Abstract: 

The emerging cloud and edge computing infrastructure provides new opportunities to develop next- generation Internet-centered distributed applications that are adaptive, evolvable, and emergent. Such applications may include knowledge-driven distributed workflows that are dynamically orchestrated and managed by utilizing computation, data, and storage resources available in a cloud data center, enterprise networks as well as Internet of Things (IoT) devices. IoT devices in such workflows provide a diverse range of functionalities, from measurement and dissemination of sensory data observation to computation services for real-time data stream processing. In workflows that are designed for extreme situations, such as emergencies, a significant benefit of IoT devices is that they can help gain a more complete situational understanding of the environment. However, this requires the ability to effectively utilize resource constrained IoT devices.

The IoT-centric applications that we consider in this dissertation are knowledge-driven workflows. A unique aspect that differentiates them from traditional workflows (business processes, scientific workflows, etc.) is that they are emergent and their execution evolves based on the knowledge of the execution status, environmental context, and situation and case-specific parameters that are not known a priori and are subject to change at runtime. Moreover, in such workflows, the binding of tasks to service/resource endpoints may not be known at design time. The orchestration and management of such workflows therefore requires dynamic discovery, selection and binding of workflow tasks to available cloud or edge resources as well as establishing coordination between these resources based on the functional and nonfunctional requirements of the workflow.

In this dissertation, we develop an integrated framework that supports dynamic orchestration and management of IoT-centric and knowledge-driven workflow applications in the cloud and edge computing environment. Users submit their workflow orchestration and management requests to a workflow coordinator by providing workflow specifications. The different tasks in the workflow may run on computation/data resources on the cloud, edge nodes and on IoT devices. These edge nodes and IoT devices may be geographically distributed. For workflow orchestration, the coordinator dynamically binds the workflow tasks to the services available in the cloud or edge/IoT devices based on the location and context requirements of tasks. The binding may need to change in real time as the dynamics of the underlying environment change.

There are two main underlying approaches that perform dynamic orchestration and management in the proposed framework. The first approach is Global Orchestration and Management (GOM) approach, which is a centralized approach and it completely rely on Global Coordinator for binding, execution, deployment and adaptation of all the workflow related activities. The Global Coordinator maintains the global information of all the edge resources and cloud services. The second approach is Local Orchestration and Management (LOM) approach which, performs orchestration and management in a distributed manner by different peer nodes. A comprehensive experimental evaluation performed by emulating real-time data streaming workflows shows the effectiveness of our proposed approaches.

List of Publications:

1. Sehrish Amjad, Ahmed Akhtar, Muhammad Ali, Ayesha Afzal, Basit Shafiq, Jaideep Vaidya, Shafay Shamail, and Omer Rana, “Orchestration and Management of Adaptive IoT-Centric Distributed Applications”. IEEE Internet of Things Journal, vol. 11, no. 3, pp. 3779-3791, 1 Feb. 2024. doi:10.1109/JIOT.2023.3306238.

2. Sehrish Amjad, Ahmed Akhtar, Muhammad Ali, Basit Shafiq, Shafay Shamail, Ayesha Afzal, and Jaideep Vaidya, “Demo: Orchflow: Orchestration and Management of IoT Centric Distributed Workflows,” accepted for publication in the Proceedings of the 44th IEEE International Conference on Distributed Computing Systems (ICDCS), 23 July – 26 July 2024, Jersey City, New Jersey, USA.