Energy- and performance-aware load-balancing in vehicular fog computing

Hameed, Ahmad Raza; Islam, Saif ul; Ahmad, Ishfaq; Munir, Kashif

doi:10.1016/j.suscom.2020.100454

Cited by 37 publications

(11 citation statements)

References 34 publications

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“…For the efficient processing of IoT jobs, Hameed et al [22] offer a capacity-based load-balancing strategy for vehicular fog distributed computing supported by a cluster. By taking into account vehicle position, speed, and direction when forming clusters that serve as a pool of computing resources, the authors suggest a dynamic clustering approach.…”

Section: Related Workmentioning

confidence: 99%

A Fog Computing Model for VANET to Reduce Latency and Delay Using 5G Network in Smart City Transportation

et al. 2022

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Connected vehicles are a vital part of smart cities, which connect over a wireless connection and bring mobile computation and communication abilities. As a mediator, fog computing resides between vehicles and the cloud and provides vehicles with processing, storage, and networking power through Vehicular Ad-hoc networks (VANET). VANET is a time-sensitive technology that requires less time to process a request received from a vehicle. Delay and latency are the notorious issues of VANET and fog computing. To deal with such problems, in this work, we developed a priority-based fog computing model for smart urban vehicle transportation that reduces the delay and latency of fog computing. To upgrade the fog computing infrastructure to meet the latency and Quality of Service (QoS) requirements, 5G localized Multi-Access Edge Computing (MEC) servers have also been used, which resulted tremendously in reducing the delay and the latency. We decreased the data latency by 20% compared to the experiment carried using only cloud computing architecture. We also reduced the processing delay by 35% compared with the utilization of cloud computing architecture.

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Section: Related Workmentioning

confidence: 99%

A Fog Computing Model for VANET to Reduce Latency and Delay Using 5G Network in Smart City Transportation

et al. 2022

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“…Hameed et al 65 presented an LB system for vehicular fog networks that is based on capacity‐based load distribution. The authors used a dynamic clustering strategy to build clusters of vehicles, considering position, speed, and direction.…”

Section: Dynamic Lbmentioning

confidence: 99%

“…According to the U.S energy consumption report, in 2006 the 6000 data centers were 61 × 109 kWh 32 . Based on 2010 efficiency standards, data center energy usage is expected to exceed 10,300 TWh/year in 2030 65 . The rate of energy consumption rises as the number of connected devices increases.…”

Section: Challenges and Open Research Issues In Fog Computingmentioning

confidence: 99%

Load balancing techniques for fog computing environment: Comparison, taxonomy, open issues, and challenges

Kashyap

Kumar

2022

Concurrency and Computation

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SUMMARY Load balancing (LB) is nothing but the systematic distribution of load over different servers. The fog server is handling the maximum data of the cloud server to enhance the advancement of users' requests. The growth in data requests is escalating, and fog computing has intensified the accessibility of the data. Fog computing achieves many challenges according to the demands of the users, but even so, some challenges require more progress. The problem faced by fog computing is LB due to an increase in traffic on the network layer. Various LB techniques have already been proposed in the cloud layer, but until now they have only been in progress in the fog layer. Inefficient LB may cause a decrease in service quality, like delays in response time, processing time, security, and many more. In this survey, several algorithms have been discussed that are based on LB, which works out the issue of overloaded data on the network. Some parameters that authors have focused in LB are latency, bandwidth, deadlines, cost, security, execution time, and response time. Other parameters based on fault tolerance are also discussed with their quality parameter table and algorithm. In addition to this some of the limitations of the author's work, that is discussed in this article.

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“…To avoid this situation, load-balancing methods are suggested to distribute loads over the nodes. Load-balancing in FNs refers to the even distribution of input tasks across a group of processing FNs so that the capacity of FNs is fairly utilized and task processing speed is increased [4][5][6][7][8]. FNs can allocate their tasks to underloaded neighbor nodes or the cloud through the load-balancing approach and reduce overload and processing delay as much as possible.…”

Section: Introductionmentioning

confidence: 99%

A Blind Load-Balancing Algorithm (BLBA) for Distributing Tasks in Fog Nodes

Tahmasebi-Pouya

Sarram

Mostafavi

2022

Wireless Communications and Mobile Computing

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In the distributed infrastructure of fog computing, fog nodes (FNs) can process user requests locally. In order to reduce the delay and response time of a user’s requests, incoming requests must be evenly distributed among FNs. For this purpose, in this paper, we propose a blind load-balancing algorithm (BLBA) to improve the load distribution in the fog environment. In the proposed algorithm, the mobile device sends a task to a FN. Then, the FN decides to process that task using the Double- Q -learning algorithm. One of the critical advantages of BLBA is that decision-making on tasks is done without any knowledge of the state of neighbor nodes. The proposed system consists of four layers: (i) IoT layer, (ii) fog layer, (iii) proxy server layer, and (iv) cloud layer. The experimental results show that the proposed algorithm with proper distribution of tasks between nodes significantly reduces the delay and user response time compared to the existing methods.

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Energy- and performance-aware load-balancing in vehicular fog computing

Cited by 37 publications

References 34 publications

A Fog Computing Model for VANET to Reduce Latency and Delay Using 5G Network in Smart City Transportation

A Fog Computing Model for VANET to Reduce Latency and Delay Using 5G Network in Smart City Transportation

Load balancing techniques for fog computing environment: Comparison, taxonomy, open issues, and challenges

A Blind Load-Balancing Algorithm (BLBA) for Distributing Tasks in Fog Nodes

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