An Auction-Based Bid Prediction Mechanism for Fog-Cloud Offloading Using Q-Learning

Besharati, Reza; Rezvani, Mohammad Hossein; Sadeghi, Mohammad Mehdi Gilanian

doi:10.1155/2023/5222504

Cited by 12 publications

(3 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [ 49 ], the authors proposed a bid prediction mechanism to optimize computation offloading using auction theory. The mechanism was based on Q-learning, where nodes bid to offload tasks on their upstream node, and the winning node offloads the task.…”

Section: Related Workmentioning

confidence: 99%

Delay reduction in MTC using SDN based offloading in Fog computing

2023

View full text Add to dashboard Cite

Fog computing (FC) brings a Cloud close to users and improves the quality of service and delay services. In this article, the convergence of FC and Software-Defined-Networking (SDN) has been proposed to implement complicated mechanisms of resource management. SDN has suited the practical standard for FC systems. The priority and differential flow space allocation have been applied to arrange this framework for the heterogeneous request in Machine-Type-Communications. The delay-sensitive flows are assigned to a configuration of priority queues on each Fog. Due to limited resources in the Fog, a promising solution is offloading flows to other Fogs through a decision-based SDN controller. The flow-based Fog nodes have been modeled according to the queueing theory, where polling priority algorithms have been applied to service the flows and to reduce the starvation problem in a multi-queueing model. It is observed that the percentage of delay-sensitive processed flows, the network consumption, and the average service time in the proposed mechanism are improved by about 80%, 65%, and 60%, respectively, compared to traditional Cloud computing. Therefore, the delay reductions based on the types of flows and task offloading is proposed.

show abstract

Section: Related Workmentioning

confidence: 99%

Delay reduction in MTC using SDN based offloading in Fog computing

2023

View full text Add to dashboard Cite

show abstract

“…The fundamental concept is that the current state only depends on the previous state and is independent of earlier states. Markov chains are extensively applied in common domains such as automatic control and optimization, prediction, information technology, and cloud/fog computing [29], [30]. In this study, the behavioral states of the PV output and load are also considered as a type of stochastic process, exhibiting a certain degree of temporal correlation.…”

Section: A Markov Chain Principle For State Quantity Of Distribution ...mentioning

confidence: 99%

Collaborative Optimization Allocation of Grid-Forming and Grid-Following Reactive Power Resources Considering Auxiliary Equipment Services

Xue,

Zeng,

Jia

et al. 2023

IEEE Access

View full text Add to dashboard Cite

The large-scale integration of high-penetration distributed photovoltaic systems into distribution networks can result in significant grid voltage fluctuations within a short period. However, centralized regulation instructions for passive/reactive compensation, by themselves, are insufficient for effectively suppressing these fluctuations. Thus, this study used the grid-forming and grid-following control characteristics of modern power electronic inverters to propose an optimal allocation strategy for reactive power compensation equipment. This strategy aimed to address the insufficient proactive support capacity in the reactive power equipment used to suppress short-time grid voltage fluctuations. After establishing uncertain operation scenarios for the distribution network, we analyzed the respective multi-timescale behavioral characteristics of traditional, grid-forming, and grid-following reactive power compensation devices. The primary and auxiliary objectives were to minimize the investment cost of the special equipment and voltage deviation of the entire network, respectively. To achieve these objectives, we established a collaborative optimal allocation model for grid-forming and grid-following reactive power equipment. A multi-timescale cooperative allocation strategy was proposed to decompose the total reactive power demand curves at the equipment installation nodes into reactive power curves for different response levels and then collaboratively allocate the multiple devices. A comparative analysis of the three schemes in IEEE 33-node and 69-node systems shows that the proposed strategy guarantees lower overall network voltages while reducing the cost by at least 20% compared to those of other schemes.INDEX TERMS Collaborative optimization allocation, photovoltaic inverter, multi-time scale, gridforming control, optimal reactive power

show abstract

“…Other proposals focused on computation offloading optimization and energy conservation. In [49], a bid prediction mechanism based on Q-learning was introduced to optimize computation offloading, reducing energy consumption, execution time, and saving network resources. In [50], an incentive-compatible offloading scheme was suggested to minimize latency and energy consumption for user tasks in the Fog computing environment and Cloud layer, using an auction algorithm.…”

Section: Related Workmentioning

confidence: 99%

Delay reduction in MTC via SDN based offloading in a Fog Computing environment with the assistance of volunteer nodes

Arefian,

Khayyambashi,

Movahhedinia

2023

Preprint

View full text Add to dashboard Cite

This article discusses the integration of Distributed Fog Computing (FC) and Software-Defined Networking (SDN) for efficient resource management in Machine Type Communications (MTC). FC brings Cloud resources closer to the user, enhancing service quality and reducing delays. Some MTC devices have powerful processors that can be used as volunteer nodes to process lightweight requests, thereby increasing the network's distributed processing capabilities. The paper proposes a framework that uses priority and differential flow space allocation to handle heterogeneous requests in MTC and assign delay-sensitive flows to priority queues on each Fog node. To address the limited resources available on individual Fog nodes, the article recommends offloading flows to other Fog nodes and volunteer nodes through a decision-based SDN controller. The article models flow-based Fog nodes using queueing theory, employing priority polling algorithms to service the flows and alleviate the issue of resource starvation in a multi-queueing environment. It is observed that the percentage of delay-sensitive processed flows, the network consumption, and the average service time in the proposed mechanism are improved by about85%, 68%, and 62%, respectively, compared to traditional Cloud computing. Therefore, the delay reductions based on the types of flows and task offloading is proposed.

show abstract

An Auction-Based Bid Prediction Mechanism for Fog-Cloud Offloading Using Q-Learning

Cited by 12 publications

References 46 publications

Delay reduction in MTC using SDN based offloading in Fog computing

Delay reduction in MTC using SDN based offloading in Fog computing

Collaborative Optimization Allocation of Grid-Forming and Grid-Following Reactive Power Resources Considering Auxiliary Equipment Services

Delay reduction in MTC via SDN based offloading in a Fog Computing environment with the assistance of volunteer nodes

Contact Info

Product

Resources

About