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Design Patterns for Distributed Systems

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Implementing a distributed algorithm is more complicated than implementing a non-distributed algorithm. This is because distributed systems involve coordination of different processes each of which has a partial view of

Implementing a distributed algorithm is more complicated than implementing a non-distributed algorithm. This is because distributed systems involve coordination of different processes each of which has a partial view of the global system state. The only way to share information in a distributed system is by message passing. Task that are straightforward in a non-distributed system, like deciding on the value of a global system state, can be quite complicated to achieve in a distributed system [1]. On top of the difficulties caused by the distributed nature of the computations, distributed systems typically need to be able to operate normally even if some of the nodes in the system are faulty which further adds to the uncertainty that processes have about the global state. Many factors make the implementation of a distributed algorithms difficult. Design patterns [2] are useful in simplifying the development of general algorithms. A design pattern describes a high level solution to a common, abstract problem that many systems may face. Common structural, creational, and behavioral problems are identified and elegantly solved by design patterns. By identifying features that an algorithm uses, and framing each feature as one of the common problems that a specific design pattern solves, designing a robust implementation of an algorithm becomes more manageable. In this way, design patterns can aid the implementation of algorithms. Unfortunately, design patterns are typically not discussed when developing distributed algorithms. Because correctly developing a distributed algorithm is difficult, many papers (eg. [1], [3], [4]) focus on verifying the correctness of the developed algorithm. Papers that are more practical ([5], [6]) establish the correctness of their algorithm and that their algorithm is efficient enough to be practical. However, papers on distributed algorithms usually make little mention of design patterns. The goal of this work was to gain experience implementing distributed systems including learning the application of design patterns and the application of related technical topics. This was achieved by implementing a currently unpublished algorithm that is tentatively called Bakery Consensus. Bakery Consensus is a replicated state-machine protocol that can tolerate servers with Byzantine faults, but assumes non-faulty clients. The algorithm also establishes non-skipping timestamps for each operation completed by the replicated state-machine. The design of the structure, communication, and creation of the different system parts depended heavily upon the book Design Patterns [2]. After implementing the system, the success of the in implementing its various parts was based upon their ability to satisfy the SOLID [7] principles as well as their ability to establish low coupling and high cohesion [8]. The rest of this paper is organized as follows. We begin by providing background information about distributed algorithms, including replicated state-machine protocols and the Bakery Consensus algorithm. Section 3 gives a background on several design patterns and software engineering principles that were used in the development process. Section 4 discusses the well designed parts of the system that used design patterns, and how these design patterns were chosen. Section 5 discusses well designed system parts that relied upon other technical topics. Section 6 discusses system parts that need redesign. The conclusion summarizes what was accomplished by the implementation process and the lessons learned about design patterns for distributed algorithms.

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Date Created
  • 2017-05