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تعداد صفحات این فایل: ۲۰ صفحه

بخشی از مقاله انگلیسیعنوان انگلیسی:Implementation Issues of A Cloud Computing Platform~~en~~

Abstract

Cloud computing is Internet based system development in which large scalable computing resources are provided “as a service” over the Internet to users. The concept of cloud computing incorporates web infrastructure, software as a service (SaaS), Web 2.0 and other emerging technologies, and has attracted more and more attention from industry and research community. In this paper, we describe our experience and lessons learnt in construction of a cloud computing platform. Specifically, we design a GFS compatible file system with variable chunk size to facilitate massive data processing, and introduce some implementation enhancement on MapReduce to improve the system throughput. We also discuss some practical issues for system implementation. In association of the China web archive (Web InfoMall) which we have been accumulating since 2001 (now it contains over three billion Chinese web pages), this paper presents our attempt to implement a platform for a domain specific cloud computing service, with large scale web text mining as targeted application. And hopefully researchers besides our selves will benefit from the cloud when it is ready.

۱ Introduction

As more facets of work and personal life move online and the Internet becomes a platform for virtual human society, a new paradigm of large-scale distributed computing has emerged. Web-based companies, such as Google and Amazon, have built web infrastructure to deal with the internet-scale data storage and computation. If we consider such infrastructure as a “virtual computer”, it demonstrates a possibility of new computing model, i.e., centralize the data and computation on the “super computer” with unprecedented storage and computing capability, which can be viewed as a simplest form of cloud computing.

More generally, the concept of cloud computing can incorporate various computer technologies, including web infrastructure, Web 2.0 and many other emerging technologies. People may have different perspectives from different views. For example, from the view of end-user, the cloud computing service moves the application software and operation system from desktops to the cloud side, which makes users be able to plug-in anytime from anywhere and utilize large scale storage and computing resources. On the other hand, the cloud computing service provider may focus on how to distribute and schedule the computer resources. Nevertheless, the storage and computing on massive data are the key technologies for a cloud computing infrastructure.

Google has developed its infrastructure technologies for cloud computing in recent years, including Google File System (GFS) [8], MapReduce [7] and Bigtable [6]. GFS is a scalable distributed file system, which emphasizes fault tolerance since it is designed to run on economically scalable but inevitably unreliable (due to its sheer scale) commodity hardware, and delivers high performance service to a large number of clients. Bigtable is a distributed storage system based on GFS for structured data management. It provides a huge three-dimensional mapping abstraction to applications, and has been successfully deployed in many Google products. MapReduce is a programming model with associated implementation for massive data processing. MapReduce provides an abstraction by defining a “mapper” and a “reducer”. The “mapper” is applied to every input key/value pair to generate an arbitrary number of intermediate key/value pairs. The “reducer” is applied to all values associated with the same intermediate key to generate output key/value pairs. MapReduce is an easy-to-use programming model, and has sufficient expression capability to support many real world algorithms and tasks. The MapReduce system can partition the input data, schedule the execution of program across a set of machines, handle machine failures, and manage the inter-machine communication.

More recently, many similar systems have been developed. KosmosFS [3] is an open source GFS-Like system, which supports strict POSIX interface. Hadoop [2] is an active Java open source project. With the support from Yahoo, Hadoop has achieved great progress in these two years. It has been deployed in a large system with 4,000 nodes and used in many large scale data processing tasks.

In Oct 2007, Google and IBM launched “cloud computing initiative” programs for universities to promote the related teaching and research work on increasingly popular large-scale computing. Later in July 2008, HP, Intel and Yahoo launched a similar initiative to promote and develop cloud computing research and education. Such cloud computing projects can not only improve the parallel computing education, but also promote the research work such as Internet-scale data management, processing and scientific computation. Inspired by this trend and motivated by a need to upgrade our existing work, we have implemented a practical web infrastructure as cloud computing platform, which can be used to store large scale web data and provide high performance processing capability. In the last decade, our research and system development focus is on Web search and Web Mining, and we have developed and maintained two public web systems, i.e., Tianwang Search Engine [4] and Web Archive system Web infomall [1] as shown in Figure 1.

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