System design and power optimization for mobile computers

Smailagic, A.; Ettus, M.

doi:10.1109/isvlsi.2002.1016867

“…These applications include image processing, e.g., target detection and recognition used in robot control (Kremer et al [2001]), voice recognition (Smailagic et al [2002]), and large-scale numerical computations (Rudenko et al [1998]). …”

Section: Introductionmentioning

confidence: 99%

“…Experiments performed in Smailagic et al [2002] and Rudenko et al [1998] demonstrated the potential of remote processing for significant power savings in a number of real time tasks. The results of one experiment reported in reference Rudenko et al [1998] are depicted in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

A Markovian Decision-Based Approach for Extending the Lifetime of a Network of Battery-Powered Mobile Devices by Remote Processing

Peng¹,

Pedram²

2010

Journal of Low Power Electronics

2

0

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-This paper addresses the problem of extending the lifetime of a batterypowered mobile host in a client-server wireless network by using task migration and remote processing. This problem is solved by first constructing a stochastic model of the client-server system based on the theory of continuous-time Markovian decision processes. Next the dynamic power management problem with task migration is formulated as a policy optimization problem and solved exactly by using a linear programming approach. Based on the off-line optimal policy derived in this way, an online adaptive policy is proposed, which dynamically monitors the channel conditions and the server behavior, takes into account real-time constraints, and adopts a client-side power management policy with task migration that results in optimum energy consumption in the client. Experimental results demonstrate that the proposed method outperforms existing heuristic methods by as much as 35% in terms of the overall energy savings.

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“…These applications include image processing, e.g., target detection and recognition used in robot control [1], voice recognition [2], and large-scale numerical computations [3]. The effectiveness of the remote processing technique is limited by the fact that data transmission over wireless channel results in additional power consumption.…”

Section: Introductionmentioning

confidence: 99%

“…A number of research results related to this problem have been reported in the literature. Experiments performed in [2][3] demonstrated the potential of remote processing for significant power savings in a number of real time tasks. Based on CPU measurements, reference [5] proposed an adaptive decision-making policy for a repetitive task.…”

Section: Introductionmentioning

confidence: 99%

Extending the lifetime of a network of battery-powered mobile devices by remote processing

Peng

¹

,

Pedram

²

2003

Proceedings of the 40th Annual Design Automation Conference

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This paper addresses the problem of extending the lifetime of a batterypowered mobile host in a client-server wireless network by using task migration and remote processing. This problem is solved by first constructing a stochastic model of the client-server system based on the theory of continuous-time Markovian decision processes. Next the dynamic power management problem with task migration is formulated as a policy optimization problem and solved exactly by using a linear programming approach. Based on the off-line optimal policy derived in this way, an on-line adaptive policy is proposed, which dynamically monitors the channel conditions and the server behavior and adopts a client-side power management policy with task migration that results in optimum energy consumption in the client. Experimental results demonstrate that the proposed method outperforms existing heuristic methods by as much as 35% in terms of the overall energy savings.

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“…Many key applications running on mobile platforms can benefit from task migration and remote processing. These applications include image processing, e.g., target detection and recognition used in robot control [1], voice recognition [2], and large-scale numerical computations [3]. The effectiveness of the remote processing technique is limited by the fact that data transmission over wireless channel results in additional power consumption.…”

Section: Introductionmentioning

confidence: 99%

Extending the lifetime of a network of battery-powered mobile devices by remote processing: a Markovian decision-based approach

Rong¹,

Pedram²

Proceedings 2003. Design Automation Conference (IEEE Cat. No.03CH37451)

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This paper addresses the problem of extending the lifetime of a batterypowered mobile host in a client-server wireless network by using task migration and remote processing. This problem is solved by first constructing a stochastic model of the client-server system based on the theory of continuous-time Markovian decision processes. Next the dynamic power management problem with task migration is formulated as a policy optimization problem and solved exactly by using a linear programming approach. Based on the off-line optimal policy derived in this way, an on-line adaptive policy is proposed, which dynamically monitors the channel conditions and the server behavior and adopts a client-side power management policy with task migration that results in optimum energy consumption in the client. Experimental results demonstrate that the proposed method outperforms existing heuristic methods by as much as 35% in terms of the overall energy savings.

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System design and power optimization for mobile computers

Cited by 22 publications

References 6 publications

A Markovian Decision-Based Approach for Extending the Lifetime of a Network of Battery-Powered Mobile Devices by Remote Processing

A Markovian Decision-Based Approach for Extending the Lifetime of a Network of Battery-Powered Mobile Devices by Remote Processing

Extending the lifetime of a network of battery-powered mobile devices by remote processing

Extending the lifetime of a network of battery-powered mobile devices by remote processing: a Markovian decision-based approach

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