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awesome-cpp icon awesome-cpp

A curated list of awesome C++ (or C) frameworks, libraries, resources, and shiny things. Inspired by awesome-... stuff.

cache-allocation-project icon cache-allocation-project

The purpose of this project is to implement machine learning methods to study resource allocation problems, that is how to share limited resources out among several agents.

caching-algorithm icon caching-algorithm

In this project, you will design a caching algorithm for your users to improve user experience by reducing delay and also to save bandwidth for your organization. In this scenario, there are 100 files with varying popularity and sizes. Users request these files based on their popularity.

codedcaching icon codedcaching

A Computational Approach to Derive Outer Bound for Communication Systems and Data Stoarge Systems

cyc2018 icon cyc2018

【转】技术面试必备基础知识、Leetcode 题解、后端面试、Java 面试、春招、秋招、操作系统、计算机网络、系统设计

ddlo icon ddlo

Distributed Deep Learning-based Offloading for Mobile Edge Computing Networks

distributedservercache icon distributedservercache

[Operational Research - Bee algorithm] University project of distributed server caching algorithm

drl icon drl

Deep Reinforcement Learning for Offloading and Resource Allocation in Vehicle Edge Computing and Networks

edge-offloading icon edge-offloading

computation offloading in mobile edge computation using Reinforcement Learning

energy-aware-compute-strategy icon energy-aware-compute-strategy

The growth of data volume and the popularity of mobile devices have spawned a large number of low-latency, high-computing applications. At the same time, under the wave of global new energy, the energy efficiency utilization of electronic equipment has also received more and more attention. Due to a large delay and occupying the large bandwidth of cloud computing, and the limited computing power of end devices, mobile edge computing is considered to be the best way to achieve low latency of complex tasks. Wifi routers can provide high bandwidth and can work as intermediate nodes for forwarding tasks. In this report, we propose to use dynamic programming based on wifi routers to dynamically schedule computing tasks to edge computing nodes to achieve maximum bandwidth utilization, under the energy consumption constraint of the user. The experiment result shows that the DP method compared the with other three traditional algorithms can get higher bandwidth by using less Energy cost and fewer numbers workers. Besides, we can effectively search for the last phase Table to save much time by using Computation Reusability.

free icon free

翻墙、免费翻墙、免费科学上网、免费节点、免费梯子、免费ss/ssr/v2ray/trojan节点、蓝灯、谷歌商店、翻墙梯子

leetcode-master icon leetcode-master

《代码随想录》LeetCode 刷题攻略:200道经典题目刷题顺序,共60w字的详细图解,视频难点剖析,50余张思维导图,支持C++,Java,Python,Go,JavaScript等多语言版本,从此算法学习不再迷茫!🔥🔥 来看看,你会发现相见恨晚!🚀

markdown-resume-template icon markdown-resume-template

BAT程序员自己的简历模板分享出来了 。技术简历追求简单明了,避免没有必要的花哨修饰,大家可以fork到自己仓库中,基于这个模板进行修改。

mec_offloading icon mec_offloading

Code for Intelligent Dynamic Data Offloading in a Competitive Mobile Edge Computing Market paper https://www.mdpi.com/1999-5903/11/5/118/pdf

ml-rl-simulations icon ml-rl-simulations

A multi-layer guided reinforcement learning-based tasks offloading in edge computing - Simulations

my_mec_program icon my_mec_program

I build this Mobile Edge Computation simulating environment all by myself, and use the costomized ddpg reinforcement learning algorithm to make offloading decision.

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