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Abstract
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Introduction
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Discussion and Comparison
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Computer Science

Scheduling Algorithms in Fog Computing: A Survey

Profile image of Khaled MatroukKhaled Matrouk

2021, International Journal of Networked and Distributed Computing

https://doi.org/10.2991/IJNDC.K.210111.001
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Abstract

Nowadays, with the development of technology, the Internet of Things (IoT) applications have become parts of our daily life. Consequently, the number of devices used in these applications will increase, leading to the creation of huge amounts of data. This data will be transferred to cloud computing for processing, and because the cloud is far from these devices, there will be a delay in the response. From here, it was necessary to find a new technology closer to the Internet of Things devices and overcome the problems in the cloud. So, Cisco proposed fog computing in 2012 [1], which placed between cloud computing and the IoT devices (end users). Fog computing is an emerging computing paradigm that extends cloud computing from the core of the network to the edge of the network. It aims to bring computation, storage and networking services close to users [2]. Internet of Things applications is connecting every physical object like cameras, vehicles, sensors, wearables, and home appliances [3]. Many applications require low latency, mobility, location awareness, high response time. Although many researchers have developed algorithms to improve the performance of cloud computing about the IoT applications, there are still many challenges regarding the requirements of the IoT applications and mobile services such as low latency, high response time, cost, support for mobility and geo-distributions [4]. They can be addressed these challenges by fog computing [5]. Kazem [6] presents a comparison between fog and cloud computing according to their latency and response time. As a result of the comparison, fog computing always performs better than cloud computing to meet the demands of time-sensitive applications by reducing the delay and response time. Through the prediction of Cisco, there are more than 50 billion devices that will be connected to the internet by 2020. Also, the data produced by users, devices and their interactions will reach 500 zettabytes [7]. The general fog computing architecture can be divided into three layers, as shown in Figure 1. The first is IoT devices layer that includes different types of devices, such as smartphones, smart vehicles, tablet computers, and various smart home devices. This layer can sense the surrounding environment and collect data through sensor devices, and communicate with the fog computing layer through 3G, 4G, 5G, WiFi, and Bluetooth technologies. The second is the middle layer is the fog computing layer that includes routers, gateways, workstations, switches, access points. This layer has the capability of computing, networking, and storage. Finally, the upper layer is cloud computing that includes cloud servers with high computation power [2,8-10]. Changes like user devices in terms of bandwidth, storage, latency and computation make resource management in the fog computing environment a major issue [11]. Scheduling is the main challenge in fog computing. In a fog environment, tasks are divided into two groups: tasks requiring the computing intensity and tasks requiring the data intensity. While scheduling the tasks requiring computing intensity, the scheduler migrates the data to the high productivity resource, and hence, the task execution time is reduced. On the other hand, while scheduling

Figures (11)
Figure 1 Fog computing architecture.
Figure 1 Fog computing architecture.
Table 1 Metrics considered of current scheduling algorithms in fog environment the failure rate of associated tasks scheduling with mixed deadlines. Anyway, the authors consider the scheduling of tasks that include only associated tasks with no independent tasks. They do not also propose putting the task to a nearby edge server which would be more benefit those delay-sensitive tasks. Wu et al. [34] propose an energy-efficient called Estimation of Distribution (EDA-P), scheduling of three tiers of IoT systems. T evolutionary algorithm to address the resource he EDA-P algorithm is used for producing task permutation. The experiments performed on both single and multi-application cases show that the proposed algo- rithm is effective in reducing make-span, t he energy consumption of devices and prolonging the lifetime of IoT devices. On the other hand, the proposed algorithm does not address the application with soft or hard deadlines, and it does not consid er the execution cost.
Table 1 Metrics considered of current scheduling algorithms in fog environment the failure rate of associated tasks scheduling with mixed deadlines. Anyway, the authors consider the scheduling of tasks that include only associated tasks with no independent tasks. They do not also propose putting the task to a nearby edge server which would be more benefit those delay-sensitive tasks. Wu et al. [34] propose an energy-efficient called Estimation of Distribution (EDA-P), scheduling of three tiers of IoT systems. T evolutionary algorithm to address the resource he EDA-P algorithm is used for producing task permutation. The experiments performed on both single and multi-application cases show that the proposed algo- rithm is effective in reducing make-span, t he energy consumption of devices and prolonging the lifetime of IoT devices. On the other hand, the proposed algorithm does not address the application with soft or hard deadlines, and it does not consid er the execution cost.
Table 2 A comparison of the current scheduling algorithms in fog environment—Continued
Table 2 A comparison of the current scheduling algorithms in fog environment—Continued
Table 2. A comparison of the current scheduling algorithms in fog environment—Continued
Table 2. A comparison of the current scheduling algorithms in fog environment—Continued
Figure 3 Percentage of the applied case studies of scheduling algorithms in fog computing.
Figure 3 Percentage of the applied case studies of scheduling algorithms in fog computing.
Figure 4 Percentage of the presented evaluation tools in the literature.
Figure 4 Percentage of the presented evaluation tools in the literature.
Figure 5 Percentage of performance metrics for evaluating scheduling algorithms.
Figure 5 Percentage of performance metrics for evaluating scheduling algorithms.

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A Survey on Scheduling the Task in Fog Computing Environment
Faiza Ishaq 169-FBAS/PHDCS/F20

arXiv (Cornell University), 2023

With the rapid increase in the Internet of Things (IoT), the amount of data produced and bullet.png processed is also increased. Cloud Computing facilitates to handle storage, processing, and analysis of data as needed. However cloud computing devices are located far away from the IoT devices. Fog computing has emerged as a small cloud computing paradigm that is near to the edge devices and handles the task very efficiently. Fog nodes have a small storage capability than the cloud node but it is designed and deployed near to the edge device so that request must be accessed efficiently and executes in time. In this survey paper we have investigated and analysed the main challenges and issues raised in scheduling the task in fog computing environment. To the best of our knowledge there is no comprehensive survey paper on challenges in task scheduling of fog computing paradigm. In this survey paper research is conducted from 2018 to 2021 and most of the paper selection is done from 2020-2021. Moreover this survey paper organizes the task scheduling approaches and technically plans the identified challenges and issues. Based on the identified issues, we have highlighted the future work directions in the field of task scheduling in fog computing environment.

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TASK SCHEDULING IN FOG COMPUTING ENVIRONMENT: AN OVERVIEW
IJETMS IJETMS

The number of Internet of Things (IoT) devices and sensors has significantly expanded in recent years. In general, fog computing increases cloud-based processing, storage, and networking capabilities by being located closer to IoT devices and sensors. Task scheduling is used to finish work in a set amount of time using a finite number of resources. The completion of tasks within the allotted time in fog computing is a key difficulty due to the increased amount of data that needs to be processed. Additionally, in order to identify current research gaps in the field of fog, we map the existing works to the taxonomy. This article offers a broad overview of various task and resource scheduling techniques used in fog computing. It examines and contrasts several techniques created for a fog computing environment to ascertain their contributions and limitations. Moreover, it offers encouraging study directions for other researchers working in this area.

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An Optimal Task Scheduling Towards Minimized Cost and Response Time in Fog Computing Infrastructure
Kunal Bhaisare

2019 International Conference on Information Technology (ICIT), 2019

Due to the transformation and expansion of Internet of Things(IoT), a large number of services are deployed on the edge of the network to provide the services to the end users rather than from the cloud data center since processing the data at the edge can reduce the response time and bandwidth cost while fulfilling the Quality of services(QoS). The fog-cloud computing environment offers promising solution to provision the available resources for IoT based application.Undoubtely Fog computing compliment of cloud computing helps to provide efficient solution to deal with diverse IoT application. However to provide efficient solution in such environment is challenge in different IoT based application such as health care applications, intelligent transportation system and smart cities. Task scheduling and Resource allocation are the NP-hard issues in distributed computing. Each Application consists of several modules that requires resources to execute. However, providing an optimal task scheduling policy in such a heterogeneous system is a NP class problem and has been proposed by different methods like Greedy, meta-heuristic and all nature inspired algorithm for solving an NP-complete problem. The task scheduling problem is a key challenge in the distributed computing system. In this paper, we are trying to map the independent task into the fog layer and our algorithm gives good result if we place the services in fog layer rather than cloud data center. The system resources available may be CPU, RAM, etc by assigning some priority to the task based on its deadline. Also, we have assumed that once a task assigned to a particular node will not leave that until its execution complete. In this paper, we also proposed a three-layer architecture for efficient task scheduling for application such as health care in smart homes.

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A Survey on Various Methods and Algorithms of Scheduling in Fog Computing
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2021

Received: 4 January 2021 Accepted: 16 March 2021 The rapid deployment of IoT in different areas generates a massive amount of data transferred to the Cloud. To solve this challenge a new paradigm, called Fog Computing, is located at the edge of the network and close to the connected objects. Its main role is to extend the capacities of Cloud and improve the performance and the QoS required by the applications by the use of different methods and techniques based on scheduling algorithms. In this paper, we review various recent studies available in the literature that are interested in the scheduling methods and algorithms used in Fog computing. The use of fog layer, in solving optimization problem, is faced with serious challenges. Therefore, to help practitioners and researchers, we present an in-depth overview of Fog Computing studying various scheduling methods and algorithms. We analyze, compare and classify these different scheduling approaches according to the nature of the alg...

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A job scheduling algorithm for delay and performance optimization in fog computing
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Concurrency and Computation: Practice and Experience, 2019

SummaryDue to an ever‐increasing number of Internet of Everything (IoE) devices, massive amounts of data are produced daily. Cloud computing offers storage, processing, and analysis services for handling of such large quantities of data. The increased latency and bandwidth consumption is not acceptable to real‐time applications like online gaming, smart health, video surveillance, etc. Fog computing has emerged to overcome the increase in latency and bandwidth consumption in Cloud computing. Fog Computing provides storage, processing, networking, and analytical services at the edge of a network. As Fog Computing is still in its infancy, its significant challenges include resource‐allocation and job‐scheduling. The Fog devices at the edge of the network are resource‐constrained. Therefore, it is important to decide the assignment and scheduling of a job on a Fog node. An efficient job scheduling algorithm can reduce energy consumption and response time of an application request. In t...

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    Recently, intelligent scheduling approaches using surrogate models have been proposed to efficiently allocate volatile tasks in heterogeneous fog environments. Advances like deterministic surrogate models, deep neural networks (DNN) and gradient-based optimization allow low energy consumption and response times to be reached. However, deterministic surrogate models, which estimate objective values for optimization, do not consider the uncertainties in the distribution of the Quality of Service (QoS) objective function that can lead to high Service Level Agreement (SLA) violation rates. Moreover, the brittle nature of DNN training and the limited exploration with low agility in gradient-based optimization prevent such models from reaching minimal energy or response times. To overcome these difficulties, we present a novel scheduler that we call GOSH for Gradient Based Optimization using Second Order derivatives and Heteroscedastic Deep Surrogate Models. GOSH uses a second-order gradi...

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    Workflow scheduling is a long-studied problem in parallel and distributed computing (PDC), aiming to efficiently utilize compute resources to meet user's service requirements. Recently proposed scheduling methods leverage the low response times of edge computing platforms to optimize application Quality of Service (QoS). However, scheduling workflow applications in mobile edge-cloud systems is challenging due to computational heterogeneity, changing latencies of mobile devices and the volatile nature of workload resource requirements. To overcome these difficulties, it is essential, but at the same time challenging, to develop a long-sighted optimization scheme that efficiently models the QoS objectives. In this work, we propose MCDS: Monte Carlo Learning using Deep Surrogate Models to efficiently schedule workflow applications in mobile edge-cloud computing systems. MCDS is an Artificial Intelligence (AI) based scheduling approach that uses a tree-based search strategy and a deep neural network-based surrogate model to estimate the long-term QoS impact of immediate actions for robust optimization of scheduling decisions. Experiments on physical and simulated edge-cloud testbeds show that MCDS can improve over the state-of-the-art methods in terms of energy consumption, response time, SLA violations and cost by at least 6.13, 4.56, 45.09 and 30.71 percent respectively.

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    Task scheduling in fog computing is one of the areas where researchers are having challenges as the demand grows for the use of internet of things (IoT) to access cloud computing resources. Many resource scheduling and optimization algorithms were used by many researchers in fog computing; some used single techniques while others used combined schemes to achieve dynamic scheduling in fog computing, many optimization techniques were assessed based on deterministic and meta-heuristic to find out solution to task scheduling problem in fog computing but could not achieve excellent results as required. This article proposes hybrid meta-heuristic optimization algorithm (HMOA) for energy efficient task scheduling in fog computing, the study combined modified particle swarm optimization (MPSO) meta-heuristic and deterministic spanning tree (SPT) to achieve task scheduling with the intention of eliminating the drawbacks of the two algorithms when used separately, the MPSO was used to schedul...

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    Fog computing is a paradigm that allows the provisioning of computational resources and services at the edge of the network, closer to the end devices and users, complementing cloud computing. The heterogeneity and large number of devices are challenges to obtaining optimized resource allocation in this environment. Over time, some surveys have been presented on resource management in fog computing. However, they now lack a broader and deeper view about this subject, considering the recent publications. This article presents a systematic literature review with a focus on resource allocation for fog computing, and in a more comprehensive way than the existing works. The survey is based on 108 selected publications from 2012 to 2022. The analysis has exposed their main techniques, metrics used, evaluation tools, virtualization methods, architecture, and domains where the proposed solutions were applied. The results show an updated and comprehensive view about resource allocation in fo...

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