Grant: Discovery Grants Program - Individual

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.