Reliable resources brokering scheme in wireless grids based on non-cooperative bargaining game

Birje, Mahantesh N.; Manvi, Sunilkumar S.; Das, Sajal K.

doi:10.1016/j.jnca.2013.07.007

Cited by 27 publications

(20 citation statements)

References 38 publications

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“…Game theory is "the study of mathematical models of conflict and cooperation between intelligent rational decision-makers" [Myerson 1991]. Originally developed to model problems in the field of economics, game theory has recently been applied to a variety of problems, in most cases, to solve the resource allocation problems in competitive environments [Hassan et al 2012], [Birje et al 2014], including the design of incentive mechanisms for participatory sensing systems. This is because the entities involved (i.e., the participating users) are autonomous agents, making decisions only for their own interests.…”

Section: Preliminaries On Game Theorymentioning

confidence: 99%

Incentive Mechanisms for Participatory Sensing

Restuccia

Das

Payton

2016

ACM Trans. Sen. Netw.

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150

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Participatory sensing is a powerful paradigm which takes advantage of smartphones to collect and analyze data beyond the scale of what was previously possible. Given that participatory sensing systems rely completely on the users' willingness to submit up-to-date and accurate information, it is paramount to effectively incentivize users' active and reliable participation. In this paper, we survey existing literature on incentive mechanisms for participatory sensing systems. In particular, we present a taxonomy of existing incentive mechanisms for participatory sensing systems, which are subsequently discussed in depth by comparing and contrasting different approaches. Finally, we discuss an agenda of open research challenges in incentivizing users in participatory sensing.

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Section: Preliminaries On Game Theorymentioning

confidence: 99%

Incentive Mechanisms for Participatory Sensing

Restuccia

Das

Payton

2016

ACM Trans. Sen. Netw.

Self Cite

150

View full text Add to dashboard Cite

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“…Works (Soni and Kalra 2014;Xu et al 2016;Li and Zhang 2016;Abazari et al 2018;Juarez et al 2018;Shojafar et al 2016) focus on makespan reduction using different methods. Papers (Birje et al 2012;Buyya et al 2008;Achar and Thilagam 2014;Goudar et al 2017;Birje et al 2014) discuss the different strategies based on game theory for developing a pricing model.…”

Section: Related Workmentioning

confidence: 99%

Utility based load balancing using firefly algorithm in cloud

Tapale

Goudar

Birje

et al. 2020

J. of Data, Inf. and Manag.

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Scheduling and load balancing are the major challenges faced in cloud scenario due to distributed computing and heterogeneous nature of the infrastructure. Scheduling of tasks to the appropriate virtual machines (VMs) can be done using different mechanisms, but balancing the load is the major problem that occurs due to fluctuation of load, and different VM specifications. This leads to imbalanced resource utilization and performance degradation of the system. To address this issue, the paper proposes a scheme that tries to maximize resource utilization while optimizing the utility (profit) using bargaining protocol, and also balances the load across cloud system by distributing jobs to reliable VMs using firefly algorithm for better performance. The proposed scheme is simulated using CloudSim tool and the results are compared with existing work. It is observed that the proposed scheme performs better than the existing work.

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“…Offering local resources behind a proxy to a Grid infrastructure is a strategy commonly adopted by traditional Grid platforms like Ibis-Satin [24], JCluster [27] and GridGain 1 . Due to its simplicity, proxy-based networks have been the starting point for defining different local scheduling policies in mobile Grids [7], [2], [5], [18]. Figure 1 depicts a proxy-based mobile Grid which is the targeted topology in this work.…”

Section: Introductionmentioning

confidence: 99%

“…Despite the incipient nature of the area, several works address job scheduling using mobile devices as resource providers [14], [5], [2], [16], [25]. They assume complete knowledge, or accurate estimations, about all jobs requirements from the software to run and resources characteristics.…”

Section: Introductionmentioning

confidence: 99%

“…Then, it is common that these works propose different optimal or near-optimal schedulers with constraints that are associated to the mobile device special features and/or topologies introduced above. For instance, [5], [2], [16], [25] treat the limited energy of mobile devices as a formal resource constraint. Moreover, [14] and [16] focus on optimizing energy utilization in ad-hoc multi-hop networks.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A performance comparison of data-aware heuristics for scheduling jobs in mobile grids

Hirsch

Mateos

Rodríguez

et al. 2017

2017 XLIII Latin American Computer Conference (CLEI)

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Given mobile devices ubiquity and capabilities, some researchers now consider them as resource providers of distributed environments called mobile Grids for running resource intensive software. Therefore, job scheduling has to deal with device singularities, such as energy constraints, mobility and unstable connectivity. Many existing schedulers consider at least one of these aspects, but their applicability strongly depends on information that is unavailable or difficult to estimate accurately, like job execution time. Other efforts do not assume knowing job CPU requirements but ignore energy consumption due to data transfer operations, which is not realistic for data-intensive applications. This work, on the contrary, considers the last as non negligible and known by the scheduler. Under these assumptions, we conduct a performance study of several traditional scheduling heuristics adapted to this environment, which are applied with the known information of jobs but evaluated along with job information unknown to the scheduler. Experiments are performed via a simulation software that employs hardware profiles derived from real mobile devices. Our goal is to contribute to better understand both the capabilities and limitations of this kind of schedulers in the incipient area of mobile Grids.

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Reliable resources brokering scheme in wireless grids based on non-cooperative bargaining game

Cited by 27 publications

References 38 publications

Incentive Mechanisms for Participatory Sensing

Incentive Mechanisms for Participatory Sensing

Utility based load balancing using firefly algorithm in cloud

A performance comparison of data-aware heuristics for scheduling jobs in mobile grids

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