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Cloud-Based DSS and Availability Context: The Probability of Successful Decision Outcomes

  • Stephen Russell
  • Victoria Yoon
  • Guisseppi Forgionne
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 22)

Abstract

In an age of cloud computing, mobile users, and wireless networks, the availability of decision support related computing resources can no longer guarantee five-nines (99.999%) availability but the dependence on decision support systems is ever increasing. If not already, the likelihood of obtaining accurate deterministic advice from these systems will become critical information. This study proposes a probabilistic model that maps decision resource availability to correct decision outcomes. Grounded in system reliability theory, the probability functions are given and developed. The model is evaluated with a simulated decision opportunity and the outcome of the experimentation is quantified using a goodness of fit measure and ANOVA testing.

Keywords

Context Aware Computing Availability Awareness Decision Support Systems Cloud Computing 

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References

  1. 1.
    Buyya, R., Sulistio, A.: Service and Utility Oriented Distributed Computing Systems: Challenges and Opportunities for Modeling and Simulation Communities. In: 41st Annual Simulation Symposium (ANSS-41), Ottawa, Canada, pp. 68–81 (2008)Google Scholar
  2. 2.
    Russell, S., Forgionne, G., Yoon, V.: Presence and Availability Awareness for Decision Support Systems in Pervasive Computing Environments. International Journal of Decision Support System Technology 1 (2008)Google Scholar
  3. 3.
    Bhagwan, R., Savage, S., Voelker, G.: Understanding Availability. In: Peer-to-Peer Systems II (IPTPS 2003), Berkeley, CA, USA, pp. 256–267 (2003)Google Scholar
  4. 4.
    Reussner, R.H., Schmidt, H.W., Poernomo, I.H.: Reliability Prediction for Component-Based Software Architectures. Journal of Systems and Software 66, 241–252 (2003)CrossRefGoogle Scholar
  5. 5.
    Mikic-Rakic, M., Malek, S., Medvidovic, N.: Improving Availability in Large, Distributed Component-Based Systems Via Redeployment. In: Dearle, A., Eisenbach, S. (eds.) CD 2005. LNCS, vol. 3798, pp. 83–98. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  6. 6.
    Henson, V., Ven, A.v.d., Gud, A., Brown, Z.: Chunkfs: Using Divide–and–Conquer to Improve File System Reliability and Repair. In: 2nd Workshop on Hot Topics in System Dependability (HotDep 2006), Seattle, WA, USA (2006)Google Scholar
  7. 7.
    Dai, Y.S., Xie, M., Poh, K.L., Liu, G.Q.: A Study of Service Reliability and Availability for Distributed Systems. Reliability Engineering & System Safety 79, 103–112 (2003)CrossRefGoogle Scholar
  8. 8.
    Salas, J., Perez-Sorrosal, F., Martinez, M.P., Jimenez-Peris, R.: Ws-Replication: A Framework for Highly Available Web Services. In: 15th International World Wide Web Conference, Edinburgh, Scotland. ACM, New York (2006)Google Scholar
  9. 9.
    Sung, H., Choi, B., Kim, H., Song, J., Han, S., Ang, C.-W., Cheng, W.-C., Wong, K.-S.: Dynamic Clustering Model for High Service Availability. In: Eighth International Symposium on Autonomous Decentralized Systems (ISADS 2007), Sedona, AZ, USA. IEEE Computer Society, Los Alamitos (2007)Google Scholar
  10. 10.
    Ibach, P., Horbank, M.: Highly Available Location-Based Services in Mobile Environments. In: Malek, M., Reitenspiess, M., Kaiser, J. (eds.) ISAS 2004. LNCS, vol. 3335, pp. 134–147. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  11. 11.
    Xu, J., Lee, W.: Sustaining Availability of Web Services under Distributed Denial of Service Attacks. IEEE Transactions on Computers 52, 195–208 (2003)CrossRefGoogle Scholar
  12. 12.
    Chakraborty, S., Yau, D.K.Y., Lui, J.C.S., Dong, Y.: On the Effectiveness of Movement Prediction to Reduce Energy Consumption in Wireless Communication. IEEE Transactions on Mobile Computing 5, 157–169 (2006)CrossRefGoogle Scholar
  13. 13.
    Rahmati, A., Zhong, L.: Context-for-Wireless: Context-Sensitive Energy-Efficient Wireless Data Transfer. In: 5th International Conference on Mobile systems, Applications and Services, San Juan, Puerto Rico, pp. 165–178 (2007)Google Scholar
  14. 14.
    Shahram, G., Shyam, K., Bhaskar, K.: An Evaluation of Availability Latency in Carrier-Based Wehicular Ad-Hoc Networks. In: Proceedings of the 5th ACM international workshop on Data engineering for wireless and mobile access, Chicago, Illinois, USA. ACM Press, New York (2006)Google Scholar
  15. 15.
    Roughan, M., Griffin, T., Mao, M., Greenberg, A., Freeman, B.: Combining Routing and Traffic Data for Detection of Ip Forwarding Anomalies. ACM SIGMETRICS Performance Evaluation Review 32, 416–417 (2004)CrossRefGoogle Scholar
  16. 16.
    Brown, A., Oppenheimer, D., Keeton, K., Thomas, R., Kubiatowicz, J., Patterson, D.A.: Istore: Introspective Storage for Data-Intensive Network Services. In: The IEEE Seventh Workshop on Hot Topics in Operating Systems, Rio Rico, AZ, USA, pp. 32–37 (1999)Google Scholar
  17. 17.
    Weatherspoon, H., Chun, B.-G., So, C.W., Kubiatowicz, J.: Long-Term Data Maintenance in Wide-Area Storage Systems: A Quantitative Approach. In: University of California, Berkely, Electrical Engineering & Computer Sciences Department, Berkely, CA, USA (2005)Google Scholar
  18. 18.
    Zhoujun, H., Zhigang, H., Zhenhua, L.: Resource Availability Evaluation in Service Grid Environment. In: 2nd IEEE Asia-Pacific Service Computing Conference (APSCC 2007), Tsukuba Science City, Japan. IEEE Computer Society, Los Alamitos (2007)Google Scholar
  19. 19.
    Blake, C., Rodrigues, R.: High Availability, Scalable Storage, Dynamic Peer Networks: Pick Two. In: The 9th conference on Hot Topics in Operating Systems (HOTOS 2003), Lihue, HI, USA, p. 1 (2003)Google Scholar
  20. 20.
    Thio, N., Karunasekera, S.: Automatic Measurement of a Qos Metric for Web Service Recommendation. In: 2005 Australian Software Engineering Conference, Brisbane, Australia, pp. 202–211 (2005)Google Scholar
  21. 21.
    Loyall, J.P., Schantz, R.E., Zinky, J.A., Bakken, D.E.: Specifying and Measuring Quality of Service in Distributed Object Systems. In: First International Symposium on Object-Oriented Real-Time Distributed Computing (ISORC 1998), Kyoto, Japan (1998)Google Scholar
  22. 22.
    Ali, A.S., Rana, O., Walker, D.W.: Ws-Qoc: Measuring Quality of Service Compliance. In: International Conference on Service Oriented Computing (ICSOC 2004), New York, NY, USA (2004)Google Scholar
  23. 23.
    Menasce, D.A.: Composing Web Services: A Qos View. IEEE Internet Computing 8, 88–90 (2004)CrossRefGoogle Scholar
  24. 24.
    Peer, J.: Web Service Composition as Ai Planning - a Survey, University of St. Gallen (2005)Google Scholar
  25. 25.
    Pistore, M., Barbon, F., Bertoli, P., Shaparau, D., Traverso, P.: Planning and Monitoring Web Service Composition (2004)Google Scholar
  26. 26.
    Muhlenbrock, M., Brdiczka, O., Snowdon, D., Meunier, J.L.: Learning to Detect User Activity and Availability from a Variety of Sensor Data. In: Second IEEE Annual Conference on Pervasive Computing and Communications (PerCom 2004), Orlando, FL, USA, pp. 13–22 (2004)Google Scholar
  27. 27.
    Danninger, M., Kluge, T., Stiefelhagen, R.: Myconnector: Analysis of Context Cues to Predict Human Availability for Communication. In: 8th international conference on Multimodal interfaces, Banff, Alberta, Canada (2006)Google Scholar
  28. 28.
    Begole, J.B., Matsakis, N.E., Tang, J.C.: Lilsys: Sensing Unavailability. In: 2004 ACM conference on Computer supported cooperative work, Chicago, Illinois, USA (2004)Google Scholar
  29. 29.
    Horvitz, E., Koch, P., Kadie, C.M., Jacobs, A.: Coordinate: Probabilistic Forecasting of Presence and Availability. In: The Eighteenth Conference on Uncertainty and Artificial Intelligence, Edmonton, Alberta, Canada, pp. 224–233 (2002)Google Scholar
  30. 30.
    Covin, J.G., Slevin, D.P., Heeley, M.B.: Strategic Decision Making in an Intuitive Vs. Technocratic Mode: Structural and Environmental Considerations. Journal of Business Research 52, 51–67 (2001)CrossRefGoogle Scholar
  31. 31.
    Amaro, H., Blake, S.M., Morrill, A.C., Cranston, K., Logan, J., Conron, K.J., Dai, J.: HIV Prevention Community Planning: Challenges and Opportunities for Data-Informed Decision-Making. AIDS and Behavior 9, 9–27 (2005)CrossRefGoogle Scholar
  32. 32.
    Fontanills, G.A., Gentile, T.: The Stock Market Course. John Wiley & Sons, New York (2001)Google Scholar
  33. 33.
    Chernoff, H., Lehmann, E.L.: The Use of Maximum Likelihood Estimates in X2 Tests for Goodness-of-Fit. The Annals of Mathematical Statistics 25, 579–586 (1954)MathSciNetCrossRefzbMATHGoogle Scholar
  34. 34.
    Plackett, R.L.: Karl Pearson and the Chi-Squared Test. International Statistical Review 51, 59–72 (1983)MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Stephen Russell
    • 1
  • Victoria Yoon
    • 2
  • Guisseppi Forgionne
    • 2
  1. 1.Department of Information Systems & Technology ManagementThe George Washington UniversityWashingtonUSA
  2. 2.Information Systems DepartmentUniversity of Maryland, Baltimore CountyBaltimore, MarylandUSA

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