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Optimized software component allocation on clustered application servers.

机译:在集群应用程序服务器上优化了软件组件分配。

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In the last decade, online e-commerce businesses, represented by the e-commerce portals, have grown significantly and become an important sector of world economy. This dissertation helps address the server scalability problem for supporting the sustainable growth of the online e-commerce industries.; Most of today's e-commerce portals are implemented with distributed component technologies and server clusters. Each server application comprises dozens or hundreds of distributed software components, and each of such components can run on any of a cluster of application servers connected by a high-speed fiber local area network (LAN). While multiple server machines support parallel execution of the software components, inter-server communication is a few orders slower than servers' CPU speed. This research studies the optimized allocation of software components to server machines to maximize computation load balance and minimize communication overhead.; Multi-way graph partitioning is first adopted to model the software component allocation problem. The problem is proved to be NP-hard. A novel graph transformation is introduced to combine the two conflicting objectives into a single objective function, and a transformation theorem is proved that problem instances before and after this transformation are equivalent. Based on careful observation of the properties of the solution space, a scheme for incremental objective function evaluation is designed to speed up any iterative solution heuristics to this problem by a factor proportional to the number of software components involved. Simulated annealing is adopted to solve the problem. Extensive experimental study shows that the proposed simulated annealing algorithm can outperform repeated random running in the same amount of time by 20% to 2366.67%, and outperform local optimization by 1.96% to 1000% with a running time about 6 to 100 times of that for the latter.; The major contributions of this research include using multi-way graph partitioning to model a challenging performance problem critical to sustainable growth of e-commerce portals, creative problem transformation for simplifying a complex problem, and incremental objective function evaluation that can benefit any iterative solution heuristics.
机译:在过去的十年中,以电子商务门户网站为代表的在线电子商务业务已显着增长,并已成为世界经济的重要部门。本文旨在解决服务器可扩展性问题,以支持在线电子商务行业的可持续增长。当今的大多数电子商务门户都是使用分布式组件技术和服务器集群来实现的。每个服务器应用程序都包含数十个或数百个分布式软件组件,每个这样的组件都可以在通过高速光纤局域网(LAN)连接的任何应用程序服务器群集中运行。虽然多台服务器计算机支持并行执行软件组件,但服务器间的通信速度比服务器的CPU速度慢了几个数量级。这项研究研究了优化软件组件到服务器机器的分配,以最大程度地提高计算负载平衡并最小化通信开销。首先采用多向图分区来建模软件组件分配问题。该问题被证明是NP难的。引入一种新颖的图变换将两个冲突的目标组合成一个目标函数,并证明了一个变换定理证明该变换前后的问题实例是等效的。基于对解决方案空间属性的仔细观察,设计了一种增量目标函数评估方案,以通过与所涉及软件组件数量成比例的因子来加快对该问题的任何迭代解法启发式处理。采用模拟退火解决了该问题。大量的实验研究表明,所提出的模拟退火算法可以在相同的时间内胜过重复随机运行20%至2366.67%,并比局部优化胜过1.96%至1000%,运行时间约为该时间的6到100倍。后者。;该研究的主要贡献包括使用多向图分区来建模对电子商务门户的可持续增长至关重要的具有挑战性的性能问题,用于简化复杂问题的创造性问题转换以及可以使任何迭代解决方案启发式方法受益的递增目标函数评估。

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