Managing capacity for telecommunications networks under uncertainty
IEEE/ACM Transactions on Networking (TON)
Multithreaded Algorithms for Pricing a Class of Complex Options
IPDPS '01 Proceedings of the 15th International Parallel & Distributed Processing Symposium
Performance Evaluation of a Multithreaded Fast Fourier Transform Algorithm for Derivative Pricing
The Journal of Supercomputing
Grid resource management: state of the art and future trends
Grid resource management: state of the art and future trends
Proceedings of the 35th conference on Winter simulation: driving innovation
Proceedings of the 35th conference on Winter simulation: driving innovation
A second order L0 stable algorithm for evaluating European options
International Journal of High Performance Computing and Networking
High performance computing for a financial application using fast fourier transform
Euro-Par'05 Proceedings of the 11th international Euro-Par conference on Parallel Processing
A Market-Based Pricing Scheme for Grid Networks
AIMS '09 Proceedings of the 3rd International Conference on Autonomous Infrastructure, Management and Security: Scalability of Networks and Services
A fuzzy grid-QoS framework for obtaining higher grid resources availability
GPC'08 Proceedings of the 3rd international conference on Advances in grid and pervasive computing
A fuzzy Grid-QoS framework for obtaining higher grid resources availability
The Journal of Supercomputing
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In this study, we model pricing of grid/distributed computing resources as a problem of real option pricing. Grid resources are non-storable compute commodities (eg., CPU cycles, memory, etc). The non-storable characteristic feature of the grid resources hinders it from fitting into a risk-adjusted spot price model for pricing financial options. Grid resources users pay upfront to acquire and use grid compute cycles in the future, for example, six months. The user expects a high and acceptable degree of satisfaction expressed as the Quality of Service (QoS) assurance. This requirement further imposes service constraints on the grid because it must provide a user-acceptable QoS guarantee to compensate for the upfront value. This study integrates three threads of our research; pricing the grid compute cycles as a problem of real option pricing, modeling grid resources spot price using a discrete time approach, and addressing uncertainty constraints in the provision of QoS using fuzzy logic. We have proved the feasibility of this model through experiments and we have presented some of our pricing results and discussed them.