Task Classification and Scheduling Based on K-Means Clustering for Edge Computing

Ullah, Ihsan; Youn, Hee Yong

doi:10.1007/s11277-020-07343-w

Cited by 33 publications

(19 citation statements)

References 28 publications

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“…The dependencies between tasks can be represented by a graph and, by following a fuzzy clustering [236], makespan (i.e., the time difference between the start and finish of tasks), monetary and energy costs can be minimized. The K-means clustering method [237] can provide efficient task scheduling, thus increasing the utilization of the Edge devices, based on the type of resource requirements in terms of CPU, I/O and communication. Lastly, a policy-based clustering approach [238] can provide energy efficient task offloading solutions, by organizing the interactions among the Edge nodes.…”

Section: Machine Learningmentioning

confidence: 99%

Task offloading in Edge and Cloud Computing: A survey on mathematical, artificial intelligence and control theory solutions

et al. 2021

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Next generation communication networks are expected to accommodate a high number of new and resource-voracious applications that can be offered to a large range of end users. Even though end devices are becoming more powerful, the available local resources cannot cope with the requirements of these applications. This has created a new challenge called task offloading, where computation intensive tasks need to be offloaded to more resource powerful remote devices. Naturally, the Cloud Computing is a well-tested infrastructure that can facilitate the task offloading. However, Cloud Computing as a centralized and distant infrastructure creates significant communication delays that cannot satisfy the requirements of the emerging delay-sensitive applications. To this end, the concept of Edge Computing has been proposed, where the Cloud Computing capabilities are repositioned closer to the end devices at the edge of the network. This paper provides a detailed survey of how the Edge and/or Cloud can be combined together to facilitate the task offloading problem. Particular emphasis is given on the mathematical, artificial intelligence and control theory optimization approaches that can be used to satisfy the various objectives, constraints and dynamic conditions of this end-to-end application execution approach. The survey concludes with identifying open challenges and future directions of the problem at hand.

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Section: Machine Learningmentioning

confidence: 99%

Task offloading in Edge and Cloud Computing: A survey on mathematical, artificial intelligence and control theory solutions

et al. 2021

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“…Various authors published review papers [12][13][14][15][16] on task offloading in FC as a research direction. Many researchers use the queuing theory [17][18][19], game theory [20][21][22], dynamic programming and clustering techniques [18,23,24] to solve CO problems. It has been observed that each has separately investigated essential parameters, for example, delay, execution time, service time, communication cost, computational cost, quality of service and energy consumption etc.…”

Section: Literature Reviewmentioning

confidence: 99%

E-MOGWO Algorithm for Computation Offloading in Fog Computing

Yadav¹,

Suman²

2023

Intelligent Automation &Amp; Soft Computing

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Despite the advances mobile devices have endured, they still remain resource-restricted computing devices, so there is a need for a technology that supports these devices. An emerging technology that supports such resource-constrained devices is called fog computing. End devices can offload the task to close-by fog nodes to improve the quality of service and experience. Since computation offloading is a multiobjective problem, we need to consider many factors before taking offloading decisions, such as task length, remaining battery power, latency, communication cost, etc. This study uses the multiobjective grey wolf optimization (MOGWO) technique for optimizing offloading decisions. This is the first time MOGWO has been applied for computation offloading in fog computing. A gravity reference point method is also integrated with MOGWO to propose an enhanced multiobjective grey wolf optimization (E-MOGWO) algorithm. It finds the optimal offloading target by taking into account two parameters, i.e., energy consumption and computational time in a heterogeneous, scalable, multifog, multi-user environment. The proposed E-MOGWO is compared with MOG-WO, non-dominated sorting genetic algorithm (NSGA-II) and accelerated particle swarm optimization (APSO). The results showed that the proposed algorithm achieved better results than existing approaches regarding energy consumption, computational time and the number of tasks successfully executed.

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“…Bashir et al [ 22 ] use logistic regression to calculate the load of each edge node and propose a dynamic resource allocation policy. Ullah et al [ 23 ] use K-means clustering approach to provide efficient task scheduling based on resource requirements in terms of CPU, I/O, and communication, thus improving the utilization of edge devices. To determine the combination of different devices and dynamic tasks, Rani et al [ 24 ] propose a deep learning model to address the speed, power, and security challenges, while meeting the QoS.…”

Section: Related Workmentioning

confidence: 99%

A Federated Learning and Deep Reinforcement Learning-Based Method with Two Types of Agents for Computation Offload

Liu

Yang

Zhang

et al. 2023

Sensors

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With the rise of latency-sensitive and computationally intensive applications in mobile edge computing (MEC) environments, the computation offloading strategy has been widely studied to meet the low-latency demands of these applications. However, the uncertainty of various tasks and the time-varying conditions of wireless networks make it difficult for mobile devices to make efficient decisions. The existing methods also face the problems of long-delay decisions and user data privacy disclosures. In this paper, we present the FDRT, a federated learning and deep reinforcement learning-based method with two types of agents for computation offload, to minimize the system latency. FDRT uses a multi-agent collaborative computation offloading strategy, namely, DRT. DRT divides the offloading decision into whether to compute tasks locally and whether to offload tasks to MEC servers. The designed DDQN agent considers the task information, its own resources, and the network status conditions of mobile devices, and the designed D3QN agent considers these conditions of all MEC servers in the collaborative cloud-side end MEC system; both jointly learn the optimal decision. FDRT also applies federated learning to reduce communication overhead and optimize the model training of DRT by designing a new parameter aggregation method, while protecting user data privacy. The simulation results showed that DRT effectively reduced the average task execution delay by up to 50% compared with several baselines and state-of-the-art offloading strategies. FRDT also accelerates the convergence rate of multi-agent training and reduces the training time of DRT by 61.7%.

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Task Classification and Scheduling Based on K-Means Clustering for Edge Computing

Cited by 33 publications

References 28 publications

Task offloading in Edge and Cloud Computing: A survey on mathematical, artificial intelligence and control theory solutions

Task offloading in Edge and Cloud Computing: A survey on mathematical, artificial intelligence and control theory solutions

E-MOGWO Algorithm for Computation Offloading in Fog Computing

A Federated Learning and Deep Reinforcement Learning-Based Method with Two Types of Agents for Computation Offload

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