Risk Management and Optimal Pricing in Online Storage Grids

Online storage service providers grant a way for companies to avoid spending resources on maintaining their own in-house storage infrastructure and thereby allowing them to focus on their core business activities. These providers, however, follow a fixed, posted pricing strategy that charges the same price in each time period and thus bear all the risk arising out of demand uncertainties faced by their client companies. We examine the effects of providing a spot market with dynamic prices and forward contracts to hedge against future revenue uncertainty. We derive revenue-maximizing spot and forward prices for a single seller facing a known set of buyers. We perform a simulation study using publicly available traffic data regarding Amazon S3 clients from Alexa.com to validate our analytical results. Our field study supports our analysis and indicates that spot markets alone can enhance revenues to Amazon, but this comes at the cost of increased risks due to the increased market share in the spot markets. Furthermore, adding a forward contract feature to the spot markets can reduce risks while still providing the benefits of enhanced revenues. Although the buyers incur an increase in costs in the spot market, adding a forward contract does not cause any additional cost increase while transferring the risk to the buyers. Thus, storage grid providers can greatly benefit by applying a forward contract alongside the spot market.

[1]  Andrew B. Whinston,et al.  Capacity Provision Networks: Foundations of Markets for Sharable Resources in Distributed Computational Economies , 2008, Inf. Syst. Res..

[2]  Ian T. Foster,et al.  Condor-G: A Computation Management Agent for Multi-Institutional Grids , 2004, Cluster Computing.

[3]  Nemo Semret,et al.  Auctions for Network Resource Sharing , 1997 .

[4]  Rajkumar Buyya,et al.  Economic-based Distributed Resource Management and Scheduling for Grid Computing , 2002, ArXiv.

[5]  K. Eric Drexler,et al.  Markets and computation: agoric open systems , 1988 .

[6]  Gerhard Weiss,et al.  Multiagent Systems and Societies of Agents , 2000 .

[7]  Gerhard Weiss,et al.  Multiagent Systems , 1999 .

[8]  J. Kephart,et al.  Price dynamics of vertically differentiated information markets , 1998, ICE '98.

[9]  Hector Garcia-Molina,et al.  Bidding for storage space in a peer-to-peer data preservation system , 2002, Proceedings 22nd International Conference on Distributed Computing Systems.

[10]  Reid G. Smith,et al.  The Contract Net Protocol: High-Level Communication and Control in a Distributed Problem Solver , 1980, IEEE Transactions on Computers.

[11]  Jan Stallaert,et al.  A Clock-and-Offer Auction Market for Grid Resources When Bidders Face Stochastic Computational Needs , 2011, INFORMS J. Comput..

[12]  Michael Stonebraker,et al.  An economic paradigm for query processing and data migration in Mariposa , 1994, Proceedings of 3rd International Conference on Parallel and Distributed Information Systems.

[13]  Deborah Estrin,et al.  Pricing in computer networks: motivation, formulation, and example , 1993, TNET.

[14]  Spyros Lalis,et al.  JaWS: An Open Market-Based Framework for Distributed Computing over the Internet , 2000, GRID.

[15]  Bernardo A. Huberman,et al.  The ecology of computation , 1988, Digest of Papers. COMPCON Spring 89. Thirty-Fourth IEEE Computer Society International Conference: Intellectual Leverage.

[16]  Jan Stallaert,et al.  A Market Design for Grid Computing , 2008, INFORMS J. Comput..

[17]  Stephen Pickles,et al.  Mini-Grids: Effective Test-Beds for GRID Application , 2000, GRID.

[18]  Stephen Russell,et al.  Resource management in the Mungi single-address-space operating system , 1998 .

[19]  Tad Hogg,et al.  Spawn: A Distributed Computational Economy , 1992, IEEE Trans. Software Eng..

[20]  José Manuél Gómez-Pérez,et al.  Success Stories , 2019, Exploiting Linked Data and Knowledge Graphs in Large Organisations.