Joint optimization of wireless bandwidth and computing resource in cloudlet-based mobile cloud computing environment

Meng, Sachula; Wang, Ying; Zhang, Miao; Sun, Kai

doi:10.1007/s12083-017-0544-x

Cited by 27 publications

(12 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…First Stackelberg game was proposed to analyze the pricing problem and then a many-to-many matching algorithm is used for FN-DSS (data service subscribers) pricing problem. For the joint optimization problem of cloudlet selection and bandwidth allocation, an iterative algorithm was proposed by authors in [12] for a triple-stage Stackelberg game. To achieve maximum utility by minimizing latency during association authors have proposed the Boltzmann-Gibbs learning algorithm [13].…”

Section: Literature Reviewmentioning

confidence: 99%

Cloudlet Selection in Cache-Enabled Fog Networks for Latency Sensitive IoT Applications

Basir¹,

Qaisar

Ali

et al. 2021

IEEE Access

View full text Add to dashboard Cite

Over the coming years, the foresighted enormous increase in smart devices supporting Internet-of-Things (IoT) applications demand novelty in network design. A promising solution to the everincreasing low-latency requirement of IoT applications is the development of fog network architecture. However, the presence of an enormous number of smart devices in fog networks affects the performance of the network. To harvest the benefits of fog networking necessitates finding optimal cloudlet selection strategies. This article formulates a mixed-integer non-linear programming (MINLP) problem that has the objective of latency minimization. An exhaustive search on our cache-enabled (CE) fog architecture cannot be applied because of the problem's combinatorial and NP-hard nature. Similarly, the genetic algorithm (GA) cannot be used to find the solution because of the calculation of the number of generations. The increase in the number of IoT and fog nodes increases the solution search space, hence an Outer Approximation Algorithm (OAA) is proposed to arrive at the solution. Low complexity, convergence, and effectiveness of the proposed algorithm ensures the -optimal solution = 10 −3 , obtained through standard problem solvers.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Cloudlet Selection in Cache-Enabled Fog Networks for Latency Sensitive IoT Applications

Basir¹,

Qaisar

Ali

et al. 2021

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Approaches based on game theory have also been used in the field of fog computing (or edge computing/femto-cloud) for resource optimization, 49,50 latency and/or the energy consumption reduction, 51,52 computation offloading, 53,54 and resource aggregation into a single distributed system. 55,56 In this work, we target a goal which is different from those addressed by the above papers.…”

Section: Related Workmentioning

confidence: 99%

Profit‐aware coalition formation in fog computing providers: A game‐theoretic approach

Anglano

Canonico

Castagno

et al. 2019

Concurrency and Computation

View full text Add to dashboard Cite

We consider fog computing scenarios where data generated by a set of IoT applications need to be processed locally by a set of fog nodes, belonging to distinct Fog Infrastructure Providers (FIPs) sharing the same co-location facility, with the aim of increasing their profits. This is a challenging goal as it requires reducing costs and meeting QoS targets despite time-varying workloads. We argue that these FIPs may find it profitable to cooperate by mutually sharing their workload and resources, and we show (by using a game-theoretical framework) that this is indeed the case when stable coalitions can be formed. Based on these results, in this paper, we present (1) a mathematical model for maximizing the profit obtained for allocating IoT applications to a group of FIPs, and (2) a coalition formation algorithm that allows each FIP to decide with whom to cooperate so as to increment its profits. The efficacy of the devised algorithm is assessed by means of an experimental evaluation taking into account different workload intensities. The results from these experiments show the capability of the proposed algorithm to form coalitions of FIPs that are profitable and stable in all the scenarios we take into consideration.

show abstract

“…Game theory is emerged as a pivotal tool for the resource allocation problem [19][20][21][22] in MCC networks. The interaction between remote cloud, cloudlet and mobile subscriber was modelled in [23][24][25][26].…”

Section: Application Of Game Theory In MCC Networkmentioning

confidence: 99%

Hierarchical evolutionary game based dynamic cloudlet selection and bandwidth allocation for mobile cloud computing environment

et al. 2019

Self Cite

View full text Add to dashboard Cite

Joint optimization of wireless bandwidth and computing resource in cloudlet-based mobile cloud computing environment

Cited by 27 publications

References 19 publications

Cloudlet Selection in Cache-Enabled Fog Networks for Latency Sensitive IoT Applications

Cloudlet Selection in Cache-Enabled Fog Networks for Latency Sensitive IoT Applications

Profit‐aware coalition formation in fog computing providers: A game‐theoretic approach

Hierarchical evolutionary game based dynamic cloudlet selection and bandwidth allocation for mobile cloud computing environment

Contact Info

Product

Resources

About