Performance Management of Enterprise Application Systems in the Cloud Era

云时代企业应用系统的性能管理

• 批准号: RGPIN-2018-04224
• 负责人: Krishnamurthy, Diwakar
• 金额: $ 2.48万
• 依托单位: University of Calgary
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=712814
• 关键词: Performance; Management; Enterprise; Application; Systems

Enterprise applications, e.g., Web and interactive big data services, need to respond quickly to user transactions. Consequently, system operators need techniques that ensure applications meet their response time objectives while utilizing computing resources in a cost-effective way. Several factors necessitate new performance management techniques for such systems. For example, these applications are being increasingly deployed on public cloud platforms, which can suffer from unpredictable performance degradations due to contention for shared cloud resources. Novel approaches are needed to manage system performance in the presence of such platform induced interference. Furthermore, these systems typically experience bursty workloads, which can degrade performance in complex ways. This motivates new techniques that can predict and mitigate the impact of burstiness. This program seeks to address such challenges. We will investigate new techniques that allow an operator to accurately predict the cloud resources needed by a system to satisfy a desired response time target while handling a given workload. Techniques based on queuing analysis typically require an expert to manually author a system model. Also, accuracy can be impacted when predicting for bursty workloads. Machine learning (ML) techniques promise a data-driven alternative to queuing analysis. However, existing work does not provide clear intuition on tasks that can have a big impact on accuracy such as ML technique selection, featurization, and training data selection. My program will address this knowledge gap and realize automated prediction techniques that do not burden an operator with such tasks. We will also explore runtime techniques to mitigate the impact of burstiness and interference. Existing work has not focused on handling the adverse impact of service demand burstiness, i.e., user transaction patterns that cause sustained periods of high or low utilizations at system resources. Our initial work suggests that such burstiness can be tamed using fewer resources by intelligently reordering incoming transactions. We will build on this insight to realize new runtime scheduling techniques. As part of this theme, we will also exploit our ongoing work on interference detection to automatically scale cloud resource instances , e.g., containers, in response to interference. Existing approaches do not consider how individual transaction types get impacted by interference at a given instance. We will build models that can use such fine-grained information to intelligently distribute transactions to instances such that interference is mitigated using minimum instances. This program will expand the state of the art in data-driven performance prediction and management research. Canadian organizations can exploit the research to reduce costs related to poor performance and resource over-provisioning.

企业应用程序，例如Web和交互式大数据服务，需要快速响应用户交易。因此，系统操作员需要确保应用程序符合其响应时间目标的技术，同时以具有成本效益的方式利用计算资源。几个因素需要针对此类系统的新绩效管理技术。例如，这些应用程序越来越多地部署在公共云平台上，由于共享云资源的争议，可能会遭受不可预测的性能降解。需要采用新颖的方法来管理这种平台引起的干扰。此外，这些系统通常会经历爆发的工作负载，这会以复杂的方式降低性能。这激发了可以预测和减轻爆发影响的新技术。该计划旨在应对此类挑战。 我们将研究新技术，这些技术使操作员能够准确预测系统所需的云资源，以满足所需的响应时间目标，同时处理给定的工作负载。基于排队分析的技术通常需要专家手动撰写系统模型。同样，预测爆发工作负载时可能会影响准确性。机器学习（ML）技术有助于数据驱动的排队分析替代方案。但是，现有工作并不能对可能对ML技术选择，特征和培训数据选择等准确性产生重大影响的任务提供明确的直觉。我的计划将解决这一知识差距，并实现自动预测技术，这些技术不会为操作员负担此类任务。 我们还将探索运行时技术，以减轻爆发和干扰的影响。现有工作并不集中于处理服务需求爆发的不利影响，即导致系统资源高利用率或低利用率的用户交易模式。我们的最初工作表明，通过智能重新排序传入的交易可以使用更少的资源来驯服这种爆发。我们将基于这个见解，以实现新的运行时计划技术。作为此主题的一部分，我们还将利用正在进行的干扰检测的工作，以自动扩展云资源实例，例如容器，以响应干扰。现有的方法不考虑在给定实例的干扰影响单个交易类型如何受到干扰的影响。我们将构建可以使用此类细粒度信息来智能分配交易的模型，以便使用最低实例来减轻干扰。 该计划将在数据驱动的绩效预测和管理研究中扩展艺术状态。加拿大组织可以利用研究，以降低与绩效差和资源过度相关的成本。