迈向云应用：第三版全方位指南

Azure
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《移动应用到云端（第三版）》是由Dominic Betts、Alex Homer、Alejandro Jezierski、Masashi Narumoto和Hanz Zhang共同编著的一本专著，旨在帮助读者理解和实践将应用程序迁移到Microsoft Windows Azure™平台的过程。本书是针对技术专业人士和开发者的实用指南，特别是那些希望利用云技术扩展其业务或优化IT架构的人。 该书第三版更新了关于Azure的内容，这是在Microsoft的云计算服务平台上，涵盖了Azure的多个关键组件如Active Directory、MSDN、SQL Server、Visual C#、Visual C++、Visual Basic、Visual Studio等。通过深入浅出的方式，作者们提供了详细的指导，包括但不限于如何设计、迁移和管理云应用，以及如何利用Windows Azure SQL Database和Windows Live等服务进行数据存储和社交集成。 书中还强调了实际操作中的注意事项，如文档可能未经通知就发生变更，提供的示例仅用于演示，并非真实的商业案例。版权方面，所有内容受Microsoft集团商标保护，同时尊重其他相关公司的知识产权。 全书共分为以下几个部分： 1. **序言 - Yousef Khalidi**: 由本书序言作者对第三版的重要性和背景进行了概述，可能提及技术趋势和发展。 2. **第三版序言**：着重介绍了新版本的更新内容，可能包括对Azure平台的新功能介绍，以及与前一版相比的改进之处。 3. **本书适用对象**：明确指出本书的目标读者，包括初级到高级的开发者、IT管理员和架构师，以及对云计算有兴趣的商业用户。 4. **前言**：概括性地阐述了写作目的、预期目标和读者可以从中获得的知识收益。 《移动应用到云端（第三版）》不仅是一本技术手册，也是一本实战指南，提供了丰富的实践案例和最佳实践，帮助读者在迁移过程中克服挑战，实现应用程序在云端的高效运行。随着云计算的日益普及和Azure的持续发展，这本书对于IT专业人士来说是一份不可多得的参考资料。

Moving Hadoop to The Cloud epub

2017-10-01 上传
Moving Hadoop to The Cloud 英文epub 本资源转载自网络，如有侵权，请联系上传者或csdn删除 本资源转载自网络，如有侵权，请联系上传者或csdn删除

Moving Hadoop to the Cloud

2017-09-14 上传
Chapter 1 Why Hadoop in the Cloud? Chapter 2 Overview and Comparison of Cloud Providers Chapter 3 Instances Chapter 4 Networking and Security Chapter 5 Storage Chapter 6 Setting Up in AWS Chapter 7 ...

The starting configuration of this puzzle is a row of cells, with disks located on cells through . The goal is to move the disks to the end of the row using a constrained set of actions. At each step, a disk can only be moved to an adjacent empty cell, or to an empty cell two spaces away if another disk is located on the intervening square. Given these restrictions, it can be seen that in many cases, no movements will be possible for the majority of the disks. For example, from the starting position, the only two options are to move the last disk from cell to cell , or to move the second-to-last disk from cell to cell . 1. [15 points] Write a function solve_identical_disks(length, n) that returns an optimal solution to the above problem as a list of moves, where length is the number of cells in the row and n is the number of disks. Each move in the solution should be a twoelement tuple of the form (from, to) indicating a disk movement from the cell from to the cell to. As suggested by its name, this function should treat all disks as being identical. Your solver for this problem should be implemented using a breadth-first graph search. The exact solution produced is not important, as long as it is of minimal length. Unlike in the previous two sections, no requirement is made with regards to the manner in which puzzle configurations are represented. Before you begin, think carefully about which data structures might be best suited for the problem, as this choice may affect the efficiency of your search

2023-06-06 上传
The `successors` function generates all possible successor configurations for a given configuration by moving a disk to an adjacent empty cell or to an empty cell two spaces away. The `queue` ...

Warning: 'https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/pytorch/' already in 'channels' list, moving to the top

2023-09-26 上传
这个警告信息说明您已经在Anaconda的channels列表中添加了https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/pytorch/，并且将其移动到了列表的顶部。这通常是由于重复加载或者之前安装过程中出现的问题导致的。...

Consider a moving average filter with the following difference equation: y[n] = 1/25(x[n-24] + x[n-23] + ... + x[n-1] + x[n]) Use the MATLAB function ‘filter()’ to apply this filter to the data. Write down the LAST 10 outputs (to 4 decimal places) in the filtered data. Then, plot the original data and the filtered data on the same figure.

2023-05-25 上传
Assuming the input data is stored in a vector called "x", the following code can be used to apply the filter and obtain the last 10 outputs: ```matlab b = ones(25,1)/25; a = 1; y = filter(b,a,x); ...

Write a function with the declaration: smoothed=rectFilt(x,width). The filter should take a vector of noisy data (x) and smooth it by doing a symmetric moving average with a window of the specified width. For example if width=5, then smoothed(n) should equal mean(x(n-2:n+2)). Note that you may have problems around the edges: when n<3 and n>length(x)-2. a. The lengths of x and smoothed should be equal. b. For symmetry to work, make sure that width is odd. If it isn’t, increase it by 1 to make it odd and display a warning, but still do the smoothing. c. Make sure you correct edge effects so that the smoothed function doesn’t deviate from the original at the start or the end. Also make sure you don’t have any horizontal offset between the smoothed function and the original (since we’re using a symmetric moving average, the smoothed values should lie on top of the original data). d. You can do this using a loop and mean (which should be easy but may be slow), or more efficiently by using conv (if you are familiar with convolution). e. Load the mat file called noisyData.mat. It contains a variable x which is a noisy line. Plot the noisy data as well as your smoothed version, like below (a width of 11 was used):

2023-06-07 上传
To use this function on the `noisyData.mat` file and plot the results, you can run the following code: ``` load('noisyData.mat'); % Smooth the data with a width of 11 smoothed = rectFilt(x, 11); % ...

explain why Kelvin waves propagate with the coast on the left in the southern hemisphere?

2023-04-03 上传
This means that the wave will appear to be moving with the coast on the left. This is because the Coriolis effect deflects the water to the left, which creates a pressure gradient that causes the ...

Consider a problem to find the shortest path from a given starting node s to any other nodes in the (undirected) graph. The length of a path in the graph is the number of edges included. Complete the function shortest_path(adj_list, s), which takes the adjacency list of the graph and a starting node as input, and returns a list containing the length of the shortest path from the starting node to all other nodes. Note: If there is no path from the starting node to a node, set the length of shortest path as float('inf'). You can use any Python built-in package to implement the stack/queue/priority queue if needed. Hint: Consider the three graph traversal strategies (DFS, BFS and PFS) discussed in the class. Which one is applicable in this problem? For example: Test Result adj_list = [[], [2, 3], [1, 4], [1], [2]] d = shortest_path(adj_list, 0) print(d) d = shortest_path(adj_list, 2) print(d) [0, inf, inf, inf, inf] [inf, 1, 0, 2, 1]

2023-05-26 上传
The basic idea of BFS is to explore all the vertices at distance 1 from the current vertex before moving on to vertices at distance 2. Here is the implementation of the shortest_path function using...

SQL moving average

2023-07-28 上传
SQL moving average refers to calculating the average of a specific column's values over a specified number of periods. Here is an example of how you can calculate a moving average in SQL: ```sql ...

R.elem[0] = R.elem[low]; pivotkey = R.elem[low].key;

2023-04-03 上传
These two lines of code are ...Overall, these two lines of code are used to set up the initial state of the partitioning process by choosing the pivot element and moving it to the beginning of the array.
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