A Fuzzy Real Option Model for Pricing Grid Compute Resources
| dc.contributor.author | Allenotor, David | |
| dc.contributor.examiningcommittee | Graham, Peter (Computer Science) Walton, Desmond (Computer Science) Mossman, Charles (Accounting and Finance) Buyya, Rajkumar (The University of Melbourne) | en |
| dc.contributor.supervisor | Thulasiram, Ruppa (Computer Science) | en |
| dc.date.accessioned | 2011-01-21T15:41:53Z | |
| dc.date.available | 2011-01-21T15:41:53Z | |
| dc.date.issued | 2011-01-21T15:41:53Z | |
| dc.degree.discipline | Computer Science | en_US |
| dc.degree.level | Doctor of Philosophy (Ph.D.) | en_US |
| dc.description.abstract | Many of the grid compute resources (CPU cycles, network bandwidths, computing power, processor times, and software) exist as non-storable commodities, which we call grid compute commodities (gcc) and are distributed geographically across organizations. These organizations have dissimilar resource compositions and usage policies, which makes pricing grid resources and guaranteeing their availability a challenge. Several initiatives (Globus, Legion, Nimrod/G) have developed various frameworks for grid resource management. However, there has been a very little effort in pricing the resources. In this thesis, we propose financial option based model for pricing grid resources by devising three research threads: pricing the gcc as a problem of real option, modeling gcc spot price using a discrete time approach, and addressing uncertainty constraints in the provision of Quality of Service (QoS) using fuzzy logic. We used GridSim, a simulation tool for resource usage in a Grid to experiment and test our model. To further consolidate our model and validate our results, we analyzed usage traces from six real grids from across the world for which we priced a set of resources. We designed a Price Variant Function (PVF) in our model, which is a fuzzy value and its application attracts more patronage to a grid that has more resources to offer and also redirect patronage from a grid that is very busy to another grid. Our experimental results show that the application of the PVF has helped achieve equilibrium between users satisfaction measured as QoS and recovery of the infrastructure investment made by the providers. In the absence of pricing benchmarks, we setup Commodity Base Prices (CBP) and then integrated our PVF and CBP with GridSim to price grid compute resources. In summary, this thesis provides the design of a model to price grid compute resources using financial options theory. The model achieves mutual benefit for users and providers in the grid environment. The mutual benefit is expressed in terms of QoS to the users and recovery of investments on the grid infrastructure for the providers. This thesis has opened up many different opportunities for further research especially in the era of enterprise computing with clouds. | en |
| dc.description.note | February 2011 | en |
| dc.format.extent | 1902142 bytes | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.uri | http://hdl.handle.net/1993/4398 | |
| dc.language.iso | eng | en_US |
| dc.rights | open access | en_US |
| dc.subject | Grid Resources | en |
| dc.subject | Financial Options | en |
| dc.subject | Real Options | en |
| dc.subject | QoS | en |
| dc.subject | Fuzzy Logic | en |
| dc.subject | Cloud and Grid Computing | en |
| dc.title | A Fuzzy Real Option Model for Pricing Grid Compute Resources | en |
| dc.type | doctoral thesis | en_US |