Efficient deep-reinforcement learning aware resource allocation in SDN-enabled fog paradigm

Lakhan, Abdullah; Mohammed, Mazin Abed; Obaid, Omar Ibrahim; Chakraborty, Chinmay; Abdulkareem, Karrar Hameed; Kadry, Seifedine

doi:10.1007/s10515-021-00318-6

Cited by 41 publications

(9 citation statements)

References 25 publications

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“…According to a previous study [107], a smart mobility fog agent (MFA) is included in the SDN controller. The authors also developed an adaptive policy for resource allocation that handles offloading tasks along with the user mobility information.…”

Section: ) Deep Q Network (Dqn)mentioning

confidence: 99%

Offloading Mechanisms Based on Reinforcement Learning and Deep Learning Algorithms in the Fog Computing Environment

Abdulazeez

Askar

2023

IEEE Access

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Fog computing has emerged as a computing paradigm for resource-restricted Internet of things (IoT) devices to support time-sensitive and computationally intensive applications. Offloading can be utilized to transfer resource-intensive tasks from resource-limited end devices to a resource-rich fog or cloud layer to reduce end-to-end latency and enhance the performance of the system. However, this advantage is still challenging to achieve in systems with a high request rate because it leads to long queues of tasks in fog nodes and reveals inefficiencies in terms of delays. In this regard, reinforcement learning (RL) is a well-known method for addressing such decision-making issues. However, in large-scale wireless networks, both action and state spaces are complex and extremely extensive. Consequently, reinforcement learning techniques may not be able to identify an efficient strategy within an acceptable time frame. Hence, deep reinforcement learning (DRL) was developed to integrate RL and deep learning (DL) to address this problem. This paper presents a systematic analysis of using RL or DRL algorithms to address offloadingrelated issues in fog computing. First, the taxonomy of fog computing offloading mechanisms based on RL and DRL algorithms was divided into three major categories: value-based, policy-based, and hybridbased algorithms. These categories were then compared based on important features, including offloading problem formulation, utilized techniques, performance metrics, evaluation tools, case studies, their strengths and drawbacks, offloading directions, offloading mode, SDN-based architecture, and offloading decisions. Finally, the future research directions and open issues are discussed thoroughly.INDEX TERMS Fog computing, Internet of Things (IoT), offloading, reinforcement learning, deep reinforcement learning.

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Section: ) Deep Q Network (Dqn)mentioning

confidence: 99%

Offloading Mechanisms Based on Reinforcement Learning and Deep Learning Algorithms in the Fog Computing Environment

Abdulazeez

Askar

2023

IEEE Access

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“…In [33], a novel communication model has been outlined to make a trade-off between cost, deadline, and relevant constraints for accomplishing highly reliable services with a low latency rate, and an energy usage optimization design has been described as an extension of this contribution in [34]. In [35], a novel resource allocation model has been formed according to a deep-learning Q-network to optimize the system latency and energy consumption to meet the deadline within the demanded resource capacity with a 30% reduced cost in terms of energy and execution time.…”

Section: Related Workmentioning

confidence: 99%

Optimizing Service Stipulation Uncertainty with Deep Reinforcement Learning for Internet Vehicle Systems

Nain¹,

Shahana²,

Chaudhry³

et al. 2023

Computers, Materials &Amp; Continua

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Fog computing brings computational services near the network edge to meet the latency constraints of cyber-physical System (CPS) applications. Edge devices enable limited computational capacity and energy availability that hamper end user performance. We designed a novel performance measurement index to gauge a device's resource capacity. This examination addresses the offloading mechanism issues, where the end user (EU) offloads a part of its workload to a nearby edge server (ES). Sometimes, the ES further offloads the workload to another ES or cloud server to achieve reliable performance because of limited resources (such as storage and computation). The manuscript aims to reduce the service offloading rate by selecting a potential device or server to accomplish a low average latency and service completion time to meet the deadline constraints of sub-divided services. In this regard, an adaptive online status predictive model design is significant for prognosticating the asset requirement of arrived services to make float decisions. Consequently, the development of a reinforcement learning-based flexible x-scheduling (RFXS) approach resolves the service offloading issues, where x = service/resource for producing the low latency and high performance of the network. Our approach to the theoretical bound and computational complexity is derived by formulating the system efficiency. A quadratic restraint mechanism is employed to formulate the service optimization issue according to a set of measurements, as well as the behavioural association rate and adulation factor. Our system managed an average 0.89% of the service offloading rate, with 39 ms of delay over complex scenarios (using three servers with a 50% service arrival rate). The simulation outcomes confirm that the proposed scheme attained a low offloading uncertainty, and is suitable for simulating heterogeneous CPS frameworks.

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“…They are mainly focusing on the overhead associated with heavyweight profiling and offloading. The expensive healthcare services are other issues mainly faced by hospitals, and to overcome this issue, different authors used blockchain-based techniques [ 39 , 42 ], fog networks [ 40 ], bio-inspired robotics [ 43 ], etc. A critical healthcare management module [ 41 ] was developed by integrating different models such as fog and edge computing to improve the healthcare application performance.…”

Section: Related Workmentioning

confidence: 99%

A Novel CNN-TLSTM Approach for Dengue Disease Identification and Prevention using IoT-Fog Cloud Architecture

Manoharan

Kumar²,

Vadivelan³

2022

Neural Process Lett

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One of the mosquito-borne pandemic viral infections is Dengue which is mostly transmitted to humans by the Aedes agypti or female Aedes albopictis mosquitoes. The dengue disease expansion is mainly due to the different factors such as climate change, socioeconomic factors, viral evolution, globalization, etc. The unavailability of certain antiviral therapy and specific vaccine increases the risk of the dengue disease spreading even further. This arises the need for a novel technique that overcomes the complexities associated with dengue disease prediction such as low reporting level, misclassification, and incompatible disease monitoring framework. This paper mainly overcomes the above-mentioned problems by integrating the Internet of Things (IoT), fog-cloud, and deep learning techniques for efficient dengue monitoring. A compatible disease monitoring framework is formed via the IoT devices and the reports are effectively created and transferred to the healthcare facilities via the fog-cloud model. The misdiagnosis error is overcome in this paper using the novel Hybrid Convolutional Neural Network (CNN) with Tan h Long and Short Term Memory (TLSTM) based Adaptive Teaching Learning Based Optimization (ATLBO) algorithm. The ATLBO optimized CNN-TLSTM architecture mainly analyzes the dengue-related parameters such as Soft Bleeding, Muscle Pain, Joint Pain, Skin rash, Fever, Water Site, Carbon Dioxide, Water Site Humidity, Water Site Temperature, etc. for an efficient clinical decision making and timely disease diagnosis. The experimental results are conducted using a real-time dataset and its performance is validated using various performance metrics. When compared in terms of different statistical parameters such as accuracy, f-measure, mean square error, and reliability, the proposed method offers superior results in the case of dengue disease detection than other existing methods. The ATLBO optimized hybrid CNN-TLSTM shows an accuracy of 96.9%, a precision of 95.7%, recall of 96.8%, and F-measure of 96.2% which is relatively high when compared to the existing techniques. The proposed model is capable of identifying the patients in a certain geographical region and preventing the disease emergency via immediate disease diagnosis and alerting the healthcare officials to offer the stipulated services.

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Efficient deep-reinforcement learning aware resource allocation in SDN-enabled fog paradigm

Cited by 41 publications

References 25 publications

Offloading Mechanisms Based on Reinforcement Learning and Deep Learning Algorithms in the Fog Computing Environment

Offloading Mechanisms Based on Reinforcement Learning and Deep Learning Algorithms in the Fog Computing Environment

Optimizing Service Stipulation Uncertainty with Deep Reinforcement Learning for Internet Vehicle Systems

A Novel CNN-TLSTM Approach for Dengue Disease Identification and Prevention using IoT-Fog Cloud Architecture

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