Task Placement on Fog Computing Made Efficient for IoT Application Provision

Abstract

Fog computing is one of the promising technologies for realizing global-scale Internet of Things (IoT) applications as it allows moving compute and storage resources closer to IoT devices, where data is generated, in order to solve the limitations in cloud-based technologies such as communication delay, network load, energy consumption, and operational cost. However, this technology is still in its infancy stage containing essential research challenges. For instance, what is a suitable fog computing scheme where effective service provision models can be deployed is still an open question. This paper proposes a novel multitier fog computing architecture that supports IoT service provisioning. Concretely, a solid service placement mechanism that optimizes service decentralization on fog landscape leveraging context-aware information such as location, response time, and resource consumption of services has been devised. The proposed approach optimally utilizes virtual resources available on the network edges to improve the performance of IoT services in terms of response time, energy, and cost reduction. The experimental results from both simulated data and use cases from service deployments in real-world applications, namely, the intelligent transportation system (ITS) in Ho Chi Minh City, show the effectiveness of the proposed solution in terms of maximizing fog device utilization while reducing latency, energy consumption, network load, and operational cost. The results confirm the robustness of the proposed scheme revealing its capability to maximize the IoT potential.

Priority, network and energy‐aware placement of IoT‐based application services in fog‐cloud environments

“…The algorithm prioritises the services based on their deadline. Similar to this work, the authors in [11] propose a context‐aware service placement approach in fog computing with the same objective function and deadline constraint. However, they present an appropriate model to estimate response time, including communication, deployment, and execution time using IBM CPLEX [23].…”

“…We list the characteristics of FNs and PMs in Table 3. Since real datasets are not available for attributes of services and the network topology of FCs in the fog environments [20], similar to [6, 8, 9, 11, 13, 18, 20], in this work we utilise the synthetic dataset for our experiments. We present the attributes of considered services in Table 4.…”

“…There exist several approaches for solving application placement problem in fog–cloud environments [5–20] which have some limitations as follows. Some of them do not consider the priority of applications and assume that IoT applications are identical in terms of QoS requirements [6–8, 10, 15, 17].…”

CREW: Cost and Reliability aware Eagle‐Whale optimiser for service placement in Fog

“…The highly distributed and heterogeneous features of the Fog environment introduce more complexities when compared to centralised systems. Existing Fog service placement schemes consider various QoS metrics such as response time, energy, maximum utilisation of Fog resources, and cost 4 …”

Multi-Objective Optimization of Task-to-Node Assignment in Opportunistic Fog RAN

“…The notion of a cloud radio access network (Cloud-RAN) was developed to support these goals by enabling flexible splitting of RAN functionalities between radio access points (RAPs) and the cloud, based on the availability of cloud resources [2,3]. However, the Cloud-RAN may suffer from a heavy workload in its centralized baseband unit (BBU) pool, coerced backhaul capacity and difficulty in meeting the low latency requirements of delay-sensitive applications [4,5]. In order to address these challenges, the fog radio access network (F-RAN) was proposed.…”