Deadline-based dynamic resource allocation and provisioning algorithms in Fog-Cloud environment

Naha, Ranesh Kumar; Garg, Saurabh; Chan, Andrew; Battula, Sudheer Kumar

doi:10.1016/j.future.2019.10.018

Cited by 139 publications

(86 citation statements)

References 13 publications

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“…Moreover, data generated from these densely deployed, resource-starved devices contain similar and redundant patterns. Transmitting such correlated data across the network results in high energy consumption, lower QoS and lower throughput [ 108 , 109 ]. The resource limitation issue is resolved upto some extent by integrating the IoT with the cloud computing paradigm.…”

Section: Challenges and Open Research Issuesmentioning

confidence: 99%

A Comprehensive Survey on Machine Learning-Based Big Data Analytics for IoT-Enabled Smart Healthcare System

et al. 2021

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Section: Challenges and Open Research Issuesmentioning

confidence: 99%

A Comprehensive Survey on Machine Learning-Based Big Data Analytics for IoT-Enabled Smart Healthcare System

et al. 2021

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“…The method was proposed in [21] generated considering both cost and performance factors, an effective resource allocation scheme can be generated. A resource allocation and provisioning algorithm was proposed in [22] by using resource ranking and provision of resources in a hybrid and hierarchical fashion. Reference [23] proposed a Support and Confidence based (SCB) technique which optimises the resource usage in the resource monitoring service.…”

Section: Related Workmentioning

confidence: 99%

Distributed Edge Computing Offloading Algorithm Based on Deep Reinforcement Learning

Wang

et al. 2020

IEEE Access

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As a mode of processing task request, edge computing paradigm can reduce task delay and effectively alleviate network congestion caused by the proliferation of Internet of things(IoT) devices compared with cloud computing. However, in the actual construction of the network, there are various edge autonomous subnets in the adjacent areas, which leads to the possibility of unbalance of server load among autonomous subnets during the peak period of task request. In this paper, a deep reinforcement learning algorithm is proposed to solve the complex computation offloading problem for the heterogeneous Edge Computing Server(ECS) collaborative computing. The problem is solved based on the real-time state of the network and the attributes of the task, which adopts Actor Critic and Policy Gradient's Deep Deterministic Policy Gradient(DDPG) to make optimized decisions of computation offloading. Considering multi-task, the heterogeneity of edge subnet and mobility of edge devices, the proposed algorithm can learn the network environment and generate the computation offloading decision to minimize the task delay.The simulation results show that the proposed DDPG-based algorithm is competitive compared with the Deep Q Network(DQN) algorithm and Asynchronous Advantage Actor-Critic(A3C) algorithm. Moreover, the optimal solutions are leveraged to analyze the influence of edge network parameters on task delay. INDEX TERMS Edge computing, computation offload, collaborative computing, reinforcement learning, DDPG.

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“…Similarly, as in the previously reviewed papers, in [24] authors pay special attention to delay-sensitive IoT applications. Interestingly, in addition to time-sensitive applications, authors in [25] also consider a more stringent scenario where user (application) requirements can change dynamically and resource allocation needs to consider additional constraints, such as a deadline. Moreover, a different approach for application placement in fog-cloud can be found in [26].…”

Section: B Related Workmentioning

confidence: 99%

“…When the backup flag, ρ, is activated (lines [22][23][24][25][26][27], the heuristic searches for a backup leader in each cluster (which must have cloud connectivity and be able to serve the rest of cluster nodes). The solution is discarded if no backup leader is found for each cluster.…”

Section: Influence Of Fog and Cloud Costsmentioning

confidence: 99%

Designing an efficient clustering strategy for combined Fog-to-Cloud scenarios

Asensio

Masip-Bruin

Durán

et al. 2020

Future Generation Computer Systems

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The evolution of the Internet of Things (IoT) is imposing many distinct challenges, particularly to guarantee both wide and global systems communication, and to ensure an optimal execution of services. To that end, IoT services must make the most out of both cloud and fog computing (turning into combined fog-cloud scenarios), which indeed requires novel and efficient resource management procedures to handle such diversity of resources in a coordinated way. Most of the related works that can be found in the literature focus on resource mapping for service-specific execution in fog-cloud; however, those works assume a control and management architecture already deployed. Interestingly, few works propose algorithms to set that control architecture, necessary to execute the services and effectively implement services and resource mapping. This paper addresses that challenge by solving the problem of optimal clustering of devices located at the edge of the network offering their resources to support fog computing while defining the control and management role of each of them in the architecture, in order to ensure access to management functions in combined fog-cloud scenarios. In particular, we set out the Fog-Cloud Clustering (FCC) problem as an optimization problem, which is based on multi-objective optimization, including realistic, novel and stringent constraints; e.g., to improve the architecture's robustness by means of a device acting as a backup in the cluster. We model the FCC problem as a Mixed Integer Linear Programming (MILP) formulation, for which a lower and an upper bound on the number of required clusters is derived. We also propose a machine learning-based heuristic that provides scalable and near-optimal solutions in realistic scenarios in which, due to the high number of connected devices, solving the MILP formulation is not viable. By means of a simulation study, we demonstrate the effectiveness of the algorithms comparing its results with those of MILP formulation.

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Deadline-based dynamic resource allocation and provisioning algorithms in Fog-Cloud environment

Cited by 139 publications

References 13 publications

A Comprehensive Survey on Machine Learning-Based Big Data Analytics for IoT-Enabled Smart Healthcare System

A Comprehensive Survey on Machine Learning-Based Big Data Analytics for IoT-Enabled Smart Healthcare System

Distributed Edge Computing Offloading Algorithm Based on Deep Reinforcement Learning

Designing an efficient clustering strategy for combined Fog-to-Cloud scenarios

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