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: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=758743
• 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.

当今的最新应用程序通常在云基础架构中部署，利用成本收益，弹性可扩展性以及无限资源的幻觉。尽管这满足了许多应用程序域（例如Enterprise Web应用程序）的需求，但它可能不适用于现代的物联网（IoT）基于基于的应用程序。物联网应用程序（例如自动驾驶或可穿戴健康监测应用程序）会发出大量的数据流，这些数据流需要几乎实时的方式处理。将数据传输到云进行处理，这是由于网络延迟引起的延迟敏感应用程序的挑战。为了解决此问题，可以将处理移至生成数据的网络边缘。但是，边缘设备通常具有有限的计算能力。因此，有必要同时使用边缘和云基础架构，以实现低潜伏期，同时访问可扩展和无限资源。应用程序开发人员需要根据基础架构的需求来分配其应用程序并在边缘和云设备的范围内分配它们。这促使需要根据功能和性能评估许多不同可能的配置，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组工具和技术，以评估不同分配策略的性能含义并测试分区应用程序的功能。我的长期目标是设计解决方案，以可靠，安全地部署分布式数据处理应用程序，以实时决策，以以具有成本效益和能源感知的方式利用计算资源。该程序的第一个主题是自动集成测试，重点是将应用程序分配到较小的模块中，并测试在给定的异质设备上分区应用程序的集成。该主题的短期目标是1）设计有效的应用程序分配策略和2）自动生成集成测试脚本。该程序的第二个主题是边缘云计算应用程序的性能评估，将探索分布在边缘和云设备上的应用程序的性能评估技术。该主题的短期目标是3）预测在不同应用模块下卸载策略下应用程序的执行时间，4）建模共同关联的应用程序在边缘计算资源上的干扰，5）设计一种调度技术，该计划可以智能地将应用程序模块卸载到共享计算资源。该程序的结果将帮助应用程序操作员通过有效利用边缘和云资源来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。