Saving portable computer battery power through remote process execution

Руденко, А. Е.; Reiher, Peter; Popek, Gerald J.; Kuenning, Geoff

doi:10.1145/584007.584008

Cited by 238 publications

(113 citation statements)

References 8 publications

(11 reference statements)

Supporting

Mentioning

112

Contrasting

Order By: Relevance

“…Plenty of research has been conducted to outsource computing tasks to external computing sources [10,18,26,11]. Typically, these schemes focus on how to properly split the job and deploy on the external computing sources.…”

Section: Related Workmentioning

confidence: 99%

“…For example, studies [28,12] demonstrate the ability to partition the application and associate classes and thus outsourcing them. Rudenko et al [26] suggest that if the total energy cost of sending the task else where and receiving the result back is lower than the cost of running it locally, then remote process execution can save battery power. Flinn et al [18] also propose a similar idea, in which remote execution simultaneously leverages the mobility of mobile devices and the richer resources of large devices.…”

Section: Related Workmentioning

confidence: 99%

“…From the resource perspective, a lots of research have considered to outsource computing intensive tasks to external resources [10,18,26]. For example, the virtual machine-based cloning approach [12] has been explored to clone the entire mobile environment to the cloud without worrying about modifying the application or dividing the job.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Mobile MapReduce: Minimizing Response Time of Computing Intensive Mobile Applications

Hassan

Chen

2012

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

View full text Add to dashboard Cite

Abstract. The increasing popularity of mobile devices calls for effective execution of mobile applications. A lot of research has been conducted on properly splitting and outsourcing computing intensive tasks to external resources (e.g., public clouds) by considering insufficient computing resources on mobile devices. However, little attention has been paid to the overall users' response time, where the network may dominate. In this study, we set to investigate how to effectively minimize users' response time for mobile applications. We consider both the impact of the network and the computing itself. We first show that outsourcing to nearby residential computers may be more advantageous than public clouds for mobile applications due to network impact. Furthermore, to speed up computing, we leverage parallel processing techniques. Accordingly, we propose to build Mobile MapReduce (MMR) to effectively execute outsource computing intensive mobile applications. Based on the original MapReduce framework, a new scheduling model is built in MMR that can always leverage the best computing resources to conduct computation with appropriate parallel processing. To demonstrate the performance of MMR, we run several real-world applications, such as text searching, face detection, and image processing, on the prototype. The results show great potentials of MMR in minimizing the response time of the outsourced mobile applications.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Mobile MapReduce: Minimizing Response Time of Computing Intensive Mobile Applications

Hassan

Chen

2012

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

View full text Add to dashboard Cite

show abstract

“…To address the fundamental challenge posed by the limited on-device resources for computing-intensive tasks, plenty of research has been conducted on designing various job partitioning/outsourcing strategies [7,12,15] to outsource mobile computing tasks to external sources. Today external computing sources are widely available, such as public clouds or nearby surrogate computers.…”

Section: Introductionmentioning

confidence: 99%

An Investigation of Different Computing Sources for Mobile Application Outsourcing on the Road

Hassan

Chen

2012

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

View full text Add to dashboard Cite

Abstract. Mobile applications are growing fast due to pervasive usage of mobile devices. With inherently limited on-device resources, plenty of research has been conducted on job partitioning/outsourcing strategies to execute mobile computing tasks on external sources, such as public clouds or nearby computers. However, little is known about the performance difference to mobile users on these external computing sources. In this paper, considering the user's response time and the battery power consumption on mobile devices, we first show that outsourcing mobile applications to public clouds may not outperform outsourcing to nearby residential computers, particularly for delay sensitive applications. To facilitate efficient mobile outsourcing to residential computers, we propose to build a framework RoseMic (ROad-SidE-MobIle-Computing). In RoseMic, a resource overlay network is built with users' idle residential (home) computers. To encourage the sharing of idle residential computers, RoseMic also includes a credit based incentive mechanism that can be enforced automatically without users' interferences in order to defeat collusion attacks. To demonstrate the performance of RoseMic, we run several real-world applications. The results show that RoseMic outperforms Amazon EC2 by 3 times and 4 times on average in terms of response time and the battery power consumption, respectively.

show abstract

“…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)

View full text Add to dashboard Cite

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.

show abstract

Saving portable computer battery power through remote process execution

Cited by 238 publications

References 8 publications

Mobile MapReduce: Minimizing Response Time of Computing Intensive Mobile Applications

Mobile MapReduce: Minimizing Response Time of Computing Intensive Mobile Applications

An Investigation of Different Computing Sources for Mobile Application Outsourcing on the Road

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

Contact Info

Product

Resources

About