DEEDSP: Deadline‐aware and energy‐efficient dynamic service placement in integrated Internet of Things and fog computing environments

Abstract

Fog computing has become adaptable and also as a promising infrastructure for providing elastic resources at the edge of the network. Fog computing reduces the transmission latency and consumption of bandwidth while processing the incoming requests from various Internet of Things (IoT) devices.Moreover, fog computing can support and facilitate geographically distributed applications with low and predictable latency. However, this technology also has significant research issues in its current stage such as successful implementation of service location models. In this article, we propose a deadline-aware and energy-efficient dynamic service placement (DEEDSP) technique for fog computing that supports the placement of IoT based services. Further, hyper-heuristic algorithm based energy-efficient service placement technique is proposed to balance the energy-delay trade-off based on different service placement decision criteria (eg, minimum response time or energy consumption).The proposed algorithm is able to dynamically minimize the energy consumption of the system while ensuring that the response time satisfies a given time constraint. Finally, the proposed technique is evaluated in simulated fog computing environment and experimental results show that this technique performs better than state-of-the-art placement techniques in terms of energy and latency.

Edge AI: A survey

“…Each sensor detects car movements and makes decisions accordingly, and traffic lights can react to this processed information. Another critical scenario is intelligent car parking, where users will access information about urban car parking spaces according to their geographical location [89]. Currently, an intelligent transportation environment uses cloud computing, where all the processing is done at remote data centres, but cloud computing lacks key safety features in smart transportation [56].…”

“…The authors propose a resilient application platform to provide core services (for example, distributed data management) and mechanisms to monitor and optimize functionality in relatively long-lived distributed applications [90]. An alternative argument is to improve the resilience by moving the functionality closer to the end devices by relying less on any less reliable links (wireless in particular) [89].…”

“…Conversely, MEC could be used to reduce latency for AR applications without resorting to ICN. Synergies are, however, possible when MEC and ICN are structured to work cooperatively [89].…”

Self-Socio Adaptive Reliable Particle Swarm Optimization Load Balancing in Software-Defined Networking

“…Packets delivered increased with less delay, but the nodes selected to transfer the packets were not verified to be reliable or not. The authors in [22] proposed an energy-efficient service placement technique in fog computing by minimizing the response time of latency-sensitive applications. The application deadlines, application makespan time, resources of the application module, and fog node capacities were considered to dynamically place the services.…”

A Hybrid Meta-Heuristic Algorithm for Application Module Placement in Iot-Fog-Cloud Computing Environment

“…In [23], the authors proposed a deadline-aware and energy-efficient service module placement strategy and hyperheuristic algorithm in the fog system to stabilise the tradeoff connecting energy consumption and latency, considering various service placement decision criteria. The proposed approach significantly reduces the power usage of the fog system and also ensures the satisfaction of the application's Quality of Service (QoS) requirements.…”