Evaluation and Testing Framework for Cloud and Edge Computing Applications

云和边缘计算应用的评估和测试框架

• 批准号: RGPIN-2020-04849
• 负责人: Amannejad, Yasaman
• 金额: $ 1.75万
• 依托单位: Mount Royal University
• 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=717286
• 关键词: Evaluation; Testing; Framework; Cloud; Edge

Today's state-of-the-art applications are typically deployed in cloud infrastructure, leveraging cost benefits, elastic scalability, and the illusion of infinite resources. While this satisfies the needs of many application domains such as enterprise web applications, it might not be suitable for modern Internet of Things (IoT)-based applications. IoT applications, such as autonomous driving or wearable health monitoring applications, emit large volume data streams that need to be processed in near real-time fashion. Transferring data to the cloud for processing imposes challenges for latency-sensitive applications due to network latency. To tackle this problem, the processing can be moved to the edge of the network where the data are produced. However, edge devices usually have limited computational capacities. Therefore, there is a need to use both edge and cloud infrastructure at the same time to achieve low latency while having access to scalable and infinite resources. Application developers need to partition their applications and distribute them over the spectrum of edge and cloud devices based on their demand from the underlying infrastructure. This motivates the need to evaluate many different possible configurations in terms of the functionality and performance and selecting the ones that meet the needs of the application. My research program aims to provide a set of tools and techniques for evaluating the performance implication of different distribution policies and for testing the functionalities of partitioned applications. My long-term objective is to devise solutions for reliable and safe deployment of distributed data processing applications for real-time decision-making that utilize computing resources in a cost-effective and energy-aware manner. The first theme of this program, automated integration testing, focuses on partitioning applications into smaller modules and testing the integration of the partitioned application on given heterogeneous devices. The short-term objectives of this theme are to 1) devise effective application partitioning strategies and 2) automated generation of integration test scripts. The second theme of this program, performance evaluation of edge-cloud computing applications, will explore performance evaluation techniques for applications distributed over edge and cloud devices. The short-term objectives of this theme are to 3) predict the execution time of applications under different application modules offloading policies, 4) model the interference of co-located applications on edge computing resources, and 5) devise a scheduling technique that intelligently offloads application modules to shared computing resources. Outcomes of this program will help application operators maintain desired performance and functionality with effective use of edge and cloud resources and have great potential to be used in various application areas.

当今最先进的应用程序通常部署在云基础设施中，利用成本效益、弹性可伸缩性和无限资源的错觉。虽然这满足了许多应用领域的需求，如企业Web应用，但它可能不适合基于现代物联网(IoT)的应用。物联网应用程序，如自动驾驶或可穿戴式健康监测应用程序，会发出大量数据流，需要以接近实时的方式进行处理。由于网络延迟，将数据传输到云进行处理给延迟敏感型应用带来了挑战。为了解决这个问题，可以将处理移动到网络的边缘，在那里产生数据。然而，边缘设备通常具有有限的计算能力。因此，需要同时使用边缘和云基础设施，以实现低延迟，同时访问可扩展的无限资源。 应用程序开发人员需要根据其对底层基础设施的需求，对其应用程序进行分区，并将其分布在各种边缘和云设备上。这就需要从功能和性能方面评估许多不同的可能配置，并选择满足应用程序需求的配置。我的研究计划旨在提供一套工具和技术，用于评估不同分发策略的性能影响，并测试分区应用程序的功能。我的长期目标是为可靠和安全地部署分布式数据处理应用程序设计解决方案，以便以经济高效和节能的方式利用计算资源进行实时决策。 该程序的第一个主题是自动化集成测试，重点是将应用程序划分为较小的模块，并测试已分区的应用程序在给定的异类设备上的集成。这个主题的短期目标是1)设计有效的应用程序分区策略和2)自动生成集成测试脚本。该项目的第二个主题是边缘云计算应用的性能评估，将探索分布在边缘和云设备上的应用的性能评估技术。本课题的短期目标是3)预测不同应用模块卸载策略下应用程序的执行时间，4)模拟共存应用程序对边缘计算资源的干扰，5)设计一种智能地将应用程序模块卸载到共享计算资源的调度技术。该计划的成果将帮助应用运营商在有效利用边缘和云资源的情况下保持所需的性能和功能，并具有在各种应用领域使用的巨大潜力。