A python package of Cross-edge Computation Offloading (CCO) algorithm and its distributed version, Decor for Mobile Edge Computing (MEC).
This package is an implementation of the proposed offloading algorithm for mobility-aware computation-intensive partitionable applications. Specifically, for a non-convex edge site-selection sub-problem, we propose a Sampling-and-Classification-based (SAC) algorithm to obtain the near optimal solution. Based on Lyapunov optimization CCO algorithm is proposed to jointly determine edge site-selection and energy harvesting without a priori knowledge. The transmission, execution and coordination cost, as well as the penalty for task failure, are chosen as performace metrics.
Citation:
[The corresponding paper is under review now. Leave this empty temporarily.]
Used dataset:
EUA dataset @ https://github.com/swinedge/eua-dataset/.
Referenced Code:
RACOS @ https://github.com/eyounx/RACOS.
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python package racos: This package contains the algorithm named Racos, which is a specific algorithm based on Sampling-And-Classification (SAC) Framework. Details can be found at http://lamda.nju.edu.cn/yuy/research_sal.ashx.
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directory dataset: This directory contains the real-life dataset of base stations in Melbourne CBD area.
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python package cross_edge_offloading: This package contains the algorithms proposed in our work. The package named cco is the implementation of main algorithm. The package named benchmarks contains three benchmark policies for comparsion.
