Heuristic Min-conflicts Optimizing Technique for Load Balancing on Fog Computing

Kamal, Muhammad Babar; Javaid, Nadeem; Naqvi, Syed Aon Ali; Butt, Hanan; Saif, Talha; Kamal, Muhammad Daud

doi:10.1007/978-3-319-98557-2_19

Cited by 20 publications

(8 citation statements)

References 23 publications

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“…Specifically, task properties, user mobility and network constraints have been jointly formulated as a CSP [258], in order to reduce the task execution delay in Mobile Edge Computing infrastructures. A CSP formulation has also been used in combination with Min-conflicts scheduling algorithm [259], for achieving the necessary load balancing of the Edge resources and minimizing energy consumption. A more demanding offloading use case is the distributed processing of data streams.…”

Section: Constraint Satisfaction Methodsmentioning

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: Constraint Satisfaction Methodsmentioning

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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“…The proposed approach provides better simulation results by allocating optimal resources to requests. However, the performance improvement is insignificant compared to the other techniques [26].…”

Section: Task Scheduling In the Cloud-fog Computing Environmentmentioning

confidence: 86%

“…In contrast, works proposed in [5,8,18,21] ignore the makespan and/or execution cost metrics. Moreover, the works proposed in [2,17,19,26,27] ignore the deadline constraints of many IoT applications. Finally, the performance of the proposed scheduling algorithm is evaluated against traditional heuristic and metaheuristic approaches, including FF, BF, the GA, and the BLA.…”

Section: Task Scheduling In the Cloud-fog Computing Environmentmentioning

confidence: 99%

Real-Time Task Scheduling Algorithm for IoT-Based Applications in the Cloud–Fog Environment

2022

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IoT applications have become a pillar for enhancing the quality of life. However, the increasing amount of data generated by IoT devices places pressure on the resources of traditional cloud data centers. This prevents cloud data centers from fulfilling the requirements of IoT applications, particularly delay-sensitive applications. Fog computing is a relatively recent computing paradigm that extends cloud resources to the edge of the network. However, task scheduling in this computing paradigm is still a challenge. In this study, a semidynamic real-time task scheduling algorithm is proposed for bag-of-tasks applications in the cloud–fog environment. The proposed scheduling algorithm formulates task scheduling as a permutation-based optimization problem. A modified version of the genetic algorithm is used to provide different permutations for arrived tasks at each scheduling round. Then, the tasks are assigned, in the order defined by the best permutation, to a virtual machine, which has sufficient resources and achieves the minimum expected execution time. A conducted optimality study reveals that the proposed algorithm has a comparative performance with respect to the optimal solution. Additionally, the proposed algorithm is compared with first fit, best fit, the genetic algorithm, and the bees life algorithm in terms of makespan, total execution time, failure rate, average delay time, and elapsed run time. The experimental results show the superiority of the proposed algorithm over the other algorithms. Moreover, the proposed algorithm achieves a good balance between the makespan and the total execution cost and minimizes the task failure rate compared to the other algorithms. Graphical Abstract

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“…Wang et al [ 19 ] formulate the CSP of computation offloading by combining user mobility, task properties, and network constraints to reduce the task execution delay in the MEC infrastructure. Kamal et al [ 20 ] use the CSP formulation in conjunction with a minimal conflict scheduling algorithm to achieve a balanced load on MEC server resources with minimal energy consumption.…”

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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Heuristic Min-conflicts Optimizing Technique for Load Balancing on Fog Computing

Cited by 20 publications

References 23 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

Real-Time Task Scheduling Algorithm for IoT-Based Applications in the Cloud–Fog Environment

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

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