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Cloud Computing for Mobile Users: Can Offloading Computation Save Energy?
The cloud heralds a new era of computing where application services are provided through the Internet. Cloud computing can enhance the computing capability of mobile systems, but is it the ultimate…
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A Survey of Computation Offloading for Mobile Systems
TLDR
An overview of the background, techniques, systems, and research areas for offloading computation is provided, and directions for future research are described.
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Comparing System-Level Power Management Policies
TLDR
This work implemented existing power management policies and quantitatively compared their effects on power saving and performance degradation, using DPM specifically for shutting down unused I/O devices.
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A case study of mobile robot's energy consumption and conservation techniques
TLDR
A case study of a mobile robot called Pioneer 3DX is presented, which shows that motion consume less than 50% power on average, and two energy-conservation techniques are introduced: dynamic power management and real-time scheduling.
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Dynamic power management for nonstationary service requests
TLDR
This paper presents two methods for characterizing non stationary service requests by means of a prediction scheme based on sliding windows, and describes how control policies for nonstationary models can be derived.
View on ACM
Real-time moving object recognition and tracking using computation offloading
TLDR
This paper estimates the computation and communication needed for the tasks and chooses to execute them on robot processors or servers to minimize the total execution time, in order to satisfy real-time constraints.
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Sensor replacement using mobile robots
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Quantitative comparison of power management algorithms
TLDR
A framework in Windows NT is built to implement power managers running realistic workloads and directly interacting with users and defines performance degradation that reflects user perception.
View on ACM
Resource Allocation for Real-Time Tasks Using Cloud Computing
TLDR
This paper proposes a polynomial-time solution to allocate resources for real-time tasks using the "Infrastructure as a Service" model offered by cloud computing and compares the economic costs and performance provided by the solution with the optimal solution and an EDF method.
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Deployment of mobile robots with energy and timing constraints
TLDR
A speed-management method is proposed to decide the traveling speeds to maximize the traveling distance under both energy and timing constraints, and a approach to consider areas with random obstacles is provided.
View on IEEE
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