CSR: Small: Scalable, heterogeneity-aware load balancing

CSR：小型：可扩展、异构感知负载平衡

• 批准号: 1617046
• 负责人: Anshul Gandhi
• 金额: $ 39.5万
• 依托单位: SUNY at Stony Brook
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-10-01 至 2020-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1617046&HistoricalAwards=false
• 关键词: CSR; Small; Scalable; heterogeneity; aware

Several extremely large online services are provided by distributed systems. Load balancers play a vital role in such systems by distributing incoming requests among the back-end nodes. However, given the rise in cloud computing and the increasing popularity of online services, load balancers today face significant challenges that impact performance. Key among them is the need to quickly distribute millions of requests among heterogeneous nodes while adapting to changing system conditions. Failure to serve requests in a timely manner can lead to loss of revenue due to customer abandonment. This research proposes novel, scalable algorithms that enable high-throughput load balancers for cloud deployments. By leveraging concepts from queueing theory, this research will investigate dynamic, near-optimal load distribution policies with provable performance guarantees. The proposed algorithms will be designed for easy adoption in existing open-source load balancers, including Apache, HAProxy, and nginx. Recent network function virtualization trends have put the spotlight back on software network functions. This project will explore novel software load balancers for modern computing environments, including dedicated clusters and shared cloud environments. Given the need for high-throughput load balancing decisions, this research focuses on simple yet powerful randomized load balancer designs. The key idea of the proposed research is to dynamically adapt the routing probability based on inferred changes in the workload and infrastructure. Queueing theoretic models will be leveraged to understand the impact of routing on performance, and machine learning techniques will be employed for detecting system changes. The proposed load balancers will be evaluated in Web and data-dependent environments, including MapReduce implementations. The integrated theory-systems research approach will provide unique interdisciplinary educational and collaborative opportunities, including (cross-listed) course development, student training, and technology transfer with industrial partners.

一些非常大的在线服务由分布式系统提供。负载均衡器通过在后端节点之间分发传入的请求，在这样的系统中发挥着至关重要的作用。然而，随着云计算的兴起和在线服务的日益普及，负载均衡器如今面临着影响性能的重大挑战。其中的关键是需要在异构节点之间快速分发数百万个请求，同时适应不断变化的系统条件。未能及时满足请求可能会导致由于客户放弃而导致的收入损失。这项研究提出了新的，可扩展的算法，使高吞吐量的负载均衡器的云部署。通过利用概念从嵌入式理论，本研究将探讨动态的，接近最佳的负载分配策略与可证明的性能保证。所提出的算法将被设计为易于在现有的开源负载均衡器中采用，包括Apache，HAProxy和nginx。最近的网络功能虚拟化趋势使人们重新关注软件网络功能。该项目将探索用于现代计算环境的新型软件负载平衡器，包括专用集群和共享云环境。考虑到高吞吐量负载均衡决策的需要，本研究主要关注简单而强大的随机负载均衡器设计。所提出的研究的关键思想是根据推断的工作负载和基础设施的变化来动态地适应路由概率。将利用嵌入式理论模型来了解路由对性能的影响，并将采用机器学习技术来检测系统变化。拟议的负载均衡器将在Web和数据依赖环境中进行评估，包括MapReduce实现。综合理论系统研究方法将提供独特的跨学科教育和合作机会，包括（交叉列出）课程开发，学生培训和与工业合作伙伴的技术转让。

项目成果

A Graybox Approach to Rehoming Service Chains

重新安置服务链的灰盒方法

• 发表时间: 2018
• 期刊: and Simulation of Computer and Telecommunication Systems
• 作者: Wajahat, Muhammad;Balasubramanian, Bharath;Gandhi, Anshul;Jung, Gueyoung;Narayanan, Shankaranarayanan P.
• 通讯作者: Narayanan, Shankaranarayanan P.