A survey of task allocation and load balance in distributed system阅读笔记

最新推荐文章于 2021-03-20 22:05:41 发布
原创 最新推荐文章于 2021-03-20 22:05:41 发布 · 442 阅读 · 0 ·
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#分布式

本次分享主要围绕实验室老师分配的一篇英文文献展开,该文献作为分布式系统研究的基础,旨在引导深入理解并整理关键知识点。

实验室的老师扔给我一篇英文文献，作为毕设对分布式系统研究的开端要求仔细阅读并做好阅读笔记，顺便放在博客里方便以后查阅。
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[文献阅读] A formal analysis and taxonomy of task allocation in multi-robot systems（Gerkey)
luzaijiaoxia0618的博客
03-18 1033
前言       Gerkey B P, Matarić M J. A formal analysis and taxonomy of task allocation in multi-robot systems[J]. The International Journal of Robotics Research, 2004, 23(9): 939-954. 该文章提...
【论文笔记】Applications of Deep Reinforcement Learning in Communications and Networking: A Survey
观林望实的博客
06-30 3934
摘要 本文综述了深度强化学习（DRL）在通信和网络中的应用。现代网络中的实体需要在网络环境不确定的情况下在本地进行决策，以使网络性能最大化。强化学习被有效地用于使网络实体在状态空间和行为空间较小的情况下，获得动作的最优选择。然而，在复杂的大规模网络中，状态空间和动作空间往往很大，强化学习可能无法在合理的时间内找到最优策略。因此，发展强化学习与深度学习相结合的DRL来克服这一不足。在本次调查中，我们首先提供了从基本概念到高级模型的DRL教程。然后，我们回顾了为解决通信和网络中的新问题而提出的DRL方法。这些
A Survey of Task Allocation and Load Balancing
weixin_33811539的博客
12-27 331
A Survey of Task Allocation and Load Balancing in Distributed System Part 1. Overview on Distributed Systems the general characteristics of real distributed system lack...
tensorflow 使用遇到问题及解决办法
热门推荐
LaineGates的专栏
01-04 1万+
变量消耗显存不多却显存不足UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a large amount of memory. "Converting sparse IndexedSlices to a dense Tensor of unknown s
Tensorflow遇到的问题
qwexdl的博客
03-20 1991
一、初级 Failed precondition: Attempting to use uninitialized value decoder/num_blocks_0/vanilla_attention/dense/bias Cannot interpret feed_dict key as Tensor: Tensor Tensor(“Placeholder:0”, shape=(?, 15, 50), dtype=int32) is not an element of this graph. ...
流水的NLP铁打的NER：命名实体识别实践与探索
喜欢打酱油的老鸟
08-07 1662
前言 最近在做命名实体识别（Named Entity Recognition, NER）的工作，也就是序列标注（Sequence Tagging），老 NLP task 了，就是从一段文本中抽取到找到任何你想要的东西，可能是某个字，某个词，或者某个短语 为什么说流水的NLP铁打的NER？NLP四大任务嘛，分类、生成、序列标注、句子对标注。分类任务，面太广了，万物皆可分类，各种方法层出不穷；句子对标注，经常是体现人工智能（zhang）对人类语言理解能力的标准秤，孪生网络、DSSM、ESIM 各种模型一年年
rnn_for_mnist
Saker__的博客
03-02 395
Tensorflow中的实现 tensorflow支持RNN各种变体，可以从tf.contrib.rnn模块中找到这些变体，比如tf.contrib.rnn.GRUCell。 借助于tf.nn.dynamic_rnn 我们可以直接定义RNN网络 import tensorflow as tf import numpy as np from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_s
Ant Colony Optimization for Task Allocation in Multi-Agent Systems
02-09
The important features of an agent system such as the latency of the network infrastructure, dynamic topology, and node heterogeneity impose new challenges on the task allocation in Multi-Agent ...
Multi-robot task allocation based on improved market mechanism and anxiety conception
02-09
Multi-robot task allocation based on improved market mechanism and anxiety conception is proposed for occasion problem of task announcement in the process of task allocation. We can reasonably ...
RNN_for_twitter
Saker__的博客
02-26 324
import pandas as pd import numpy as np data = pd.read_csv('Tweets.csv') data = data[['airline_sentiment','text']] with open('twee', 'a', encoding = 'utf-8') as f: for string in data.text: f.writelines(string+'\n') from gensim.models import
tensorflow错误总结
Keith
03-14 8408
bias = tf.get_variable("bias", shape=[out_channels], initializer=tf.zeros_initializer()) 中tf.zeros_initializer一定要加括号，不然会报错，因为tf.zeros_initializer是个类，加了括号表示创建了这个类的一个对象。 -
mask rcnn训练 keyerror(0)
weixin_41685316的博客
11-18 4282
一个问题 坑了两天 解决不了 训练数据：1280*1856 # Train the head branches # Passing layers="heads" freezes all layers except the head # layers. You can also pass a regular expression to select # which layers to train...
Mask R-CNN 训练自己的数据集（balloon过程+报错解释）
刘大鱼的博客
03-11 1万+
因项目需要，识别带有多边形标注的图像，舍弃了速度快精度也高的yolov3，使用Mask R-CNN网络。在摸爬滚打中查找资料修改代码以及不断地调整训练集，途中踩了不少坑，终于达到预期的效果。 一、配置说明 以下是本人跑Mask R-CNN的配置说明： Python 3.52 tensorflow 1.12.0 keras 2.2.4 Jupyter Notebook 一块gpu 二、标注训练集...
基于TensorFlow的循环神经网络(RNN)
H114754726的博客
04-19 587
RNN适用场景 循环神经网络(Recurrent Neural Network)适合处理和预测时序数据 RNN的特点 RNN的隐藏层之间的节点是有连接的，他的输入是输入层的输出向量.extend(上一时刻隐藏层的状态向量)。 demo：单层全连接网络作为循环体的RNN 输入层维度：x 隐藏层维度：h 每个循环体的输入大小为：x+h 每个循环体的输出大小为：h 循环体的输出有两个用途： ...
Tensorflow rnn-word2vec-电影评论
weixin_33720078的博客
01-07 309
import pandas as pd import re import numpy as np import os 复制代码from gensim.models import word2vec 复制代码data_t = pd.read_csv('labeledTrainData.tsv',sep='\t') 复制代码data_t.shape 复制代码(25000, 3) 复制代码 if n...
xs128单片机遇到问题及解决办法
奋斗吧，少年！
09-01 3383
参加飞思卡尔车赛用的是X128单片机，当时很多时候好好的程序突然下载不进去了
周四回来要做的事情
kelly若的博客
06-18 808
(tensorflowgpu) D:\Download-of-Chrome\Faster-RCNN-TensorFlow-Python3.5-master>python train.py Loaded dataset `voc_2007_trainval` for training Set proposal method: gt Appending horizontally-flipped ...
the memory allocation and deallocation function of shall not be used
最新发布
08-29
The references do not directly mention memory allocation and deallocation functions that should not be used. However, in general programming practices, some functions may be considered less favorable in certain scenarios. For example, in C++, while `malloc` and `free` from the C standard library can be used for memory allocation and deallocation, they are generally not recommended when working with C++ classes and objects. `malloc` only allocates raw memory and does not call the constructor of the object, and `free` only deallocates the memory without calling the destructor. In contrast, `new` and `delete` (or `new[]` and `delete[]`) in C++ are designed to handle object construction and destruction properly. Using `malloc` and `free` for C++ objects can lead to issues such as uninitialized objects and memory leaks due to non - called destructors [^1][^3]. Another aspect is that using `new` and `delete` inappropriately can also cause problems. For instance, if you allocate an array with `new[]`, you must use `delete[]` to deallocate it. Using `delete` instead of `delete[]` when dealing with an array allocated by `new[]` can result in undefined behavior, typically memory leaks and potential crashes [^3]. ```cpp #include <iostream> class MyClass { public: MyClass() { std::cout << "Constructor called" << std::endl; } ~MyClass() { std::cout << "Destructor called" << std::endl; } }; int main() { // Bad practice: using malloc for a C++ object MyClass* obj = (MyClass*)malloc(sizeof(MyClass)); // Constructor is not called here free(obj); // Destructor is not called here // Correct way in C++ MyClass* obj2 = new MyClass(); delete obj2; // Bad practice: incorrect deallocation of array MyClass* arr = new MyClass[5]; delete arr; // Should be delete[] arr; return 0; } ```
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