Joint Computation Offloading and Scheduling Optimization of IoT Applications in Fog Networks

Hazra, Abhishek; Adhikari, Mainak; Amgoth, Tarachand; Srirama, Satish Narayana

doi:10.1109/tnse.2020.3021792

Cited by 65 publications

(24 citation statements)

References 29 publications

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“…They solved the formulated problem using a deep Reinforcement Learning approach (deep Qlearning with a recurrent convolutional neural network) for learning the optimal decision action based on the previous observation-actions. The authors in [36] considered joint Computation Offloading and Scheduling Optimization problem in fog environment. A gateway is considered between the hierarchical IoT layer and the fog/cloud layer that is responsible for the scheduling strategy and offloading decision.…”

Section: Related Workmentioning

confidence: 99%

An efficient population-based multi-objective task scheduling approach in fog computing systems

2021

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With the rapid development of Internet of Things (IoT) technologies, fog computing has emerged as an extension to the cloud computing that relies on fog nodes with distributed resources at the edge of network. Fog nodes offer computing and storage resources opportunities to resource-less IoT devices which are not capable to support IoT applications with computation-intensive requirements. Furthermore, the closeness of fog nodes to IoT devices satisfies the low-latency requirements of IoT applications. However, due to the high IoT task offloading requests and fog resource limitations, providing an optimal task scheduling solution that considers a number of quality metrics is essential. In this paper, we address the task scheduling problem with the aim of optimizing the time and energy consumption as two QoS parameters in the fog context. First, we present a fog-based architecture for handling the task scheduling requests to provide the optimal solutions. Second, we formulate the task scheduling problem as an Integer Linear Programming (ILP) optimization model considering both time and fog energy consumption. Finally, we propose an advanced approach called Opposition-based Chaotic Whale Optimization Algorithm (OppoCWOA) to enhance the performance of the original WOA for solving the modelled task scheduling problem in a timely manner. The efficiency of the proposed OppoCWOA is shown by providing extensive simulations and comparisons with the original WOA and some existing meta-heuristic algorithms such as Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA).

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Section: Related Workmentioning

confidence: 99%

An efficient population-based multi-objective task scheduling approach in fog computing systems

2021

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“…The main objective of kth application is to minimize the overall processing time while meeting the QoS constraints. 35 1. Local processing: The local processing of an application depends on the computational frequency of the local embedded system instead of the latency.…”

Section: Processing Timementioning

confidence: 99%

“…The main objective of a remote service provider is to minimize the overall transmission and computation energy usage for improving the performance of the environment. 35 1. Uploading energy usage: The uploading energy consumption of an application k is defined as follows.…”

Section: Energy Consumptionmentioning

confidence: 99%

“…Those real‐time applications can be processed locally or distributed on local edge devices or centralized cloud servers. The real‐time applications offload the data to the remote service providers (edge or cloud service provider) through some gateway devices or actors using two policies, that is, binary offloading 35 and partial offloading 36 . In binary offloading, an application is fully offloaded to a remote edge device or cloud server for analysis.…”

Section: Overview Of Edge Computingmentioning

confidence: 99%

“…Initially, the embedded systems aim to process the applications by themselves. However, due to the limited CPU frequency constraint

(f_{e}^{CPU})

of the e th embedded system, the applications are uploaded to the remote devices that should meet the resource requirements of the applications 35,97 …”

Section: Mathematical Model Of Research Objectives In Edge Networkmentioning

confidence: 99%

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A comprehensive survey on nature‐inspired algorithms and their applications in edge computing: Challenges and future directions

2021

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Driven by the vision of real-time applications and smart communication, recent years have witnessed a paradigm shift from centralized cloud computing toward distributed edge computing. The main features of edge computing are to drag the cloud services toward the network edge with dramatic reductions of latency while increasing the resource utilization of the network and computing devices. Being the natural extension of cloud computing, edge computing inherits a variety of research challenges and brings forth different new issues to solve.These challenges are dealing with solving complex optimization problems including scheduling and processing real-time applications. Nature-inspired meta-heuristic (NIMH) algorithm is an overarching term in the field of an optimization problem that provides robust solutions to the NP-complete problems, from computationally tractable approximate solutions to real-time optimization strategies. Nowadays, different NIMH algorithms have been applied in the field of edge computing for solving various research challenges including resource placement and scheduling, communication, mobility, and edge controlling with higher efficiency. In this survey, we classify the existing NIMH into three categories based on their nature of works and included fuzzy logic and systems in the field of edge networks along with different research challenges. Further, we introduce different challenges and future directions to identify promising research works in edge computing.

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Hierarchical fuzzy‐based Quality of Experience (QoE)‐aware application placement in fog nodes

et al. 2022

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Fog computing or a fog network is a decentralized network placed in between data source and the cloud to minimize the network latency issues and thus support in-time service delivery, of Internet of Things (IoT) applications. However, placing computational tasks of IoT applications in fog infrastructure is a challenging task. State of the art focuses on quality of service and quality of experience (QoE) based application placement. In this article, we design hierarchical fuzzy based QoE-aware application placement strategy for mapping IoT applications with compatible instances in the fog network. The proposed method considers user application expectation parameters and metrics of available fog instances, and assigns the priority of applications using hierarchical fuzzy logic.The method later uses Hungarian maximization assignment algorithm to map applications with compatible instances. The simulation results of the proposed policy show better performance over the existing baseline algorithms in terms of resource gain (RG), processing time reduction ratio (PTRR), and similarly network relaxation ratio. When considering 10 applications in the fog network, our proposed method simulation results show 70.00%, 22.

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Joint Computation Offloading and Scheduling Optimization of IoT Applications in Fog Networks

Cited by 65 publications

References 29 publications

An efficient population-based multi-objective task scheduling approach in fog computing systems

An efficient population-based multi-objective task scheduling approach in fog computing systems

A comprehensive survey on nature‐inspired algorithms and their applications in edge computing: Challenges and future directions

Hierarchical fuzzy‐based Quality of Experience (QoE)‐aware application placement in fog nodes

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