1,597 research outputs found
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
Joint Data compression and Computation offloading in Hierarchical Fog-Cloud Systems
Data compression has the potential to significantly improve the computation
offloading performance in hierarchical fog-cloud systems. However, it remains
unknown how to optimally determine the compression ratio jointly with the
computation offloading decisions and the resource allocation. This joint
optimization problem is studied in the current paper where we aim to minimize
the maximum weighted energy and service delay cost (WEDC) of all users. First,
we consider a scenario where data compression is performed only at the mobile
users. We prove that the optimal offloading decisions have a threshold
structure. Moreover, a novel three-step approach employing convexification
techniques is developed to optimize the compression ratios and the resource
allocation. Then, we address the more general design where data compression is
performed at both the mobile users and the fog server. We propose three
efficient algorithms to overcome the strong coupling between the offloading
decisions and resource allocation. We show that the proposed optimal algorithm
for data compression at only the mobile users can reduce the WEDC by a few
hundred percent compared to computation offloading strategies that do not
leverage data compression or use sub-optimal optimization approaches. Besides,
the proposed algorithms for additional data compression at the fog server can
further reduce the WEDC
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