Orchestration and Management of Adaptive IoT-Centric Distributed Applications

Current Internet of Things (IoT) devices provide a diverse range of functionalities, ranging from measurement and dissemination of sensory data observation, to computation services for real-time data stream processing. In 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 utilize IoT resources while taking into account location, battery life, and other constraints of the underlying edge and IoT devices. A dynamic approach is proposed for orchestration and management of distributed workflow applications using services available in cloud data centers, deployed on servers, or IoT devices at the network edge. Our proposed approach is specifically designed for knowledge-driven business process workflows that are adaptive, interactive, evolvable and emergent. A comprehensive empirical evaluation shows that the proposed approach is effective and resilient to situational changes.

I. Introduction

The Emerging cloud and edge computing infrastructure provides new opportunities to develop next-generation Internet-centered distributed applications that are adaptive, evolvable and emergent [1], [2]. 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.