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Bin, Li. and Dr. Lee, Gillam. Department of Computing, FEPS University of Surrey, UK Risk Informed Computer Economics Financial Risk Analysis for Autonomic Service Level Agreements

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Time series Analysis Grid, Utility, Cloud…… Computing Computational Market Economics Issues Service Level Agreements......................................absent: Pricing, Liability, etc. Risk Assessment Resource Monitoring ........Analysis Analogy........ Derivatives Risk Ana Financial Derivatives Financial Risk Management Measures Financial Market Motivation .................................. ................................ ................................................................

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Financial MarketGrid Market Resources Equities, Commodities, Currencies... Financial derivatives Computers, workstations, Network speed, clusters… computational power Capacity characteristic Storable (Stock) / Non-storable (futures, forwards) Non-storable Analysis Underlying prices changes time series Resource usage time series Time horizon Holding period (Hourly, daily, weekly, yearly) Hourly, daily, weekly, yearly PortfolioMany Resources Many Computer resources confidence Confidence Level / percentile Confidence of resources availability Result The expected worst loss Optimize the resource use RiskMarket losses Resource probability of Failure Motivation Key References: Financial Grids: Macleod G., Donachy P., Harmer T.J., Perrot R. H., Conlon B., Press J., Lungu F., “Implied Volatility Grid: Grid Based Integration to Provide On Demand Financial Risk Analysis”, Belfast e-Science Centre, Queen’s University of Belfast, 2005. Donachy P., Stødle D., “Risk Grid - Grid Based Integration of Real-Time Value-at-Risk (VaR) Services”, EPSRC UK e-Science All Hands Meeting, 2003. Germano G., Engel M., “City@home: Monte Carlo derivative pricing distributed on networked computers”, Grid Technology for Financial Modelling and Simulation, 2006. Schumacher J., Jaekel U., and Zimmermann F., “Grid Services for Derivatives Pricing”, Grid Technology for Financial Modelling and Simulation, 2006. Grid economics: Gray, J. (2003): Distributed Computing Economics. Microsoft Research Technical Report: MSRTR-2003-24 (also presented in Microsoft VC Summit 2004, Silicon Valey, April 2004) Chetty, M. and Buyya., R. (2002). Weaving electrical and computational grids: How analogous are they? Computing in Science and Engineering, to appear, May/June 2002. Kenyon, C. and Cheliotis., G. (2002). Architecture requirements for commercializing grid resources. In 11th IEEE International Symposium on High Performance Distributed Computing (HPDC'02). Kenyon, C., Cheliotis, G. (2003), Grid Resource Commercialization: Economic Engineering and Delivery Scenarios. Grid Resource Management: State of the Art and Research Issues. Kerstin, V., Karim, D., Iain, G. and James, P. (2007), AssessGrid, Economic Issues Underlying Risk Awareness in Grids, LNCS, Springer Berlin / Heidelberg Birkenheuer, G., Hovestadt, M., Voss, K., Kao, O., Djemame, K., Gourlay, I., Padgett,J.: Introducing Risk Management into the Grid. Proc. 2nd IEEE Intl. Conf. on e-Science and Grid Computing, Amsterdam, The Netherlands (2006)

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Grid for Financial Risk Analysis Risk Fact: Risk is an integral part of the real world in general, and the financial world in particular. Financial Risk Management: Monitory based, losses or profits. Risk can only be reduced (Mitigated) but never eliminated. Fundamental management theory: Portfolio (diversification). Useful analysis measurements (models): Mean-Variance Correlation The sensitivities (The Greeks) Value-at-Risk Market Grid infrastructures in Bank of America and HSBC: 3000 to 6000 processors Computational services market: Customers willing to pay for use of computer systems instead of purchasing and maintaining hardware and software. Grid / Cloud: HP, Amazon, Sun, IBM

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Value-at-Risk (VaR) Defined by Philippe Jorion, Value at Risk theory “summarizes the worst maximum potential loss in value of a portfolio of financial instruments over a certain target horizon with a given level of confidence”. Methods Comparison Monte Carlo Simulation using Condor DAG

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The Bridge Risk analysis Complex financial products and markets Service-based Financial Grids Grid Economics Grid Resources Risk-balanced portfolio Develop possible formulation provide construct

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Financial risk analysis for Grids Grid based financial risk analysis (Financial Grids): - Great demands on available resources; - Assume availability at any given time. Aim: -Ability to predict (risks of resource availability for) the predictability (risks on financial investments). Major impetus for current work - Uncertainty: availability of Grid Resource - Predict future resource availability: Grid Resource Monitoring

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Methodology Closest work: Kerstin et al: risk-aware Grid architecture. Kerstin, V., Karim, D., Iain, G. and James, P., “AssessGrid, Economic Issues Underlying Risk Awareness in Grids”, LNCS, Springer Berlin / Heidelberg, 2007 Specific financial analysis for creating Grid economy over queuing-based systems: eg, Condor Grid Economy as a commodity market; Due considerations: 1. For trading and hedging of risk, options, futures and structured products. 2. Collecting data: historical computational resource use -> predict future resource use for such class of apps. 3. Construction of portfolios of Grid resources (Extension of financial models (CDOs) offers potential for a future market in Grid economics).

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CPU usage (Real Time, year data) CPU usage (Changes, year data) CPU usage (Changes, MC simulated, normal) Predict Future Resource Availability Grid Resource Historical Usage Analyzing: Data source: UK’s National Grid Service (NGS) Monitoring system: Ganglia Grid middleware: Globus Data dimensions: 37 system metrics in XML, including use of network bandwidth, temperature and CPU use Minimum capture interval: 15 seconds Measurements: Distribution analysis Skewness, Kurtosis analysis Prediction: Simulation under normal distribution assumption Simulation under Laplace distribution assumption

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Financial CDO Constructing Grid Resource CDO Processes: sort resources among the Grid into different classes according to the historical information. make different basis points with premium to guarantee various performances. top class resource should have highest premium to insure the most availability and performance. Grid resources CDO

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Autonomic SLAs Dynamically alter themselves as the resource status changes. Strongly connected to the resource CDO, therefore the monitoring system. Also considers the situation while the job in tranches fails. The more expensive and lower risk submission is always guaranteed completion. Protects the processes in the more senior tranches. Protecting the brokers. Multiple providers? Future grid and Cloud computing will benefit.

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Grids for financial risk analysis VaR for portfolio implementation in Condor: Historical, V-C, MC Balancing analysis between computation speed and calculation accuracy Financial risk analysis for Grids Grid Economy over queuing-based system Main idea: predict the predictability Potential formulation of Grid Economy: Resource CDOs Future Work To produce a methodology for calculating and evaluating resource portfolio risk of failure. Constructing an algorithm to create on-the-fly resource tranches (resource CDO). To adapt the use of resource portfolio risk of failure and resource CDO. Automatic creation of autonomic SLAs. How to extend our work into Cloud computing Conclusion and future work

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Thank you for your attention More Details: Bin Li and Lee Gillam (2008) "Grids for Financial Risk Analysis and Financial Risk Analysis for Grids". Proceedings of UK e-Science Programme All Hands Meeting 2008 (AHM 2008). Bin Li and Lee Gillam (2009) "Risk Informed Computer Economics". IEEE International Symposium on Cluster Computing and the Grid (CCGrid 2009, ServP2P). Bin Li and Lee Gillam (2009) "Grid Service Level Agreements using Financial Risk Analysis Techniques". In Antonopoulos, Exarchakos, Li and Liotta (Eds.), Handbook of Research on P2P and Grid Systems for Service-Oriented Computing: Models, Methodologies and Applications. IGI Global. Further information: Bin Li B.Li@surrey.ac.uk Department of computing University of Surrey, UK

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