TAILIEUCHUNG - MapReduce: Simplified Data Processing on Large Clusters

A clickbot is a software robot that clicks on ads (issues HTTP requests for advertiser web pages) to help an attacker conduct click fraud. Some clickbots can be purchased, while others are malware that spread as such and are part of larger botnets. Malware-type clickbots can receive instructions from a botmas- ter server as to what ads to click, and how often and when to click them. There are many types of clickbots used on the Internet. Some are “for-sale” clickbots, while others are malware. For-sale clickbots such as the Lote Clicking Agent, I-Faker, FakeZilla, and Clickmaster can be purchased online. They typically use anonymous proxies to generate traffic with IP. | MapReduce Simplified Data Processing on Large Clusters Jeffrey Dean and Sanjay Ghemawat jeff@ sanjay@ Google Inc. Abstract MapReduce is a programming model and an associated implementation for processing and generating large data sets. Users specify a map function that processes a key value pair to generate a set of intermediate key value pairs and a reduce function that merges all intermediate values associated with the same intermediate key. Many real world tasks are expressible in this model as shown in the paper. Programs written in this functional style are automatically parallelized and executed on a large cluster of commodity machines. The run-time system takes care of the details of partitioning the input data scheduling the program s execution across a set of machines handling machine failures and managing the required inter-machine communication. This allows programmers without any experience with parallel and distributed systems to easily utilize the resources of a large distributed system. Our implementation of MapReduce runs on a large cluster of commodity machines and is highly scalable a typical MapReduce computation processes many terabytes of data on thousands of machines. Programmers find the system easy to use hundreds of MapReduce programs have been implemented and upwards of one thousand MapReduce jobs are executed on Google s clusters every day. 1 Introduction Over the past five years the authors and many others at Google have implemented hundreds of special-purpose computations that process large amounts of raw data such as crawled documents web request logs etc. to compute various kinds of derived data such as inverted indices various representations of the graph structure of web documents summaries of the number of pages crawled per host the set of most frequent queries in a given day etc. Most such computations are conceptually straightforward. However the input data is usually large and the computations have to be .

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