The Internet of Everything paradigm is being rapidly adopted in developing applications for different domains like smart agriculture, smart city, big data streaming, and so on. These IoE applications are leveraging cloud computing resources for execution. Fog computing, which emerged as an extension of cloud computing, supports mobility, heterogeneity, geographical distribution, context awareness, and services such as storage, processing, networking, and analytics on nearby fog nodes. The resource-limited, heterogeneous, dynamic, and uncertain fog environment makes task scheduling a great challenge that needs to be investigated. The article is motivated by this consideration and presents a systematic, comprehensive, and detailed comparative study by discussing the merits and demerits of different scheduling algorithms, focused optimization metrics, and evaluation tools in the fog computing and IoE environment. The goal of this survey article is fivefold. First, we review the fog computing and IoE paradigms. Second, we delineate the optimization metric engaged with fog computing and IoE environment. Third, we review, classify, and compare existing scheduling algorithms dealing with fog computing and IoE environment paradigms by leveraging some examples. Fourth, we rationalize the scheduling algorithms and point out the lesson learned from the survey. Fifth, we discuss the open issues and future research directions to improve scheduling in fog computing and the IoE environment.

https://dl.acm.org/doi/pdf/10.1145/3513002

Resource Allocation and Task Scheduling in Fog Computing and Internet of Everything Environments: A Taxonomy, Review, and Future Directions

In today’s era of Internet of Things (IoT), efficient and real-time processing of massive data generated by IoT device has become the primary issue for traditional cloud computing network architectures. As a supplement of cloud computing, edge computing enhances the real-time performance of service completion by offloading services to edge servers closer to the terminal device for execution, while reducing power consumption and computing load in the cloud. In this article, we propose the following solutions to resolve the different requests of the IoT device: in an “edge-cloud” heterogeneous network environment, create a mapping scheme between application modules and basic resource equipment, considering the two factors of tolerant task latency and system power consumption. In the application step-by-step execution process, heuristic dynamic task processing algorithm is used to reduce the task latency time. Experiments with the “iFogSim” simulator show that, application service quality is significantly improved and system power consumption is greatly reduced, which compared with the stable application module placement strategy and the static task scheduling strategy.

IoT Application Modules Placement and Dynamic Task Processing in Edge-Cloud Computing

For fog computing network, how to effectively and quickly offload the task to fog nodes is a big challenge. In this paper, a task offloading scheme based on improved contract net protocol and beetle antennae search algorithm in fog computing networks is proposed. Firstly, the system mode of task offloading in fog computing network is described. Then, a contract net protocol is presented to obtain the information from the fog nodes. Based on the information, the agent will allocate the sub-tasks to the fog nodes. And the task offloading issue in fog computing network is formulated and analyzed. Next, an efficient task offloading scheme based on improved contract net protocol and beetle antennae search algorithm is proposed. Finally, the analysis and simulation results validate the efficiency of the proposed scheme compared with other algorithms.

Task Offloading Scheme Based on Improved Contract Net Protocol and Beetle Antennae Search Algorithm in Fog Computing Networks

Due to increased use of IoT devices and data sensors a huge amount of data is being produced for processing in real‐time. Fog computing has evolved as a solution for fast processing of data. To complete the data processing as per requirement of user, it is processed at fog nodes which are near the user. To complete work in a specified time with limited resources, task scheduling is performed. With the increased amount of data to be processed, the completion of task within given time in fog computing is a major challenge. So, scheduling of tasks and resources is a very important issue. A lot of research has been undertaken in recent years. In this survey, authors have reviewed various task scheduling algorithms suggested by researchers to meet the user requirements. The focus of this article is on diverse scheduling techniques being deployed in fog computing. An effort has been made to classify existing approaches, research issues and determine significant issues existing in this field. There are four major scheduling categories that are used by researchers namely, static, dynamic, heuristic, and hybrid. As per this study, 17% researchers deployed static, 23% dynamic, 47% heuristic, and 13% hybrid approaches, respectively. Analysis shows that for QoS (Quality of Service) parameters 19% researchers are focused on response time, 18% on cost and energy consumption, and 16% on makespan. For QoS parameter other factors have much smaller contribution in comparison to the above factors. In the tools used, researchers observed that 40% of the researches have used iFogSim, according to literature. Besides, this research article also discusses open issues and future work in the field of fog computing. This article also underlines various open issues and future directions in the field of fog computing.

A systematic review of task scheduling approaches in fog computing

Optimal fog node selection based on hybrid particle swarm optimization and firefly algorithm in dynamic fog computing services

The Fog computing paradigm allow applications to be processed at the edge of a network. This paradigm is designed to mitigate high latency and the burden of task requests sent to centralized cloud servers by end devices. Fog computing permits different portions of applications to be scheduled to Fog nodes available at the edge. These Fog nodes offer cloud processing services and have appeared as a feasible technique for real time applications. However, scheduling a task among available Fog nodes must be effective, meaning it must not over consume available resources because of limited resources at the edge. Consuming extra amount of energy than available on the Fog nodes can lead to network breakdown or application failure which is not acceptable for real-time applications. Therefore, to address this challenge, this paper presents an application scheduling technique based on virtualization technology to find an efficient algorithm that can optimize energy consumption and average delay of real-time applications in Fog computing networks. This is achieved by implementing four task scheduling policies in a Fog node scheduler to assess their performance and efficiency. Simulations were conducted using the iFogSim tool and the results demonstrate that the FCFS scheduling policy achieved improvement in energy consumption by 11 %, average task delay 7.78 %, 4.4 % network usage and execution time 15.1 % better than other algorithms.

Multi-Objective Optimization Approach for Task Scheduling in Fog Computing

Fog computing extends the traditional centralized cloud-computing model to the edge of the network to provide cloud services such as computation and networking distributed closer to the end device. For Smart Farming applications, Fog computing can enable real-time analysis of crop and environmental behavior to improve the production of the agricultural sector. However, being at the edge results in limited physical underlying infrastructure thus lessens fog nodes (Fog node) resources compare to the cloud resulting in resource overconsumption problem and network performance downgrade if not managed. Therefore, to address resource enhancement problem. In this paper, we propose a K-Means based clustering algorithm for a Smart Farming application, compared network performance with first come first serve (FCFS) algorithms. Simulation results demonstrate that K-Means outperforms FCFS by 1.80 % on energy consumption, 1.2 % lesser on network usage, and 27 % lesser on end-to-end delay.

K-Means Based on Resource Clustering for Smart Farming Problem in Fog Computing

With the pacing of the next industrial revolution and adoption of industrial internet of things. It’s difficult for emerging markets to keep-up with the pacing evolution of technology due to economic reasons of course. However, edge computing has emerged as a promising technology that also caters for emerging markets enabling them to pace up with the evolution of technology. Edge computing is defined as distributed computing paradigm in which computation is largely or completely performed on distributed edge device nodes known as edge computing nodes as opposed to primarily taking place in a centralized cloud environment. This computing paradigm has been adopted mostly to reduce the workload from cloud computing or to provide computing closer to the user in order to improve performance. This paper presents a review of three edge deployment architectures focusing on their impact on emerging markets. Furthermore, present discussion of use cases in emerging markets and their business case. We then provide a framework promising to cater for edge challenges and finally provide simulation results performed on iFogSim as a framework proof of concept. The simulation result shows edge perfoms better than cloud interms of resource use. Therefore, the proposed framework is recommended and reliable for emerging markets use cases. This work only modelled a precision farming use case.

Edge Computing for Emerging Markets Addressing African Needs

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Multi-Objective Optimization Approach for Task Scheduling in Fog Computing

K-Means Based on Resource Clustering for Smart Farming Problem in Fog Computing

Edge Computing for Emerging Markets Addressing African Needs