A Fuzzy Real Option Model to Price Grid Compute Resources

A Fuzzy Real Option Model to Price Grid Compute Resources

David Allenotor (University of Manitoba, Canada), Ruppa K. Thulasiram (University of Manitoba, Canada), Kenneth Chiu (SUNY at Binghamton, USA) and Sameer Tilak (University of California at San Diego, USA)
Copyright: © 2010 |Pages: 15
DOI: 10.4018/978-1-60566-661-7.ch021
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Abstract

A computational grid is a geographically disperssed heterogeneous computing facility owned by dissimilar organizations with diverse usage policies. As a result, guaranteeing grid resources availability as well as pricing them raises a number of challenging issues varying from security to management of the grid resources. In this chapter we design and develop a grid resources pricing model using a fuzzy real option approach and show that finance models can be effectively used to price grid resources.
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Introduction

Ian Foster and Carl Kesselman (I. Foster & Kesselman, 1999) describe the grid as an infrastructure that provides a dependable, consistent, pervasive, and inexpensive access to high-end computational capabilities that enable the sharing, exchange, selection, and aggregation of geographically distributed resources. A computational grid is analogous to an electrical power grid. In the electric power grid, electrical energy is generated form various sources such as coal, solar, hydro or nuclear. The user of electrical energy has no knowledge about the source of the energy but only concerned about availability and ubiquity of the energy. Likewise, the computational grid is characterized by heterogeneous resources (grid resources) which are owned by multiple organizations and individuals. The grid distributed resources include but not limited to CPU cycles, memory, network bandwidths, throughput, computing power, disks, processor, software, various measurements and instrumentation tools, computers, software, catalogue data and databases, special devices and instruments, and people/collaborators. We describe the grid compute resources as grid compute commodities (gccs) that need to be priced. This chapter focuses on the design and development of a grid resource pricing model with an objective to provide optimal gain (profitability wise) for the grid operators and a satisfaction guarantee measured as Quality of Service1 (QoS) requirements for grid resource users and resources owners through a regulated Service Level Agreements2 (SLAs)-based resource pricing. We design our pricing model using a discrete time numerical approach to model grid resources spot price. We then model resources pricing problem as a real option pricing problem. We monitor and maintain the grid service quality by addressing uncertainty constraints using fuzzy logic.

In recent times, research efforts in computational grid has focused on developing standard for grid middleware in order to provide solutions to grid security issues and infrastructure-based issues (I. T. Foster, Kesselman, Tsudik & Tuecke, 1998), and grid market economy, (Schiffmann, Sulistio, & Buyya, 2007). Since grid resources have been available for free there has been only little effort made to price them. However, a trend is developing due to large interest in grid for public computing and because several business operatives do not want to invest in computing infrastructures due to the dynamic nature of information technology, there is expected to be huge demand for grid computing infrastructures and resources. In the future, therefore, a sudden explosion of grid usage is expected. In anticipation to cope with the sudden increase in grid and grid resources usage, Amazon has introduced a Simple Storage Service (S3) (Palankar, Onibokun, Iamnitchi, & Ripeanu, 2007) for grid consumers. S3 offers a pay-as-you-go online storage, and as such, it provides an alternative to in-house mass storage. A major drawback of the S3 is data access performance. Although the S3 project is successful, its current architecture lack requirements for supporting scientific collaborations due to its reliance on a set of assumptions based on built-in trusts.

Key Terms in this Chapter

Resource Pricing: A fair share of the grid resources that depends highly on availability (monitored by price variant factor) rather than market forces of demand and supply.

Grid Computing: A computing grid is a system that delivers processing power of a massively parallel computation and facilitates the deployment of resources-intensive applications

Distributed Computing: Grid resource as they relates to the geographical regions which is a factor in terms of availability and computability.

Price Adjustments: A control/ feed back structure that modulate grid resources price with a specific objective to benefits users and grid operatives; value depends of current tecnology or maket trend.

Resource Management: This refers to provision of the grid resources to users at the time of requested utilization.

Real Option Model: A mathematical framework similar to financial options but characterized by uncertainty in decision flexibility in a known future for determining project viabilites.

Fuzzy Support for QoS: A decision support systems that is based on managing uncertainties associated with grid resources availability.

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