Next generation communication networks are expected to accommodate a high number of new and resource-voracious applications that can be offered to a large range of end users. Even though end devices are becoming more powerful, the available local resources cannot cope with the requirements of these applications. This has created a new challenge called task offloading, where computation intensive tasks need to be offloaded to more resource powerful remote devices. Naturally, the Cloud Computing is a well-tested infrastructure that can facilitate the task offloading. However, Cloud Computing as a centralized and distant infrastructure creates significant communication delays that cannot satisfy the requirements of the emerging delay-sensitive applications. To this end, the concept of Edge Computing has been proposed, where the Cloud Computing capabilities are repositioned closer to the end devices at the edge of the network. This paper provides a detailed survey of how the Edge and/or Cloud can be combined together to facilitate the task offloading problem. Particular emphasis is given on the mathematical, artificial intelligence and control theory optimization approaches that can be used to satisfy the various objectives, constraints and dynamic conditions of this end-to-end application execution approach. The survey concludes with identifying open challenges and future directions of the problem at hand.

The potential offered by the abundance of sensors, actuators, and communications in the Internet of Things (IoT) era is hindered by the limited computational capacity of local nodes. Several key challenges should be addressed to optimally and jointly exploit the network, computing, and storage resources, guaranteeing at the same time feasibility for time-critical and mission-critical tasks. We propose the DRUID-NET framework to take upon these challenges by dynamically distributing resources when the demand is rapidly varying. It includes analytic dynamical modeling of the resources, offered workload, and networking environment, incorporating phenomena typically met in wireless communications and mobile edge computing, together with new estimators of time-varying profiles. Building on this framework, we aim to develop novel resource allocation mechanisms that explicitly include service differentiation and context-awareness, being capable of guaranteeing well-defined Quality of Service (QoS) metrics. DRUID-NET goes beyond the state of the art in the design of control algorithms by incorporating resource allocation mechanisms to the decision strategy itself. To achieve these breakthroughs, we combine tools from Automata and Graph theory, Machine Learning, Modern Control Theory, and Network Theory. DRUID-NET constitutes the first truly holistic, multidisciplinary approach that extends recent, albeit fragmented results from all aforementioned fields, thus bridging the gap between efforts of different communities.

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A Cyber-Physical Social System (CPSS) tightly integrates computer systems with the physical world and human activities. In this article, a three-level CPSS for early fire detection is presented to assist public authorities to promptly identify and act on emergency situations. At the bottom level, the system’s architecture involves IoT nodes enabled with sensing and forest monitoring capabilities. Additionally, in this level, the crowd sensing paradigm is exploited to aggregate environmental information collected by end user devices present in the area of interest. Since the IoT nodes suffer from limited computational energy resources, an Edge Computing Infrastructure, at the middle level, facilitates the offloaded data processing regarding possible fire incidents. At the top level, a decision-making service deployed on Cloud nodes integrates data from various sources, including users’ information on social media, and evaluates the situation criticality. In our work, a dynamic resource scaling mechanism for the Edge Computing Infrastructure is designed to address the demanding Quality of Service (QoS) requirements of this IoT-enabled time and mission critical application. The experimental results indicate that the vertical and horizontal scaling on the Edge Computing layer is beneficial for both the performance and the energy consumption of the IoT nodes.

Although mobile devices today have powerful hardware and networking capabilities, they fall short when it comes to executing compute-intensive applications. Computation offloading (i.e., delegating resource-consuming tasks to servers located at the edge of the network) contributes toward moving to a mobile cloud computing paradigm. In this work, a two-level resource allocation and admission control mechanism for a cluster of edge servers offers an alternative choice to mobile users for executing their tasks. At the lower level, the behavior of edge servers is modeled by a set of linear systems, and linear controllers are designed to meet the system’s constraints and quality of service metrics, whereas at the upper level, an optimizer tackles the problems of load balancing and application placement toward the maximization of the number the offloaded requests. The evaluation illustrates the effectiveness of the proposed offloading mechanism regarding the performance indicators, such as application average response time, and the optimal utilization of the computational resources of edge servers.

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Industry 4.0 applications rely on mobile robotic agents that execute many complex tasks that have strict safety and time requirements. Under this setting, the Edge Computing service delivery model allows the robotic agents to offload their computationally intensive tasks to powerful computing infrastructure in their vicinity. In this study, we propose a novel switching offloading mechanism for such robotic applications. In particular, we design opportunistic offloading strategies for the path planning and localization services of mobile robots. The offloading decision is based on the uncertainty of the robot's pose, the resource availability at the Edge of the network and the difficulty of the path planning. Our switching offloading framework is implemented and evaluated using a robot in a real Edge Computing testbed, where the trade-off between execution time and the successful completion of the robot trajectory is highlighted.