ITR Collaborative Research: Modeling and Mitigation of Communication-Delay Effects on Load Balancing in Large-Scale Distributed Systems

这是新墨西哥大学（UNM）和田纳西大学诺克斯维尔分校（UTK）之间的一项合作提案，用于在由地理距离遥远的计算元素（ce）组成的大规模分布式计算系统中建模、优化和测试创新负载平衡策略。智力优势：随着大规模分布式计算系统的出现，这些系统利用ce之间的共享通信媒介，需要准确地理解信息传输中的延迟对这些系统的功能和控制的影响。这种分布式系统可以包括移动ce网络、通过Internet连接的ce网络，或者分布在不同州或国家（例如，代表各种数据库）的ce网络。由于大规模分布式系统中节点之间的物理距离，节点之间的通信和负载转移活动充满了明显的随机延迟。这种行为不同于在局部分布式系统中通常假定的情况，在局部分布式系统中，组成ce彼此接近，受益于专用的快速通信介质。直觉和我们的蒙特卡罗模拟都明确地表明，分布式系统中这种通信和负载转移延迟的存在可能导致传统负载平衡算法的失败。因此，任务计算的动态性需要一个包含延迟的分析框架，而包含延迟的负载均衡策略的开发和优化正是在这样的框架内实现的。该计划的目标是开发一个通用的分析框架，用于建模受延迟影响的分布式系统的随机动力学，并利用它来开发负载平衡策略，以减轻由通信和负载转移延迟引起的性能下降或故障。该模型是在一个新颖的、基于再生的排队框架内开发的，负载均衡优化将通过统计学习和随机预测的方式进行。本项目开发的负载平衡策略将在具有实际延迟的物理分布式系统环境中进行测试。为此，将开发和部署一个分布式计算试验台，将UTK现有的数据搜索计算机与新墨西哥大学将开发的微型克隆系统连接起来。更广泛的影响：这里提出的工作的动机是（但不限于）联合pi（在UTK）对联邦调查局（FBI）国家DNA索引系统（NDIS）及其联合DNA索引系统（CODIS）软件所做的当前工作中出现的实际、紧迫问题。该NDIS数据库的预计增长和对其内容搜索的需求需要迁移到并行计算平台，并可能迁移到大规模分布式系统，其中数据库分布或复制在地理上遥远的中心，这些中心通过带宽有限的共享通信媒介连接。该项目的成果不仅有利于上述系统，也有利于在分布式站点上执行搜索的广泛的公共、私人和政府数据库系统。所提出的研究也将用于改善大型虚拟实验室的运作。新墨西哥大学电气与计算机工程系创造了一种通过互联网远程控制仪器和模拟的有效方法。这种方法既适用于工业教育，也适用于远程教育，目前可供指定用户使用和使用。但是，这个平台还没有在真实的分布式环境中对大量用户进行测试。本研究中的方法将应用于提高现有教育平台的性能，并将其扩展到全球机构的大型网络。这项活动还将为研究生和本科生提供大量的最新信息技术培训机会。

This is a collaborative proposal between The University of New Mexico (UNM) and The University of Tennessee-Knoxville (UTK) for modeling, optimization and testing of a innovative load balancing strategies in large-scale, distributed-computing systems consisting of geographically-distant computational elements (CEs).Intellectual Merit: With the emergence of large-scale distributed-computing systems that utilize a shared communication medium between the CEs, there is a need for accurately understanding the effect of delay in information transport on the functionality and control of these systems. Such distributed systems may include networks of mobile CEs, CEs that are connected through the Internet, or CEs that are distributed over different states or countries (representing various data bases, for example). Due to the physical distance between nodes in large-scale distributed systems, communication and load-transfer activity among the nodes is infested with tangible, random delays. This behavior is unlike what is ordinarily assumed in localized distributed systems, for which the constituent CEs are within proximity of each other, benefiting from a dedicated fast communication medium. Both intuition and our Monte-Carlo simulation definitively indicate that the presence of such communication and load-transfer delays in distributed systems can lead to the failure of traditional load balancing algorithms. Thus, a delay-inclusive analytical framework is needed for the dynamics of task computing, and it is within such a framework that the development and optimization of delay-inclusive load-balancing policies can be realized.The objectives of this program are to develop a general analytical framework for modeling the stochastic dynamics of delay-infested distributed systems and utilize it to develop load-balancing strategies that mitigate the performance degradation or failure caused by communication and load-transfer delays. The modeling is developed within a novel, regeneration-based queuing framework, and the load-balancing optimization will be carried out by means of statistical learning and stochastic prediction. The load-balancing strategies developed in this program will be tested in a physical distributed-system environment with realistic delays. To do so, a distributed-computing test-bed will be developed and deployed connecting existing data searching computers at UTK with a miniature clone system to be developed at UNM.Broader Impact: The work proposed here is motivated by (but not limited to) practical, pressing issues arising in the current work done by the Co-PIs (at UTK) on The Federal Bureau of Investigation (FBI) National DNA Index System (NDIS) and its Combined DNA Index System (CODIS) software. The projected growth of this NDIS database and in the demand for searches of its contents necessitates migration to a parallel computing platform, and potentially to large-scale distributed systems, where the database is distributed or duplicated over geographically distant centers which are connected by means of a bandwidth-limited shared communication medium. The outcomes of this program will not only benefit the above systems but also a broad range of public, private and government database systems that perform searches over distributed sites.The proposed research will also be utilized to improve the operation of large-scale virtual laboratories. The Electrical and Computer Engineering Department at UNM has created an efficient way for the remote control of instruments and simulations over the Internet. This approach is applicable to both industry and distance education, and it is currently operational and accessible for designated users. However, this platform has not been tested with a large number of users in a real, distributed environment. The methodology in this research will be applied to enhance the performance of the existing educational platform and scale up its reach to a large network of institutions worldwide. This activity will also provide tremendous training opportunities in state-of-the-art information technology for graduate and undergraduate students.