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Work simulation(原则有先后顺序)
目前两大做题中最重要原则:
1.requirement排在第一,deadline第二。
2.有manager出现的选项无脑选manager,manager就是一个组的地头蛇。
Amazon9条主要原则
原则1:客户是上帝,requirement优先,任何影响上帝的事情都不能干,
        如某个requirement影响了上帝的体验,
        你就是死键盘上也不能砍了,宁愿miss deadline
原则2:为长远考虑,即客户几年之后可能会出现的需求也要考虑到,
        不会为了交付短期的deadline,
        而牺牲长期的价值。(比如 global api  和 local api)
原则3:最高标准,“最高”对应上面的“长远”。
原则4:一般情况,能请示manager就请示manager,manager一般不会出错
原则5:速度很重要,决策和行动都可以改变,因此不需要进行过于广泛的推敲
        ,但提倡在深思熟虑下进行冒险。
原则6:不需要一定要坚持“非我发明”,需求帮助也是可以的,四处寻找创意
        ,并且接受长期被误导的可能. From 1point 3acres bbs
原则7:敢于承担责任,任劳任怨,比如领导说谁会java,你会你就跳出来说我会
原则8:对问题刨根问底,探究细节
原则9:服从大局(团队比个人重要)
打分不是关键,排序才是关键。
大部分情况下其实并没有deadline 和 requirement谁更好,更多还是在
这个组合中你对ddl 和 requirement整体的权衡。
每个选项可以评1~5分,most effective 是5,然后1是least effective
刚开始让你看一些介绍amazon工作环境的视频
1.上来给一段video,场景是项目的晨会,就是把team正在推进的项目描述一下,
期间会有多个项目和你有关系,后面会遇到
2.进入工作界面,可以看到接受到邮件,接收到的instant message
3.进入工作状态。会有同事给你发邮件,发信息。需要你对他们提出的问题做一些判断,也就是给解决问题的选项评分
4.一个21题,有log分析bug,有给报告出问题结论,有判断项目 走向的
情境1:给图书馆写图书推荐系统,关于book api
两个人,在表达不同的观点
选择:tell me more
一开始其实每个人都在强调自己是对的,即使有一个人更对一些,
也应该选tell me more(原则8),选了之后会得到更多信息. 1point3acres
情境2:选图书馆的服务器有没有开放关于实体书的api
两个小哥讨论图书推荐的api应该是自己做还是用现成的。
自己做api覆盖面广,但是due赶不上,别人做的能赶上due。
requirement优先(原则2),tell me more层层递进
情境3:经理说咱们最近服务器老挂,什么情况?
先选看internal bug的记录
选 I think service 3 is the problem,
but I would like to see another report to confirm
烙印,义正言辞说自己做了20年服务器,不可能有错误,
刚刚调试过服务器,不可能是内部错误。
选自己去查,问题的关键在于不要麻烦别人
增加开发过程中测试的时间/测试覆盖更多case,放5
写Manuel test,放3
还有个是unit test,也放3
增加QA的人手,放1
让客户来当小白鼠发现问题,放1
情境4:Amazon recommendation system item,
给你推荐一些你感兴趣的item,第一个issue总是失败,
第二个issue总是显示germany
第一个问题是因为username 太长所以一直报错。
第二个问题是因为他用proxy的name来决定是不是语言了。
情境5:德国amazon除了什么问题,让你看log回答问题。问你大概哪里除了问题
亚马逊推荐广告,给英国人推了德文广告,给你log文件,
问你可能在哪,找bug in error log
情境6:员工们讨论case media network服务器最近好多compliants
有德国的,有invalid recommendation,有返回404,
找出错原因的相同点
德语因为服务器, 一个因为用户名太长,一个是有些用户的语言变成德语
情境7:具体客户ddl 只有两周,两个方案,延到四周,做完整。
另一个说先实现一部分功能做个demo,再慢慢做。
先做demo放5,按部就班四周放3, 通知其他组说两走做不完接着做美国放1
情境8:估计项目开发时间. check 1point3acres for more.
Manager放5,找有经验的人请教4,上网查资料或是先做一段时间再估计都放3,
还有其他裸上的就1。
情境9:一个项目时间表设计
说你是这里最会用什么语言的,比如java
情境10:安排会议
视频会议 5   三个老二开会和老二去找老大开会 3   推迟会议和邮件开会 1
情境11:搞个数据库
两周时间可以搞个数据,ben可以帮忙,大腿priya可以帮,但是要等一周半 报告manager放5,和合作等大腿放4,合作/等大腿是3
自己单干,cut feaure都是1
情境12:系统是否升级
做两个feature,一个让100%用户爽,一个让20%用户爽,
但要升级系统,升级系统自己组会爽,但是升级会推迟做的feature,
不升级吧,升级之后还得做一遍
这题的中心是不升级,先做feature,先让用户爽。
先做100的feature再升级,再做20的feature,放5
不升级,因为我们承诺要做feature,放4。
不升级,要搞定feature,可以以后推了其他ddl再升级,放3
不升级,因为对其他组没影响,我们应该focus在request上面,放2
升级,推迟这两个feature的ddl,因为升级造福子孙后代,放2
升级,不然要做两次,放1
这题的关键在于升不升级,要坚定的站在一边
情境13:新产品设计
给8周时间,选择题,让你pick up 一个features的组合要求利益最大化,
每个feature都有相应的价值,H >> M >> L 都代表远大于
首先ddl是前提,中位数不能超过8太多,那样的话就算feature再多也没意义,
同价值,按照ddl排序,同ddl按照价值排序。
情境17:代码分析
三段一长选最长

Breaking the Mold: Unconventional Strategies for Accelerating Your Career Path

In traditional thinking, the job search process appears to be a clearly defined, strictly regulated path. You are expected to follow a set series of steps: write a resume, submit applications, wait for interview invitations, attend interviews, and finally, await the outcome. However, as competition in the workplace intensifies, especially in the rapidly evolving IT industry, merely following the traditional path may no longer suffice to make you stand out.

The Insight from Utah’s Film Festival

Utah hosts an annual film festival that attracts stars and social elites, and students from Stanford’s MBA program also love to join in the fun. Their professor challenged them: could they find a way to meet these VIPs at the festival? The best way to meet these big names was to attend their private dinners, which required special invitations.

One student managed to do this by emailing the dinner organizers, claiming to be a writer for Forbes magazine and expressing interest in their dinner. Hearing that she represented the media, the organizers immediately replied with an invitation link. At the dinner entrance, she brought a friend along. When the organizers pointed out she hadn’t mentioned bringing an extra person, she simply stated they had both already arrived, leaving the organizers with no choice but to let them both in.

In reality, we’ve all encountered such people. I bet you don’t like these people and certainly don’t want to become one. If that’s the case, you need to update your understanding of breaking the rules.

The Special Benefits of Breaking the Rules

A significant characteristic of civilized society is that most people overly adhere to rules. From a young age, we’re trained to be honest, behave well, not to trouble others, and avoid conflict. Most people don’t know how to handle conflicts with leaders or colleagues, so they tend to avoid conflicts and even arguments, preferring to keep the peace at any cost.

If you’re willing to confront, and the other party isn’t, they might fulfill your request to avoid conflict. As the American saying goes, “It’s easier to ask for forgiveness than permission.” In some cases, breaking the norm and adopting non-traditional methods may be key to workplace success.

Breaking Rules Can Enhance Your Image

The reality is that impolite people, those who disregard others, invade others’ territories, or flout the law are often perceived as more capable.

Breaking Rules is Necessary Because Rules Limit You

One important reason we need to break rules is that sometimes you can’t win without doing so. The underdog needs to engage in “unrestricted warfare.” Many rules are designed to protect the special interests of those in power, essentially limiting your development. Abiding by the rules benefits them. Every revolution is a redistribution of resources. If you wish to share in the benefits, you must change the existing rules.

An Example from a CMU Student

A CMU student, who had not yet graduated and was interning at a company, learned that a senior executive was interested in lecturing at CMU, likely for the prestige. This task was relatively easy for the student, who knew a professor eager to invite the executive to give a lecture.

The student half-jokingly told the executive that if he could secure a lecture opportunity at the university, would the executive help him secure a full-time position with access to the CEO? The executive agreed on the spot. The student easily convinced the professor to offer the lecture opportunity, and he secured the position.

Contact me, and I will teach you how to break the rules and fast-track your success.

NG 面试 System Design,汗流浃背了

以往System Design是社招必考的项目,现在New Grad和Intern的面试也会出现系统设计了。

首先,我们要知道:系统设计面试≠设计系统‼️
系统设计面试是一个交流的过程。

通过讨论看候选人的深度和知识型,因此我们要理解面试官要干嘛?对方问这个问题背后的深意是什么?然后把自己的想法和观点传达给面试官!并且回答要尽可能的贴近工业界的实际情况。

💥问题来了:应当如何准备System Design

对NG来说考核难度不高,没必要看论文,不过业界经典的文章介绍需要看的!每天刷1-2个小时大公司的engineer blog!还有一些youtube的频道。

具体操作:找到onsite公司近两年的面经,刷完所有的System Design题目,找到其工业界实现的blog,读到烂熟,预设面试官会问到的题目,做针对性的mock演练。

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✅大厂在职面试官带你进行Mock Interview实战演练,熟悉不同公司System Design的考核重点、面试流程、面试风格、答题思路!

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CS代考 Assignment 2: Parser and Transpiler

Assignment 2: Parser and Transpiler
Please do not change the names of the functions defined in the Assignment.hs file. Each Part of the assignment has corresponding parseExerciseX and prettyPrintExerciseX that will parse and pretty print the input as per the requirements in that part.
You may (and are highly encouraged) to implement your parsers alongside these pre-defined functions. Running the Code
1 $stacktest

This will generate the transpiled JS files using the sample input JS files, by running your pretty printing
function for each exercise.
Running the Javascript Tests
In the javascript folder run:
All example scripts are stored within javascript/inputs and the output of your parser will be saved in javascript/output .
The tests on the page test:
The generated code is valid JS (i.e. it runs without errors, including non-termination error)
The generated code has certain properties of the original code (e.g. immutable variables are still immutable)
The output is “prettified” from the input based on visual, side-by-side inspection
2 $npmrundev

最近学生PROGRAMHELP抄袭我们网址 请大家擦亮眼睛,谨防诈骗

尊敬的各位用户,我们最近发现一个名为PROGRAMHELP的诈骗网站,无视法律与道德的约束,抄袭cscodehelp的网站内容。这种行为严重侵犯了的版权,同时也对大家的权益构成了直接威胁,(目前已经收集到他们诈骗 用户的15项证据)

博士坚持抵制这样劣币驱逐良币的行为!!!大家加机构以后,一定要试试他们的英文水平!!!

可以看到这个所谓的学长代面,叫javawork的微信。根本不是一个以面试为基础的网址。大家可以看看过去做过的题集,就能看到并没有什么过往的post。

博士坚决反对并谴责任何形式的抄袭行为。原创是我们网站的基石和骄傲,我们将尽全力保护我们的面试代面基础,确保每一位用户的正当权益。同时,我们也提醒各位用户,对于类似的抄袭行为,一定要保持警惕,避免因此受到欺诈的风险。

博士已经向Google法务合作,对这种抄袭行为进行彻底调查,并采取法律手段予以制止。请大家对我们有信心,我们将尽全力维护网站的合法权益,保障大家的利益不受损害。同时,我们也希望大家在发现任何可疑的抄袭行为时,能够及时向我们报告。

同时,博士呼吁所有用户,和博士一起,维护一个公平、公正、充满创新和尊重原创的网络环境。谢谢大家的理解和支持!

警惕 powcoder 诈骗 胡乱开价 挂科 恶性竞争 p图

一直以来客户向我反映被powcoder诈骗,弄挂科,消息不回。金额数目相当之大。

powcoder网站使用爬虫完全抄袭我网站内容. 并且爬取相关资料。以为他只是抄袭。

发现他为了恶性竞争,还截图p图放到晚上。

大家擦亮眼睛,看清楚powcoder真实面目。写paper,做面试做作业之前多问一问,看看这个人到底懂不懂,打个语音聊一聊。

什么找老师,找中介,无法就是欺骗大家的钱财,交付一个差劲的软件。

没想到powcoder自己完全没有软件编程经历,完全是个诈骗犯. 看到大家被骗, 非常痛心.

付款账号到qiushiwenmeng 程雁 大家小心!!!如果你也被他欺骗或者挂科不退款, 请联系我, 可以拉到群里和其它受害者一起维权。实名制社会,咱们总能找到争取回利益。

考试COMP 9007 被诈骗23000RMB

警惕 powcoder 诈骗 胡乱开价 挂科 恶性竞争 p图插图

代面试做题,开价3000美元,未过未退款

警惕 powcoder 诈骗 胡乱开价 挂科 恶性竞争 p图插图1

代考做题,开价9000美元

警惕 powcoder 诈骗 胡乱开价 挂科 恶性竞争 p图插图2

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VO包高质量,通过语音直接转发,完全不是廉价的辅助方式。

今年43个学生通通拿offer。

CODEHELP博士 面试代面 OA代做 远程面试 直接代面 代面试插图
CODEHELP博士 面试代面 OA代做 远程面试 直接代面 代面试插图1

CS考试辅导 FIT3080 Semester 2, 2022 Informed Search – codehelp代写

FIT3080 Semester 2, 2022 Informed Search
Monash University Faculty of Information Technology FIT3080 Week 4 Lab 3: Informed Search
Exercise 1: Algorithm A
Consider the (full) state space below. Indicate (1) the order in which nodes are expanded, and (2) the nodes remaining in memory after finding the goal, for the following search strategies (assume typical left-to-right operator tie breaking):

Copyright By cscodehelp代写 加微信 aplg6666

(a) Depth-First Iterative Deepening
(b) Greedy Best-First Search
Let’s consider a new strategy called Algorithm A. This algorithm can be described as a best-first search which uses the following expansion priority for a node 𝑛:
𝐟 (𝐧) = 𝐠(𝐧) + 𝐡(𝐧)
As usual, 𝑔(𝑛) denotes the cost-so-far (i.e., from the start node to node 𝑛) and h(𝑛) is an estimate of the cost-to-go (i.e., from node n to the goal node). We can instantiate Algorithm A as using the Graph-search or Tree-search framework, provided the following invariant properties for the 𝑔− and h−value function are true:
𝑔(𝑛) >= 𝑔∗(𝑛) h(𝑛) >= 0
(c) Instantiate Algorithm A (as Graph or Tree- search) to solve the above problem.
(d) Is algorithm A the same algorithm as A*?
(e) How can we modify things (or what can we modify) so that Algorithm A behaves like A*?
(f) After the modification, what nodes are expanded by A*?
Exercise 2: Algorithm 𝐴(∗) – Traveling Salesman Problem
A salesperson must visit each of 𝑛 cities exactly once. Assume that there is a road between
each pair of cities. Starting at city 𝐴, find the route of minimal distance that visits each of the cities only once and returns to city 𝐴.

(a) Propose a state space and action space for this problem, explaining clearly under what state conditions certain actions are allowed.
(b) Propose two (non-zero) h functions for this problem. Is either of these h functions admissible (a lower bound of h∗)?
(c) Apply algorithm A with one of these h functions to the following 5 city problem:
Exercise 3: Algorithm 𝐼𝐷𝐴(∗) – 5-puzzle problem
Consider a problem called the 8-puzzle. The problem has the start and goal state as follows. Throughout the question, when doing a search, give value of 𝑓(𝑛), 𝑔(𝑛) and h(𝑛) at each node 𝑛. The estimated cost function 𝑔(𝑛) is the number of steps from the initial node.
(a) Construct two non-trivial heuristic functions, h1 and h2, that you think may help the algorithm to quickly find a solution. Are these functions admissible? Explain why?
(b) Which heuristic function is more efficient? Explain why?
(c) Useh1todoanIDA*searchtogetfromthestartstatetothegoalstateintheminimum number of steps. Make sure to show all working including value of 𝑓, h, 𝑔 functions in each steps.
(d) (optional/homework) Use h2 to do an IDA* search to get from the start state to the goal state in the minimum number of steps. Make sure to show all working including value of 𝑓, h, 𝑔 functions in each steps.

程序代写 CS代考 加微信: aplg6666 QQ: 2235208643 Email: kyit630461@163.com

CS代写 CS 111 Summer 2022 – cscodehelp代写

CS 111 Summer 2022
Lecture 17 Page 1
Operating System Principles: Distributed Systems
Operating Systems

Copyright By cscodehelp代写 加微信 cscodehelp

• Introduction
• Distributed system paradigms
• Remote procedure calls
• Distributed synchronization and consensus
• Distributed system security
• Accessing remote data
CS 111 Summer 2022
Lecture 17 Page 2

Introduction
Why do we care about distributed systems?
– Because that’s how most modern computing is done
Why is this an OS topic?
– Because it’s definitely a systems issue
– And even the OS on a single computer needs to worry about distributed issues
If you don’t know a bit about distributed
systems, you’re not a modern computer
scientist Summer 2022
Lecture 17 Page 3

Why Distributed Systems?
• Betterscalabilityandperformance
– Apps require more resources than one computer has
– Can we grow system capacity/bandwidth to meet demand?
• Improvedreliabilityandavailability
– 24×7 service despite disk/computer/software failures
• Easeofuse,withreducedoperatingexpenses
– Centralized management of all services and systems – Buy (better) services rather than computer equipment
• Enablingnewcollaborationandbusinessmodels
– Collaborations that span system (or national) boundaries
CS 111 – A global free market for a wide range of new services Summer 2022
Lecture 17 Page 4

A Few Little Problems
Different machines don’t share memory
– Or any peripheral devices
– So one machine can’t easily know the state of
Might this cause synchronization problems?
The only way to interact remotely is to use a
So how can we know what’s going on remotely?
– Usually asynchronous, slow, and error prone
– Usually not controlled by any single machine
Failures of one machine aren’t visible to other
machines Summer 2022
How can our computation be
reliable if pieces fail? Lecture 17 Page 5

Transparency
• Ideally, a distributed system would be just like a single machine system
• But better
– More resources – More reliable – Faster
• Transparent distributed systems look as much like single machine systems as possible
CS 111 Summer 2022
Lecture 17 Page 6

Deutsch’s “Seven Fallacies of Network Computing”
1. The network is reliable
2. There is no latency (instant response time)
3. The available bandwidth is infinite
4. The network is secure
5. The topology of the network does not change
6. There is one administrator for the whole network 7. The cost of transporting additional data is zero Bottom Line: true transparency is not achievable
CS 111 Summer 2022
Lecture 17 Page 7
Here’s an eight: all locations on the network are equivalent.

Distributed System Paradigms
• Parallel processing
– Relying on tightly coupled special hardware
Not widely used, we won’t discuss them.
• Single system images
– Make all the nodes look like one big computer – Somewhere between hard and impossible
• Loosely coupled systems
– Work with difficulties as best as you can
– Typical modern approach to distributed systems
• Cloud computing
CS 111 – A recent variant Summer 2022
Lecture 17 Page 8
So these are also not popular, and we won’t discuss them.

Loosely Coupled Systems
• Characterization:
– A parallel group of independent computers
– Connected by a high speed LAN
– Serving similar but independent requests
– Minimal coordination and cooperation required
• Motivation:
– Scalability and price performance
– Availability – if protocol permits stateless servers – Ease of management, reconfigurable capacity
• Examples:
– Web servers, app servers, cloud computing
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Horizontal Scalability
• Each node largely independent
• So you can add capacity just by adding a node “on the side”
• Scalability can be limited by network, instead of hardware or algorithms
– Or, perhaps, by a load balancer • Reliability is high
– Failure of one of N nodes just reduces capacity
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Horizontal Scalability Architecture
If I need more web server capacity,
WAN to clients
load balancing switch with fail-over
web server
app server
app server
app server
app server
app server
HA database server
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web server
web server
web server
web server
content distribution server

Elements of Loosely Coupled Architecture
• Farmofindependentservers
– Servers run same software, serve different requests – May share a common back-end database
• Front-endswitch
– Distributes incoming requests among available servers
– Can do both load balancing and fail-over
• Serviceprotocol
– Stateless servers and idempotent operations
– Successive requests may be sent to different servers
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Same result if you do it once, twice, three times, . . ., n times

Horizontally Scaled Performance • Individualserversareveryinexpensive
– Blade servers may be only $100-$200 each • Scalabilityisexcellent
– 100 servers deliver approximately 100x performance
• Serviceavailabilityisexcellent
– Front-end automatically bypasses failed servers – Stateless servers and client retries fail-over easily
• Thechallengeismanagingthousandsofservers
– Automated installation, global configuration services
– Self monitoring, self-healing systems
– Scaling limited by management, not HW or algorithms
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Cloud Computing
• The most recent twist on distributed computing
• Set up a large number of machines all identically configured
• Connect them to a high speed LAN – And to the Internet
• Accept arbitrary jobs from remote users
• Run each job on one or more nodes
• Entire facility probably running mix of single machine and distributed jobs, simultaneously
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What Runs in a Cloud? In principle, anything
– But general distributed computing is hard
So much of the work is run using special tools
These tools support particular kinds of parallel/distributed processing
– Either embarrassingly parallel jobs
– Or those using a method like map-reduce or
horizontal scaling
Things where the user need not be a distributed
systems expert Summer 2022
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Embarrassingly Parallel Jobs
• Problems where it’s really, really easy to parallelize them
• Probably because the data sets are easily divisible
• And exactly the same things are done on each piece
• So you just parcel them out among the nodes and let each go independently
• Everyone finishes at more or less same time
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• Perhaps the most common cloud computing software tool/technique
• A method of dividing large problems into compartmentalized pieces
• Each of which can be performed on a separate node
• With an eventual combined set of results
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The Idea Behind MapReduce
• There is a single function you want to perform on a lot of data
– Such as searching it for a particular string
• Divide the data into disjoint pieces
• Perform the function on each piece on a
separate node (map)
• Combine the results to obtain output
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An Example
• We have 64 megabytes of text data
• Count how many times each word occurs in the text
• Divide it into 4 chunks of 16 Mbytes
• Assign each chunk to one processor
• Perform the map function of “count words” on each
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The Example Continued
Foo Zoo Foo Zoo Foo Zoo Foo Zoo 16712249
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Bar 4 Baz 3
Yes 12 Too 5
Bar 3 Baz 9
Yes 17 Too 8
Bar 6 Baz 2
Yes Bar 7 10 Baz 5 Too 4
Yes 3 Too 7
That’s the map stage

On To Reduce
• We might have two more nodes assigned to doing the reduce operation
• They will each receive a share of data from a map node
• The reduce node performs a reduce operation to “combine” the shares
• Outputting its own result
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Continuing the Example
Foo Zoo Foo Zoo Foo Zoo Foo Zoo 16712249
Bar 4 Baz 3
Yes 12 Too 5
Bar 3 Baz 9
Yes 17 Too 8
Bar 6 Baz 2
Yes Bar 7 10 Baz 5 Too 4
Yes 3 Too 7
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The Reduce Nodes Do Their Job
Write out the results to files And MapReduce is done!
Foo Zoo 14 16 Bar 20 Yes Baz 42 19 Too
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But I Wanted A Combined List
• No problem
• Run another (slightly different) MapReduce on the outputs
• Have one reduce node that combines everything
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Synchronization in MapReduce
• Each map node produces an output file for each reduce node
• It is produced atomically
• The reduce node can’t work on this data
until the whole file is written
• Forcing a synchronization point between the map and reduce phases

Map Reduce vs. Embarrassing Parallelism
• Embarrassing parallelism is enough if it’s easy to divide a job into pieces
– Of the same size
• And if you don’t worry about failures
• And if you don’t need to combine the results in a non-trivial way
• Map reduce is needed if those things aren’t true
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Cloud Computing and Horizontal Scaling
• An excellent match
• Rent some cloud nodes to be your web servers
• If load gets heavy, ask the cloud for another web server node
• As load lightens, release unneeded nodes
• No need to buy new machines
• No need to administer your own machines
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Cloud Computing and Sysadmin
• Not quite as painless as it sounds
• The cloud provider will take care of lots of the problem
– Running the hardware
– Fixing broken hardware
– Loading your software onto machines
• But they won’t take care of internal administration
– E.g., updating the version of the web server you’re
running CS 111
Summer 2022
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Actually, they will take care of that, too, but at an extra price and with a loss of control.

Remote Procedure Calls
• RPC, for short
• One way of building a distributed program
• Procedure calls are a fundamental paradigm
– Primary unit of computation in most languages
– Unit of information hiding in most methodologies – Primary level of interface specification
• A natural boundary between client and server – Turn procedure calls into message send/receives
• A few limitations
– No implicit parameters/returns (e.g., global variables)
– No call-by-reference parameters
– Much slower than procedure calls (TANSTAAFL)
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Remote Procedure Call Concepts • Interface Specification
– Methods, parameter types, return types
• eXternal Data Representation (XDR)
– Machine independent data-type representations – May have optimizations for similar client/server
• Client stub
– Client-side proxy for a method in the API
• Server stub (or skeleton)
– Server-side recipient for API invocations
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Key Features of RPC
• Client application links against local procedures
– Calls local procedures, gets results
• All RPC implementation inside those procedures
• Client application does not know about RPC – Does not know about formats of messages
– Does not worry about sends, timeouts, resends
– Does not know about external data representation
• All of this is generated automatically by RPC tools
• The key to the tools is the interface specification
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RPC At Work, Step 1
Process_list
… list[0] = 10;
list[1] = 20; list[2] = 17;
max = list_max(list);
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list_max() is a remote procedure call!
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RPC At Work, Step 2
Process_list
local_max = list_max(list);
. . . list[0] = 10;
list[1] = 20; list[2] = 17;
max = list_max(list);
Format RPC message
Send the message
CS 111 Summer 2022
Extract RPC info
list_max()
Call local procedure
Lecture 17 Page 33
RPC message: list_max(), parameter list

RPC At Work, Step 3
… list[0] = 10;
list[1] = 20;
list[2] = 17;
local_max = list_max(list);
Format RPC response
Send the message
Lecture 17 Page 34
CS 111 Summer 2022
max = list_max(list);
If (max > 10) {
Extract the return value Resume the local program
RPC response: list_max(), return value 20

RPC Is Not a Complete Solution
• Requires client/server binding model
– Expects to be given a live connection
• Threading model implementation
– A single thread services requests one at a time
– So use numerous one-per-request worker threads
• Limited consistency support
– Only between calling client and called server
– What if there are multiple clients and servers working together?
• Limited failure handling
– Client must arrange for timeout and recovery
• Higher level abstractions improve RPC
– e.g. Microsoft DCOM, Java RMI, DRb, Pyro
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Distributed Synchronization
• Why is it hard to synchronize distributed systems?
• What tools do we use to synchronize them?
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What’s Hard About Distributed Synchronization?
• Spatial separation
– Different processes run on different systems
– No shared memory for (atomic instruction) locks – They are controlled by different operating systems
• Temporal separation
– Can’t “totally order” spatially separated events – Before/simultaneous/after lose their meaning
• Independent modes of failure
CS 111 – One partner can die, while others continue
Summer 2022
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Leases – More Robust Locks
• Obtained from resource manager
– Gives client exclusive right to update the file
– Lease “cookie” must be passed to server on update – Lease can be released at end of critical section
• Only valid for a limited period of time – After which the lease cookie expires
• Updates with stale cookies are not permitted – After which new leases can be granted
• Handles a wide range of failures
– Process, client node, server node, network
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Lock Breaking and Recovery • Revoking an expired lease is fairly easy
– Lease cookie includes a “good until” time • Based on server’s clock
– Any operation involving a “stale cookie” fails
• This makes it safe to issue a new lease
– Old lease-holder can no longer access object – But was object left in a “reasonable” state?
• Object must be restored to last “good” state – Roll back to state prior to the aborted lease
CS 111 – Implement all-or-none transactions Summer 2022
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Distributed Consensus
• Achievingsimultaneous,unanimousagreement
– Even in the presence of node & network failures
– Required: agreement, termination, validity, integrity
– Desired: bounded time
– Provably impossible in fully general case
– But can be done in useful special cases, or if some
requirements are relaxed
• Consensusalgorithmstendtobecomplex
– And may take a long time to converge
• Theytendtobeusedsparingly
– E.g., use consensus to elect a leader
– Who makes all subsequent decisions by fiat
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Typical Consensus Algorithm
1. Each interested member broadcasts his nomination.
2. All parties evaluate the received proposals according to a fixed and well known rule.
3. After allowing a reasonable time for proposals, each voter acknowledges the best proposal it has seen.
4. If a proposal has a majority of the votes, the proposing member broadcasts a claim that the question has been resolved.
5. Each party that agrees with the winner’s claim acknowledges the announced resolution.
6. Election is over when a quorum acknowledges the result.
What’s going to happen if someone lies . . . ?
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Security for Distributed Systems
• Security is hard in single machines
• It’s even harder in distributed systems • Why?
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Why Is Distributed Security Harder?
• Your OS cannot guarantee privacy and integrity – Network activities happen outside of the OS – Should you trust where they happen?
• Authentication is harder
– All possible agents may not be in local password file
• The wire connecting the user to the system is insecure – Eavesdropping, replays, man-in-the-middle attacks
• Even with honest partners, hard to coordinate distributed security
• The Internet is an open network for all
– Many sites on the Internet try to serve all comers
– Core Internet makes no judgments on what’s acceptable
– Even supposedly private systems may be on Internet
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Goals of Network Security
• Secure conversations
– Privacy: only you and your partner know what is said – Integrity: nobody can tamper with your messages
• Positive identification of both parties
– Authentication of the identity of message sender
– Assurance that a message is not a replay or forgery – Non-repudiation: he cannot claim “I didn’t say that”
• Availability
– The network and other nodes must be reachable when
they need to be Summer 2022
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Elements of Network Security • Cryptography
– Symmetric cryptography for protecting bulk transport of data
– Public key cryptography primarily for authentication
– Cryptographic hashes to detect message alterations
• Digital signatures and public key certificates – Powerful tools to authenticate a message’s sender
• Filtering technologies
– Firewalls and the like
– To keep bad stuff from reaching our machines
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Tamper Detection: Cryptographic Hashes
• Check-sums often used to detect data corruption – Add up all bytes in a block, send sum along with data
– Recipient adds up all the received bytes
– If check-sums agree, the data is probably OK
– Check-sum (parity, CRC, ECC) algorithms are weak
• Cryptographic hashes are very strong check-sums
– Unique –two messages vanishingly unlikely to
produce same hash
• Particularly hard to find two messages with the same hash
– One way – cannot infer original input from output
– Well distributed – any change to input changes output
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Using Cryptographic Hashes
• Startwithamessageyouwanttoprotect
• Computeacryptographichashforthatmessage
– E.g., using the Secure Hash Algorithm 3 (SHA-3) • Transmitthehashsecurely
• Recipientdoessamecomputationonreceivedtext
– If both hash results agree, the message is intact
– If not, the message has been corrupted/compromised
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Secure Hash Transport • Whymustthehashbetransmittedsecurely?
– Cryptographic hashes aren’t keyed, so anyone can produce them (including a bad guy)
• Howtotransmithashsecurely?
– Encrypt it
– Unless secrecy required, cheaper than encrypting entire message
– If you have a secure channel, could transmit it that way
CS 111 Summer 2022
• But if you have secure channel, why not use it for everything?
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A Principle of Key Use
• BothsymmetricandPKcryptographyrelyonasecret key for their properties
• Themoreyouuseonekey,thelesssecure – The key stays around in various places longer
– There are more opportunities for an attacker to get it – There is more incentive for attacker to get it
– Brute force attacks may eventually succeed
• Therefore:
– Use a given key as little as possible
– Change them often
– Within the limits of practicality and required performance
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Putting It Together: Secure Socket Layer (SSL)
• A general solution for securing network communication
• Built on top of existing socket IPC
• Establishes secure link between two parties
– Privacy – nobody can snoop on conversation – Integrity – nobody can generate fake messages
• Certificate-based authentication of server – Typically, but not necessarily
– Client knows what server he is talking to
• Optional certificate-based authentication of client – If server requires authentication and non-repudiation
• PK used to distribute a symmetric session key – New key for each new socket
• Rest of data transport switches to s

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