An Incentive-Compatible Offloading Mechanism in Fog-Cloud Environments Using Second-Price Sealed-Bid Auction

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

doi:10.1007/s10723-021-09576-w

Cited by 19 publications

(26 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We model mobile user trafc in this study as an M/M/1/K queue. As mentioned in [1], this model can capture the behavior of mobile users better than M/M/1, which was previously used by researchers [45]. Also, we model each fog node as an M/M/c/K queue [1,46].…”

Section: System Modelmentioning

confidence: 99%

“…Recently, a new paradigm called fog computing has emerged, also known as "cloud at the edge" [1]. Te use of this technology in areas such as e-health [2,3], industry [4,5], vehicular trafc management [6][7][8], and agriculture [9] is expanding rapidly.…”

Section: Introductionmentioning

confidence: 99%

“…So far, extensive research has been conducted on ofloading optimization in fog computing. Some of the essential methods used in the literature are game theory [10], auction theory [1,11,12], probabilistic modeling [13], heuristic [14], and metaheuristic [15]. Recently, the use of machine learning methods, especially reinforcement learning (RL) [16] to optimize ofoading has received much attention from the research community.…”

Section: Introductionmentioning

confidence: 99%

“…In this function, each joule of energy consumption has a predetermined price. Tis paper complements one of the state-of-the-art research [1], in which each agent uses a second-price sealed-bid (SPSB) auction mechanism for ofoading. In our modeling in this paper, the user who wins the auction has a diferent state than the others.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

2023

Self Cite

View full text Add to dashboard Cite

In the fog computing paradigm, if the computing resources of an end device are insufficient, the user’s tasks can be offloaded to nearby devices or the central cloud. In addition, due to the limited energy of mobile devices, optimal offloading is crucial. The method presented in this paper is based on the auction theory, which has been used in recent studies to optimize computation offloading. We propose a bid prediction mechanism using Q-learning. Nodes participating in the auction announce a bid value to the auctioneer entity, and the node with the highest bid value is the auction winner. Then, only the winning node has the right to offload the tasks on its upstream (parent) node. The main idea behind Q-learning is that it is stateless and only considers the current state to perform an action. The evaluation results show that the bid values predicted by the Q-learning method are near-optimal. On average, the proposed method consumes less energy than traditional and state-of-the-art techniques. Also, it reduces the execution time of tasks and leads to less consumption of network resources.

show abstract

Section: System Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

2023

Self Cite

View full text Add to dashboard Cite

show abstract

“…As is common in evolutionary computing, mutation are used to maintain diversity from one generation to the next [40]. If the algorithm gets stuck in a local optimum, then the variance of the population fitness will become zero.…”

Section:  Adaptive Mutation Strategymentioning

confidence: 99%

Energy-aware and carbon-efficient VM placement optimization in cloud datacenters using evolutionary computing methods

Abbasi-khazaei

Rezvani

2022

Soft Comput

Self Cite

View full text Add to dashboard Cite

One of the most important concerns of cloud service providers is balancing renewable and fossil energy consumption. On the other hand, the policy of organizations and governments is to reduce energy consumption and greenhouse gas emissions in cloud data centers. Recently, a lot of research has been conducted to optimize the Virtual Machine (VM) placement on physical machines to minimize energy consumption. Many previous studies have not considered the deadline and scheduling of IoT tasks. Therefore, the previous modelings are mainly not well-suited to the IoT environments where requests are time-constraint. Unfortunately, both the subproblems of energy consumption minimization and scheduling fall into the category of NP-hard issues. In this study, we propose a multi-objective VM placement to joint minimizing energy costs and scheduling. After presenting a modified memetic algorithm, we compare its performance with baseline methods as well as stateof-the-art ones. The simulation results on the CloudSim platform show that the proposed method can reduce energy costs, carbon footprints, SLA violations, and the total response time of IoT requests.

show abstract

An Auction-Based Mechanism for Task Offloading in a Secure Fog-Cloud Environment

Mikavica

Kostic-Ljubisavljevic²

2022

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

View full text Add to dashboard Cite

An Incentive-Compatible Offloading Mechanism in Fog-Cloud Environments Using Second-Price Sealed-Bid Auction

Cited by 19 publications

References 42 publications

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

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

Energy-aware and carbon-efficient VM placement optimization in cloud datacenters using evolutionary computing methods

An Auction-Based Mechanism for Task Offloading in a Secure Fog-Cloud Environment

Contact Info

Product

Resources

About