G-FRoM: Grid Resources Pricing A Fuzzy Real Option Model

Allenotor, David; Thulasiram, Ruppa K.

doi:10.1109/e-science.2007.37

Cited by 10 publications

(4 citation statements)

References 25 publications

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“…However, we can use our model for pricing other commodities in the grid as well to quote prices for them. Some of the preliminary results are presented in [1] and [2]. For the current study, we introduced an artificial spot prices for the gcc at various times of the contract period as exemplified by the trinomial tree structure of the solution space.…”

Section: Resultsmentioning

confidence: 99%

Grid resources pricing: A novel financial option based quality of service-profit quasi-static equilibrium model

Allenotor

Thulasiram

2008

2008 9th IEEE/ACM International Conference on Grid Computing

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Use of grid resources has been free so far and a trend is developing to charge the users. The challenges that characterize a grid resource pricing model include the dynamic ability of the model to provide a high satisfaction guarantee measured as Quality of Service (QoS) -from users perspectives, profitability constraints -from the grid operator perspectives, and the ability to orchestrate grid resources for their availability on-demand. In this study, we design, develop, and simulate a grid resources pricing model that balances these constraints. We employ financial option theory and treat the grid resources as real assets to capture the realistic value of the grid compute commodities. We then price the grid resources by solving the finance model. We discuss the results on pricing of compute cycles based on the actual data of grid usage pattern obtained from the WestGrid and the SHARCNET. We extend and generalize our study to any computational grid. Background/IntroductionOver the years, there has been an upsurge in the development and applications of resources-intensive computations such as the Search for Extraterrestrial Intelligence at home (SETI@Home). The consequence is the emergence of a massively parallel resource-intensive computational facility -the computational grid (Grid computing). Research efforts have focused mostly on security related issues [15] and middleware/infrastructure-based issues [16]. Since grid resources usage has been free, there are only few efforts reported in the literature that develop models to price grid resources.Grid resources pricing is a challenging task when viewed as a generic pricing problem. In a grid system for example, the resources exist as non-storable commodities and are distributed across wide geographical regions, ownership is by dissimilar organization whose rights and polices are diverse. To price the grid resources, we assume that the grid compute commodities (gcc) are real assets. The gccs include CPU cycles, memory, network bandwidths, throughput, computing power, disks, processor, and various measurements, instrumentation tools. Since gccs are transient at t 0 , t 1 , · · · , t n , their availability (α) for usage fluctuates between "now" (t n|n=0 ) and "later" (t n > 0| t n =1 ) (option to wait) which accounts for uncertainty (ũ) where gcc : α → [0, · · · , 1]. The flexibility attributes of the gccs afford the applicability of real option defined in a fuzzy domain [0, · · · , 1] (also called membership function) [6].Real option theory provides a mechanism for valuating investment opportunities in situations where the cash flows are not deterministic. Real options can be applied when quantifying the accrued gains from prototyping a product as well as in managerial flexibility. Several of these gains can be hard to quantify [12] and as a result, a comparison of alternative opportunities becomes even harder if the managerial flexibility vary across the opportunities. Given that certain factors such as environmental and economics are inevitable, traditional val...

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Section: Resultsmentioning

confidence: 99%

Grid resources pricing: A novel financial option based quality of service-profit quasi-static equilibrium model

Allenotor

Thulasiram

2008

2008 9th IEEE/ACM International Conference on Grid Computing

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“…The concise and effective fuzzy real options approach proposed by Carlsson and Fuller is widely applicable; see [19][20][21] . But this specific model restrict fuzzy variables to the exercise price and current cash flow.…”

Section: Fuzzy Real Optionsmentioning

confidence: 99%

Fuzzy Real Options in Brownfield Redevelopment Evaluation

Wang

Hipel

Kilgour

2009

Journal of Applied Mathematics and Decision Sciences

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Real options modeling, which extends the ability of option pricing models to evaluate real assets, can be used to evaluate risky projects because of its capacity to handle uncertainties. This research utilizes possibility theory to represent private risks of a project, which are not reflected in the market and hence are not fully evaluated by standard option pricing models. Using a transformation method, these private risks can be represented as fuzzy variables and then priced with a fuzzy real options model. This principle is demonstrated by valuing a brownfield redevelopment project using a prototype decision support system based on fuzzy real options. Because they generalize the original model and enable it to deal with additional uncertainties, fuzzy real options are entirely suitable for the evaluation of such projects.

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“…Many papers have been published to address to the QoS based resource reservation scheduling and negotiation [6,7] issue. Market based scheduling and negotiation algorithms [8] including the commodity model [9,10] and the auction model [11] appraise the quality of a resource as its attribution in the commerce based negotiation and mapping process. All these work focus on guaranteeing the QoS by ensuring every task is allocated with appropriate execution duration on a corresponding resource and little attention has been put on whether or not this resource can finish the task in time.…”

Section: B Related Workmentioning

confidence: 99%

Committee-based evaluation and selection of Grid resources for QoS improvement

Wang

Cao

2009

2009 10th IEEE/ACM International Conference on Grid Computing

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Abstract-The grid enables the facility of distributed resource sharing but can provide limited QoS guarantee to grid applications, e.g. in terms of execution time. In this paper, we propose a Committee-based Resource Evaluation and Selection Method (CRESM) to evaluate and select reliable resources for Users, which have higher possibility of finishing tasks in time and provide better QoS. CRESM is composed with a representative layer and a committee layer. The representative layer consists of informed and experienced Users who contribute their individual experiences to judge a particular grid resource. The committee layer aggregates these individual judgments and makes a comprehensive decision based on these aggregated information. All the judgments and the final decision are made fuzzily based on historical statistics of resource evaluation. Experimental results show that CRESM is stable and accurate in different grid environments. CRESM can also defeat collusive deceitful behaviors. Meanwhile, CRESM based resource scheduling can improve QoS support for grid applications.

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G-FRoM: Grid Resources Pricing A Fuzzy Real Option Model

Cited by 10 publications

References 25 publications

Grid resources pricing: A novel financial option based quality of service-profit quasi-static equilibrium model

Grid resources pricing: A novel financial option based quality of service-profit quasi-static equilibrium model

Fuzzy Real Options in Brownfield Redevelopment Evaluation

Committee-based evaluation and selection of Grid resources for QoS improvement

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