127 research outputs found
Improving Mobile Video Streaming with Mobility Prediction and Prefetching in Integrated Cellular-WiFi Networks
We present and evaluate a procedure that utilizes mobility and throughput
prediction to prefetch video streaming data in integrated cellular and WiFi
networks. The effective integration of such heterogeneous wireless technologies
will be significant for supporting high performance and energy efficient video
streaming in ubiquitous networking environments. Our evaluation is based on
trace-driven simulation considering empirical measurements and shows how
various system parameters influence the performance, in terms of the number of
paused video frames and the energy consumption; these parameters include the
number of video streams, the mobile, WiFi, and ADSL backhaul throughput, and
the number of WiFi hotspots. Also, we assess the procedure's robustness to time
and throughput variability. Finally, we present our initial prototype that
implements the proposed approach.Comment: 7 pages, 15 figure
Live Prefetching for Mobile Computation Offloading
The conventional designs of mobile computation offloading fetch user-specific
data to the cloud prior to computing, called offline prefetching. However, this
approach can potentially result in excessive fetching of large volumes of data
and cause heavy loads on radio-access networks. To solve this problem, the
novel technique of live prefetching is proposed in this paper that seamlessly
integrates the task-level computation prediction and prefetching within the
cloud-computing process of a large program with numerous tasks. The technique
avoids excessive fetching but retains the feature of leveraging prediction to
reduce the program runtime and mobile transmission energy. By modeling the
tasks in an offloaded program as a stochastic sequence, stochastic optimization
is applied to design fetching policies to minimize mobile energy consumption
under a deadline constraint. The policies enable real-time control of the
prefetched-data sizes of candidates for future tasks. For slow fading, the
optimal policy is derived and shown to have a threshold-based structure,
selecting candidate tasks for prefetching and controlling their prefetched data
based on their likelihoods. The result is extended to design close-to-optimal
prefetching policies to fast fading channels. Compared with fetching without
prediction, live prefetching is shown theoretically to always achieve reduction
on mobile energy consumption.Comment: To appear in IEEE Trans. on Wireless Communicatio
Wi-Fi Offload: Tragedy of the Commons or Land of Milk and Honey?
Fueled by its recent success in provisioning on-site wireless Internet
access, Wi-Fi is currently perceived as the best positioned technology for
pervasive mobile macro network offloading. However, the broad transitions of
multiple collocated operators towards this new paradigm may result in fierce
competition for the common unlicensed spectrum at hand. In this light, our
paper game-theoretically dissects market convergence scenarios by assessing the
competition between providers in terms of network performance, capacity
constraints, cost reductions, and revenue prospects. We will closely compare
the prospects and strategic positioning of fixed line operators offering Wi-Fi
services with respect to competing mobile network operators utilizing
unlicensed spectrum. Our results highlight important dependencies upon
inter-operator collaboration models, and more importantly, upon the ratio
between backhaul and Wi-Fi access bit-rates. Furthermore, our investigation of
medium- to long-term convergence scenarios indicates that a rethinking of
control measures targeting the large-scale monetization of unlicensed spectrum
may be required, as otherwise the used free bands may become subject to
tragedy-of-commons type of problems.Comment: Workshop on Spectrum Sharing Strategies for Wireless Broadband
Services, IEEE PIMRC'13, to appear 201
Efficient Mobile Edge Computing for Mobile Internet of Thing in 5G Networks
We study the off-line efficient mobile edge computing (EMEC) problem for a joint computing to process a task both locally and remotely with the objective of minimizing the finishing time. When computing remotely, the time will include the communication and computing time. We first describe the time model, formulate EMEC, prove NP-completeness of EMEC, and show the lower bound. We then provide an integer linear programming (ILP) based algorithm to achieve the optimal solution and give results for small-scale cases. A fully polynomial-time approximation scheme (FPTAS), named Approximation Partition (AP), is provided through converting ILP to the subset sum problem. Numerical results show that both the total data length and the movement have great impact on the time for mobile edge computing. Numerical results also demonstrate that our AP algorithm obtain the finishing time, which is close to the optimal solution
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