Cross-edge Computation Offloading

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.

Quick Start

Code Structure

• 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.

• directory dataset:
  This directory contains the real-life dataset of base stations in Melbourne CBD area.

• 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.

Results