Over the last two decades, information technology has been moving towards distributed computing to host their applications and services. These systems can process more data more reliably than their central processing counterparts; however, distributed applications are more complex to design and develop because they require additional properties like replication and fault tolerance to work effectively. These complexities translate to the educational setting, where schools need to invest in additional infrastructure, knowledge, and technologies to teach distributed concepts to students. This project presents the design and implementation of a complete educational framework for the teaching of distributed computing concepts at Cal Poly. The framework consists of three components: a Raspberry Pi cluster, a custom distributed file system (DecaFS), and a set of labs that can be used to support coursework in a distributed computing class. Each cluster is composed of five networked Raspberry Pi computers. The DecaFS distributed file system runs on the Raspberry Pi cluster. DecaFS provides the base functionality of a distributed file system with a design that allows for easy modification of sections of the implementation. The lab exercises focus on important distributed computing concepts that represent a variety of problems encountered in distributed systems including distribution, replication, fault tolerance, recovery, rebalancing, and efficiency. Isolation of the lab related modules allows students to focus on the learning objectives of the labs without needing to set up network and file system infrastructure to support the distributed aspects. The complexities of teaching distributed computing concepts in a classroom setting at Cal Poly have been addressed with this project's framework. The solution overcomes key educational challenges as it is portable, modular, scalable and affordable. The framework provides the ability to offer courses in distributed computing to better prepare students for the challenges presented in industry today. Through the use of a modular distributed file system and computing cluster that were created for this project, students are able to solve complex distributed problems, in the form of labs, in an isolated environment that is conducive to quarter long learning objectives. This work is a major step to bringing distributed computing into the classrooms at Cal Poly and classes are currently being designed around this curriculum. Cal Poly can evolve the framework to keep pace with the ever advancing information technology world so that it may continue to serve the needs of the faculty and students of Cal Poly.

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The process of bioinformatics computing consumes huge computing resources, in situation of difficulty in improvement of algorithm and high cost of mainframe, many scholars choice distributed computing as an approach for reducing computing time. When using distributed computing for bioinformatics, how to find a properly tasks allocation strategy among different computing nodes to keep load-balancing is an important issue. By adopting multi-agent system as a tool, system developer can design tasks allocation strategies through intuitional view and keep load-balancing among computing nodes. The purpose of our research work is using multi-agent system as an underlying tool to develop a distributed computing environment and assist scholars in solving bioinformatics computing problem, In comparison with public computing projects such as BOINC, our research work focuses on utilizing computing nodes deployed inside organization and connected by local area network.

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Detecting the existence of a consistent global state that satisfies a predicate in a distributed environment is a processing intensive task since all the consistent global states must be checked to verify that none of them satisfies the predicate. Three different serial implementations have been provided for a breath-first, depth-first, and lexical traversal of the lattice generated by enumerating the possible consistent global states has been provided by Alagar and Venkatesan, Cooper and Marzullo, and Garg. This paper modifies those implementations to perform the checks in a distributed environment, providing the final algorithms, source code, and preliminary results for comparisons with the original algorithms. / text

The Galois system provides a solution to the hard problem of parallelizing irregular algorithms using amorphous data-parallelism. The present system works on the shared-memory programming model. The programming model has limitations on the memory and processing power available to the application. A scalable distributed parallelization tool would give the application access to a very large amount of memory and processing power by interconnecting computers through a network. This thesis presents the design for a distributed execution programming model for the Galois system. This distributed Galois system is capable of executing irregular graph based algorithms on a distributed environment. The API and programming model of the new distributed system has been designed to mirror that of the existing shared-memory Galois. This was done to enable existing applications on shared memory applications to run on distributed Galois with minimal porting effort. Finally, two existing test cases have been implemented on distributed Galois and shown to scale with increasing number of hosts and threads. / text

Existing frequent-sequence mining algorithms perform multiple scans of a database, or a structure that captures the database. In this M.Sc. thesis, I propose a frequent-sequence mining algorithm that mines each database row as it reads it, so that it can potentially complete mining in the time it takes to read the database once. I achieve this by having my algorithm enumerate all sub-sequences from each row as it reads it. Since sub-sequence enumeration is a time-consuming process, I create a method to distribute the work over multiple computers, processors, and thread units, while balancing the load between all resources, and limiting the amount of communication so that my algorithm scales well in regards to the number of computers used. Experimental results show that my algorithm is effective, and can potentially complete the mining process in near the time it takes to perform one scan of the input database.