753 research outputs found
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
Computation Offloading and Resource Allocation in Mixed Fog/Cloud Computing Systems with Min-Max Fairness Guarantee
Cooperation between the fog and the cloud in mobile
cloud computing environments could offer improved offloading
services to smart mobile user equipment (UE) with computation
intensive tasks. In this paper, we tackle the computation offloading
problem in a mixed fog/cloud system by jointly optimizing
the offloading decisions and the allocation of computation resource,
transmit power and radio bandwidth, while guaranteeing
user fairness and maximum tolerable delay. This optimization
problem is formulated to minimize the maximal weighted cost
of delay and energy consumption (EC) among all UEs, which
is a mixed-integer non-linear programming problem. Due to
the NP-hardness of the problem, we propose a low-complexity
suboptimal algorithm to solve it, where the offloading decisions
are obtained via semidefinite relaxation and randomization and
the resource allocation is obtained using fractional programming
theory and Lagrangian dual decomposition. Simulation results
are presented to verify the convergence performance of our
proposed algorithms and their achieved fairness among UEs, and
the performance gains in terms of delay, EC and the number of
beneficial UEs over existing algorithms
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