Computer Science - Research and Development

, Volume 24, Issue 3, pp 137–151 | Cite as

Multi-objective scheduling for real-time data warehouses

Special Issue Paper

Abstract

The issue of write-read contention is one of the most prevalent problems when deploying real-time data warehouses. With increasing load, updates are increasingly delayed and previously fast queries tend to be slowed down considerably. However, depending on the user requirements, we can improve the response time or the data quality by scheduling the queries and updates appropriately. If both criteria are to be considered simultaneously, we are faced with a so-called multi-objective optimization problem. We transformed this problem into a knapsack problem with additional inequalities and solved it efficiently. Based on our solution, we developed a scheduling approach that provides the optimal schedule with regard to the user requirements at any given point in time. We evaluated our scheduling in an extensive experimental study, where we compared our approach with the respective optimal schedule policies of each single optimization objective.

Keywords

Real-time data warehouse  Scheduling  Resource allocation  Multicriterial optimization 

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References

  1. 1.
    Branke J, Saliho~glu E, Uyar Ş (2005) Towards an analysis of dynamic environments. In: GECCO, ACM, New York, NY, USA, pp 1433–1440Google Scholar
  2. 2.
    Braumandl R, Kemper A, Kossmann D (2003) Quality of service in an information economy. ACM Trans Interet Technol 3(4):291–333CrossRefGoogle Scholar
  3. 3.
    Davison DL, Graefe G (1995) Dynamic resource brokering for multi-user query execution. SIGMOD Rec 24(2):281–292CrossRefGoogle Scholar
  4. 4.
    Dellaert BGC, Kahn BE (1999) How tolerable is delay? consumers evaluations of internet web sites after waiting. J Interact Market 13:41–54CrossRefGoogle Scholar
  5. 5.
    Francalanci C, Pernici B (2004) Data quality assessment from the user’s perspective. In: IQIS, ACM, New York, NY, USA, pp 68–73, doi: http://doi.acm.org/10.1145/1012453.1012465
  6. 6.
    Garey MR, Johnson DS (1979) Computers and Intractability: A Guide to the Theory of NP-Completeness. W.H. Freeman, New York, NYGoogle Scholar
  7. 7.
    Haritsa JR, Carey MJ, Livny M (1993) Value-Based Scheduling in Real-Time Database Systems. VLDB J 2(2):117–152CrossRefGoogle Scholar
  8. 8.
    Hong D, Johnson T, Chakravarthy S (1993) Real-time transaction scheduling: A cost conscious approach. In: Proceedings of the 1993 ACM SIGMOD international conference on Management of data, May 25–28, 1993, Washington, D.C., pp 197–206Google Scholar
  9. 9.
    Hughes EJ (2005) Evolutionary many-objective optimisation: many once or one many? In: Congress on Evolutionary Computation, Institute of Electrical and Electronics Engineers (IEEE), March, 2008, Witten-Bommerholz, pp 222–227Google Scholar
  10. 10.
    Kang KD (2004) Managing deadline miss ratio and sensor data freshness in real-time databases. TKDE 16(10):1200–1216, senior member Sang H. Son and fellow John A. StankovicGoogle Scholar
  11. 11.
    Kang KD, Son SH, Stankovic JA, Abdelzaher TF (2002) A qos-sensitive approach for timeliness and freshness guarantees in real-time databases. In: ECRTS, pp 203–212Google Scholar
  12. 12.
    Karp RM (1972) Reducibility among combinatorial problems. In: Complexity of Computer Computations. Plenum, New YorkGoogle Scholar
  13. 13.
    Krompass S, Dayal U, Kuno HA, Kemper A (2007) Dynamic workload management for very large data warehouses: Juggling feathers and bowling balls. In: VLDB, pp 1105–1115Google Scholar
  14. 14.
    Leung J, Kelly L, Anderson JH (2004) Handbook of Scheduling: Algorithms, Models, and Performance Analysis. CRC Press, Inc., Boca Raton, FL, USAMATHGoogle Scholar
  15. 15.
    Motro A, Rakov I (1996) Estimating the quality of data in relational databases. In: In Proceedings of the 1996 Conference on Information Quality, MIT, pp 94–106Google Scholar
  16. 16.
    Nauss RM (1978) The 0-1 knapsack problem with multiple choice constraints. Eur J Oper Res 2(2):125–131MATHCrossRefMathSciNetGoogle Scholar
  17. 17.
    Nemhauser G, Ullmann Z (1969) Discrete dynamic programming and capital allocation. Manag Sci 15:494–505CrossRefMathSciNetGoogle Scholar
  18. 18.
    Schrage LE (1968) A proof of the optimality of the shortest remaining processing time discipline. Oper Res 16:678–690CrossRefGoogle Scholar
  19. 19.
    Schrage LE, Miller LW (1966) The queue m/g/1 with the shortest remaining processing time discipline. Oper Res 14:670–684MATHCrossRefMathSciNetGoogle Scholar
  20. 20.
    Schroeder B, Harchol-Balter M, Iyengar A, Nahum E (2006) Achieving class-based qos for transactional workloads. In: ICDE, IEEE Computer Society, Washington, DC, USA, p 153Google Scholar
  21. 21.
    Smith WE (1956) Various optimizers for single-stage production. Naval Res Logist Quart 3:59–66CrossRefMathSciNetGoogle Scholar
  22. 22.
    Stonebraker M, Aoki PM, Litwin W, Pfeffer A, Sah A, Sidell J, Staelin C, Yu A (1996) Mariposa: a wide-area distributed database system. The VLDB J 5(1):048–063CrossRefGoogle Scholar
  23. 23.
    Thiele M, Fischer U, Lehner W (2007) Partition-based workload scheduling in living data warehouse environments. In: DOLAP, ACM, New York, NY, USA, pp 57–64Google Scholar
  24. 24.
    Thiele M, Fischer U, Lehner W (2008) Partition-based workload scheduling in living data warehouse environments. Inform Syst 34:1–5Google Scholar
  25. 25.
    Thomsen C, Pedersen TB, Lehner W (2008) Rite: Providing on-demand data for right-time data warehousing. In: ICDE, pp 456–465Google Scholar
  26. 26.
    T’Kindt V, Billaut JC (2006) Multicriteria Scheduling – Theory, Models and Algorithms. Springer Verlag, BerlinMATHGoogle Scholar
  27. 27.
    Toth P (1980) Dynamic programming algorithms for the zero-one knapsack problem. Computing 25:29–45MATHCrossRefMathSciNetGoogle Scholar
  28. 28.
    Vassiliadis P, Bouzeghoub M, Quix C (1999) Towards quality-oriented data warehouse usage and evolution. In: CAiSE, Springer, Heidelberg, pp 164–179Google Scholar
  29. 29.
    Weikum G (1999) Towards guaranteed quality and dependability of information systems. In: Proceedings of the Conference Datenbanksysteme in Buro, Technik und Wissenschaft, Springer Verlag, pp 379–409Google Scholar
  30. 30.
    Zhou M, Zhou L (1996) How does waiting duration information influence customers’ reactions to waiting for services. J Appl Soc Psychol 26:1702–1717CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2009

Authors and Affiliations

  1. 1.Technische Universität DresdenDatabase Technology GroupDresdenGermany

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