Optimized task scheduling and preemption for distributed resource management in fog-assisted IoT environment

Wadhwa, Heena; Aron, Rajni

doi:10.1007/s11227-022-04747-2

Cited by 21 publications

(7 citation statements)

References 39 publications

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“…In the IoT-fog network, a task scheduling method was proposed to allocate resources to IoT tasks, which optimally selects the best resources to execute the tasks [41,42]. Ranumayee et al [43] used the evolutionary learning method to optimize energy, makespan, and cost and schedule tasks in the IoT-fog-cloud network.…”

Section: Related Workmentioning

confidence: 99%

An Optimal Scheduling Method in IoT-Fog-Cloud Network Using Combination of Aquila Optimizer and African Vultures Optimization

Liu¹,

Kosarirad

Meisami

et al. 2023

Processes

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Today, fog and cloud computing environments can be used to further develop the Internet of Things (IoT). In such environments, task scheduling is very efficient for executing user requests, and the optimal scheduling of IoT task requests increases the productivity of the IoT-fog-cloud system. In this paper, a hybrid meta-heuristic (MH) algorithm is developed to schedule the IoT requests in IoT-fog-cloud networks using the Aquila Optimizer (AO) and African Vultures Optimization Algorithm (AVOA) called AO_AVOA. In AO_AVOA, the exploration phase of AVOA is improved by using AO operators to obtain the best solution during the process of finding the optimal scheduling solution. A comparison between AO_AVOA and methods of AVOA, AO, Firefly Algorithm (FA), particle swarm optimization (PSO), and Harris Hawks Optimization (HHO) according to performance metrics such as makespan and throughput shows the high ability of AO_AVOA to solve the scheduling problem in IoT-fog-cloud networks.

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Section: Related Workmentioning

confidence: 99%

An Optimal Scheduling Method in IoT-Fog-Cloud Network Using Combination of Aquila Optimizer and African Vultures Optimization

Liu¹,

Kosarirad

Meisami

et al. 2023

Processes

View full text Add to dashboard Cite

show abstract

“…During task allocation, the execution strategy corresponding to the solution x needs to be solved to meet the dependencies between tasks and environmental parameters, that is, the solution obtained should be in the feasible space. In the research of task allocation, the existing deep reinforcement learning methods usually regard it as an end-to-end learning task, and have designed different models and training methods [11][12][13][14][15][16][17]. However, by exploring each step of action, the model will not get a reward function value for completing the task until the whole scheduling task is completed, resulting in sparse rewards, large state space, and difficulty in training.…”

Section: Graph Convolution Fusion Scheduling Modelmentioning

confidence: 99%

Task Scheduling Strategy of Logistics Cloud Robot Based on edge computing

Tang,

Jiao,

Xue

et al. 2023

Preprint

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The logistics industry has entered the era of highly information-based intelligent logistics. In order to save resource consumption and improve the cooperation efficiency between robots, a centralized computing mode has emerged in the application of warehousing robots, namely, logistics cloud robots. In order to improve the work efficiency of the logistics cloud robot, this paper designs a task resource scheduling model of the cloud logistics robot in the edge computing environment. The logistics cloud robot can improve the response efficiency by unloading some delay sensitive tasks to the edge server, and assign the delay insensitive tasks to the cloud computing center. Therefore, it is particularly important to reasonably allocate tasks to appropriate computing cells. The model combines graph convolution network and heuristic algorithm, and designs a joint scheduling strategy to unload requests to edge services and cloud servers reasonably, so as to maximize the use of computing resources. Based on the joint scheduling strategy, the optimal long-term scheduling decision is generated to reduce scheduling time and improve service quality. To evaluate the effectiveness of the model, we use heterogeneous earliest completion time (HEFT) and other algorithms as benchmarks to compare the performance of the model. In order to verify the authenticity of the scheduling scheme, we conducted simulation experiments on the EdgeCloudSim platform to verify the quality of service of task scheduling in different scenarios. The experimental results show that the proposed method is superior to most of the comparison methods and can better optimize the task scheduling efficiency of the logistics cloud robot.

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“…However, the subsequent effect on response time was not elaborated. A task scheduling and preemption model (OSCAR) used clustering, heap-based optimizer, and a deep Q-network to decrease response time, makespan, waiting time, and SLA violations in [53]. It also improved system throughput while satisfying deadlines.…”

Section: ) Reinforcement Learning (Rl) Based Algorithmsmentioning

confidence: 99%

A Review on Task Scheduling Techniques in Cloud and Fog Computing: Taxonomy, Tools, Open Issues, Challenges, and Future Directions

Khan,

Aziz,

Osman

et al. 2023

IEEE Access

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Efficient task scheduling on the cloud is critical for optimal utilization of resources in data centers. It became even more challenging with the emergence of 5G and IoT applications that generate massive number of tasks with stringent latency requirements. This gives birth to fog/edge computing -a complementary layer to cloud. Tasks latency in fog computing can be reduced as processing in the network is done closer to the end devices and users, but every task cannot be scheduled in the fog due to limited resources availability. Conventional scheduling algorithms often fail to exploit the heterogeneous resources; therefore, specially designed and well-tuned scheduling algorithms are desired for achieving better quality of service. In this study, the state-of-the-art task scheduling algorithms in the cloud and fog environments are investigated in a diverse set of dimensions. Among the relevant studies published between 2018-2022 and indexed in the Web-of-Science (WOS), SCOPUS, and Google Scholar databases, eighteen studies are selected for both the cloud and fog domains from WOS and Scopus, while seventeen studies are chosen for both the cloud and fog domains from Google Scholar. Thus, a total of 106 studies are included in this survey for the detail investigation. The scheduling algorithms are broadly classified into three categories such as heuristic, meta-heuristic, and hybrid meta-heuristic followed by detailed critical analysis. It has been observed that most of the scheduling algorithms are dynamic and non-preemptive in nature, while the higher fraction of the tasks is independent in comparison to bag of tasks and workflows. Similarly, 97% of the studies focus on multiple objectives and 68% of the techniques are non-deterministic. Further, a total of twenty different scheduling objectives are identified with makespan, resource utilization, delay, load balancing, and energy consumption as the most significant metrics. The evaluation methods including simulations (51%), real experiments (4%), analytical equations (2%), and datasets (43%) etc. are surveyed. At the end, the open issues, challenges, and future directions are argued. Cloud computing, fog computing, task scheduling, workflow, Internet of Things (IoT). INDEX TERMS I. INTRODUCTION Cloud computing offers different kinds of scalable resources to end users based on pay-per-use basis by providing costThe associate editor coordinating the review of this manuscript and approving it for publication was Zhiwu Li . savings, security, flexibility, and automatic software updates etc. The users can concentrate on their personal and business objectives without bothering about the infrastructure and supporting services as the vendor is responsible for managing it. There are three service models and four deployment models provided by the cloud providers [1]. Though it is

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Optimized task scheduling and preemption for distributed resource management in fog-assisted IoT environment

Cited by 21 publications

References 39 publications

An Optimal Scheduling Method in IoT-Fog-Cloud Network Using Combination of Aquila Optimizer and African Vultures Optimization

An Optimal Scheduling Method in IoT-Fog-Cloud Network Using Combination of Aquila Optimizer and African Vultures Optimization

Task Scheduling Strategy of Logistics Cloud Robot Based on edge computing

A Review on Task Scheduling Techniques in Cloud and Fog Computing: Taxonomy, Tools, Open Issues, Challenges, and Future Directions

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