David Allenotor

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

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.

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

Current research efforts in grid computing show that the available grid resources exist as non-storable compute cycles (grid compute commodities) and distributed geographically across dissimilar organizations with diverse resources usage polices. Therefore, guaranteeing grid resources availability as well as pricing them raises a number of challenging issues in several areas of computer applications. To guarantee QoS we propose a price-based, quality-aware model. We design and develop our model using the financial option theory from a real option perspective and value the grid resources by treating them as real assets. Our hybridized model combines both advantages of fuzzy logic reasoning and real options of a decision-based system. We have taken into account the fact that the grid resources availability depend on the time of use and are transient, and hence solutions from our model captures the realistic value of the grid resources and guarantees the certainty in the resources availability.

A fuzzy Grid-QoS framework for obtaining higher grid resources availability

A computational grid ensures the on-demand delivery of computing resources, in a security-aware, shared, scalable, and standards-based computing environment. A major concern is how to evolve a general and an encompassing framework that guarantees users’ satisfaction measured as Quality of Services (QoS). To obtain a higher QoS, effective QoS perceived by subscribers (users) must conform to specified QoS agreements in the Service Level Agreements (SLAs) document—a legal contract between the Grid Services Provider (GSP) and users. Sometimes the effective user QoS does not conform to the specifications in the SLA because of the vagueness in linguistic definitions in the SLA. Existing approaches overcommitted resources to meet QoS. In this paper, we propose a fuzzy logic framework for calibrating a grid resources user-QoS that addresses the vagueness in linguistic definitions of the SLA document without overcommitting grid resources.

A grid resources valuation model using fuzzy real option

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.

A Financial Option Based Grid Resources Pricing Model: Towards an Equilibrium between Service Quality for User and Profitability for Service Providers

In this paper, we design and develop a financial options-based model for pricing grid resources. The objective is to strike and maintain an equilibrium between service satisfaction of grid users and profitability of service providers. We explain how option theory fits well to price the grid resources. We price various grid resources such as memory, storage, software, and compute cycles as individual commodities. We carried out several experiments and provide a mapping of our research results based on the spot prices to the expected cost of utilizing the resources from three real grids that reflects their usage pattern. We further enhance our model to achieve the objective of equilibrium between Quality of Service (QoS) and profitability from the perspectives of the users and grid operators respectively.

Grid resources valuation with fuzzy real option

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 (e.g., 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, modelling 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.

A novel application of option pricing to distributed resources management

In this paper, we address a novel application of financial option pricing theory to the management of distributed computing resources. To achieve the set objective, first, we highlight the importance of finance models for the given problem and explain how option theory fits well to price the distributed grid compute resources. Second, we design and develop a pricing model and generate pricing results based on the trace data drawn from two real grids: one commercial grid Auvergrid and one experimental platform grid LCG. We evaluate our proposed model using various grid compute resources (such as memory, storage, software, and compute cycles) as individual commodities. By carrying out several experiments, a justification of the pricing model is obtained by comparing real behavior to a simulated system based on the spot price for the resources. We further enhanced our model to achieve a desirable balance between Quality of Service (QoS) and profitability from the perspectives of the users and resource operators respectively.

Integrating a financial option based model with GridSim for pricing Grid resources

Analysis of grid resources utilization from real grid trace data shows the feasibility of a financial option based model for pricing grid resources to attract more users for profitability for the grid provider while providing high Quality of Service (QoS) to users. However, in the absence of grid resource pricing benchmarks, we simulate grid resources usage in order to justify our pricing model using GridSim toolkit. In this work we integrate a financial option based pricing model with GridSim framework and use it as a grid simulation tool to price grid compute resources.

A Discrete Time Financial Option Pricing Model for Cloud Services

Option pricing is one of the most challenging problems in computational finance and derivative modeling. As a result, one has to resort to computational approaches since it is difficult to obtain closed form solution for options other than simple options such as European style options. Also, due to the complex nature of the governing mathematics, several numerical approaches have been proposed in the past to price American style options as well as complex options. In the current study, we apply trinomial lattice which has been used in many scientific and engineering applications to model option pricing for assets with high volatility. The three novelties of this paper include, the formulation of cloud asset price using stochastic process, the improvement of the American style option pricing algorithm by integrating our option pricing factor (pf) into the algorithm, and the presentation of the computed option values for various strike price. With carefully select strike-price spacing, we guarantee a fine-grain integration of pf into the trinomial lattice.

Evaluation of a Financial Option Based Pricing Model for Grid Resources Management: Simulation vs. Real Data

Abstract—In this paper, we apply the theory of financial option to design a model to price grid resources. We use GridSim, a grid simulation tool to simulate resource usage in a Grid. First, we integrate our pricing model to GridSim to price resources for the usage pattern generated randomly for a grid. Then, we price resources on six real grids for the resource usage trace data on these grids that we collected over a period of time.We introduce a new function called price variant function (pvf) in our model to adjust the charges for resources at various times so that the grid remains busy. We show that the pvf helps the resource provider in (1) keeping the grid busy and (2) recovering the investment on the infrastructure in a pre-determined period of time.