2,482 research outputs found
Offloading in Software Defined Network at Edge with Information Asymmetry: A Contract Theoretical Approach
The proliferation of highly capable mobile devices such as smartphones and
tablets has significantly increased the demand for wireless access. Software
defined network (SDN) at edge is viewed as one promising technology to simplify
the traffic offloading process for current wireless networks. In this paper, we
investigate the incentive problem in SDN-at-edge of how to motivate a third
party access points (APs) such as WiFi and smallcells to offload traffic for
the central base stations (BSs). The APs will only admit the traffic from the
BS under the precondition that their own traffic demand is satisfied. Under the
information asymmetry that the APs know more about own traffic demands, the BS
needs to distribute the payment in accordance with the APs' idle capacity to
maintain a compatible incentive. First, we apply a contract-theoretic approach
to model and analyze the service trading between the BS and APs. Furthermore,
other two incentive mechanisms: optimal discrimination contract and linear
pricing contract are introduced to serve as the comparisons of the anti adverse
selection contract. Finally, the simulation results show that the contract can
effectively incentivize APs' participation and offload the cellular network
traffic. Furthermore, the anti adverse selection contract achieves the optimal
outcome under the information asymmetry scenario.Comment: 10 pages, 9 figure
Mobile, collaborative augmented reality using cloudlets
The evolution in mobile applications to support advanced interactivity and demanding multimedia features is still ongoing. Novel application concepts (e.g. mobile Augmented Reality (AR)) are however hindered by the inherently limited resources available on mobile platforms (not withstanding the dramatic performance increases of mobile hardware). Offloading resource intensive application components to the cloud, also known as "cyber foraging", has proven to be a valuable solution in a variety of scenarios. However, also for collaborative scenarios, in which data together with its processing are shared between multiple users, this offloading concept is highly promising. In this paper, we investigate the challenges posed by offloading collaborative mobile applications. We present a middleware platform capable of autonomously deploying software components to minimize average CPU load, while guaranteeing smooth collaboration. As a use case, we present and evaluate a collaborative AR application, offering interaction between users, the physical environment as well as with the virtual objects superimposed on this physical environment
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
Hyperprofile-based Computation Offloading for Mobile Edge Networks
In recent studies, researchers have developed various computation offloading
frameworks for bringing cloud services closer to the user via edge networks.
Specifically, an edge device needs to offload computationally intensive tasks
because of energy and processing constraints. These constraints present the
challenge of identifying which edge nodes should receive tasks to reduce
overall resource consumption. We propose a unique solution to this problem
which incorporates elements from Knowledge-Defined Networking (KDN) to make
intelligent predictions about offloading costs based on historical data. Each
server instance can be represented in a multidimensional feature space where
each dimension corresponds to a predicted metric. We compute features for a
"hyperprofile" and position nodes based on the predicted costs of offloading a
particular task. We then perform a k-Nearest Neighbor (kNN) query within the
hyperprofile to select nodes for offloading computation. This paper formalizes
our hyperprofile-based solution and explores the viability of using machine
learning (ML) techniques to predict metrics useful for computation offloading.
We also investigate the effects of using different distance metrics for the
queries. Our results show various network metrics can be modeled accurately
with regression, and there are circumstances where kNN queries using Euclidean
distance as opposed to rectilinear distance is more favorable.Comment: 5 pages, NSF REU Site publicatio
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