while the kinetics (Cube Root dependence) is conserved.
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Convective coarsening of secondary dendrite arms
2007Co-Authors: Lorenz Ratke, Sonja SteinbachAbstract:Coarsening of secondary dendrite arms in binary and multi-component alloys has been investigated theoretically and experimentally since decades and it generally is accepted that the secondary arms coarsen as the Cube Root of solidification time. Recent experimental investigation with AlSi, AlSiMg and AlSiCu alloys using gentle magnetic stirring in directional solidification experiments show that the coarsening kinetics are changed. Instead of a Cube Root relation it is observed that the secondary arms coarsen as the square Root of solidification time. This paper describes a theoretical model for the secondary arm spacing based on an Ostwald ripening model with convective mass transport. The model predicts a coarsening in accordance with the experiments and correctly predicts increasing stirring magnitude accelerates coarsening. It also makes predictions on the effect of the permeability of the mush on the coarsening rate factor.
Holeung Chan - One of the best experts on this subject based on the ideXlab platform.
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improved bounds for speed scaling in devices obeying the Cube Root rule
International Colloquium on Automata Languages and Programming, 2009Co-Authors: Nikhil Bansal, Holeung Chan, Kirk Pruhs, Dmitriy KatzAbstract:Speed scaling is a power management technique that involves dynamically changing the speed of a processor. This gives rise to dual-objective scheduling problems, where the operating system both wants to conserve energy and optimize some Quality of Service (QoS) measure of the resulting schedule. In the most investigated speed scaling problem in the literature, the QoS constraint is deadline feasibility, and the objective is to minimize the energy used. The standard assumption is that the power consumption is the speed to some constant power *** . We give the first non-trivial lower bound, namely e *** *** 1/*** , on the competitive ratio for this problem. This comes close to the best upper bound which is about 2e *** + 1. We analyze a natural class of algorithms called qOA, where at any time, the processor works at q *** 1 times the minimum speed required to ensure feasibility assuming no new jobs arrive. For CMOS based processors, and many other types of devices, *** = 3, that is, they satisfy the Cube-Root rule. When *** = 3, we show that qOA is 6.7-competitive, improving upon the previous best guarantee of 27 achieved by the algorithm Optimal Available (OA). So when the Cube-Root rule holds, our results reduce the range for the optimal competitive ratio from [1.2, 27] to [2.4, 6.7]. We also analyze qOA for general *** and give almost matching upper and lower bounds.
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ICALP (1) - Improved Bounds for Speed Scaling in Devices Obeying the Cube-Root Rule
Automata Languages and Programming, 2009Co-Authors: Nikhil Bansal, Holeung Chan, Kirk Pruhs, Dmitriy KatzAbstract:Speed scaling is a power management technique that involves dynamically changing the speed of a processor. This gives rise to dual-objective scheduling problems, where the operating system both wants to conserve energy and optimize some Quality of Service (QoS) measure of the resulting schedule. In the most investigated speed scaling problem in the literature, the QoS constraint is deadline feasibility, and the objective is to minimize the energy used. The standard assumption is that the power consumption is the speed to some constant power *** . We give the first non-trivial lower bound, namely e *** *** 1/*** , on the competitive ratio for this problem. This comes close to the best upper bound which is about 2e *** + 1. We analyze a natural class of algorithms called qOA, where at any time, the processor works at q *** 1 times the minimum speed required to ensure feasibility assuming no new jobs arrive. For CMOS based processors, and many other types of devices, *** = 3, that is, they satisfy the Cube-Root rule. When *** = 3, we show that qOA is 6.7-competitive, improving upon the previous best guarantee of 27 achieved by the algorithm Optimal Available (OA). So when the Cube-Root rule holds, our results reduce the range for the optimal competitive ratio from [1.2, 27] to [2.4, 6.7]. We also analyze qOA for general *** and give almost matching upper and lower bounds.
Kirk Pruhs - One of the best experts on this subject based on the ideXlab platform.
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improved bounds for speed scaling in devices obeying the Cube Root rule
International Colloquium on Automata Languages and Programming, 2009Co-Authors: Nikhil Bansal, Holeung Chan, Kirk Pruhs, Dmitriy KatzAbstract:Speed scaling is a power management technique that involves dynamically changing the speed of a processor. This gives rise to dual-objective scheduling problems, where the operating system both wants to conserve energy and optimize some Quality of Service (QoS) measure of the resulting schedule. In the most investigated speed scaling problem in the literature, the QoS constraint is deadline feasibility, and the objective is to minimize the energy used. The standard assumption is that the power consumption is the speed to some constant power *** . We give the first non-trivial lower bound, namely e *** *** 1/*** , on the competitive ratio for this problem. This comes close to the best upper bound which is about 2e *** + 1. We analyze a natural class of algorithms called qOA, where at any time, the processor works at q *** 1 times the minimum speed required to ensure feasibility assuming no new jobs arrive. For CMOS based processors, and many other types of devices, *** = 3, that is, they satisfy the Cube-Root rule. When *** = 3, we show that qOA is 6.7-competitive, improving upon the previous best guarantee of 27 achieved by the algorithm Optimal Available (OA). So when the Cube-Root rule holds, our results reduce the range for the optimal competitive ratio from [1.2, 27] to [2.4, 6.7]. We also analyze qOA for general *** and give almost matching upper and lower bounds.
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ICALP (1) - Improved Bounds for Speed Scaling in Devices Obeying the Cube-Root Rule
Automata Languages and Programming, 2009Co-Authors: Nikhil Bansal, Holeung Chan, Kirk Pruhs, Dmitriy KatzAbstract:Speed scaling is a power management technique that involves dynamically changing the speed of a processor. This gives rise to dual-objective scheduling problems, where the operating system both wants to conserve energy and optimize some Quality of Service (QoS) measure of the resulting schedule. In the most investigated speed scaling problem in the literature, the QoS constraint is deadline feasibility, and the objective is to minimize the energy used. The standard assumption is that the power consumption is the speed to some constant power *** . We give the first non-trivial lower bound, namely e *** *** 1/*** , on the competitive ratio for this problem. This comes close to the best upper bound which is about 2e *** + 1. We analyze a natural class of algorithms called qOA, where at any time, the processor works at q *** 1 times the minimum speed required to ensure feasibility assuming no new jobs arrive. For CMOS based processors, and many other types of devices, *** = 3, that is, they satisfy the Cube-Root rule. When *** = 3, we show that qOA is 6.7-competitive, improving upon the previous best guarantee of 27 achieved by the algorithm Optimal Available (OA). So when the Cube-Root rule holds, our results reduce the range for the optimal competitive ratio from [1.2, 27] to [2.4, 6.7]. We also analyze qOA for general *** and give almost matching upper and lower bounds.