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iCBS: Incremental Cost-based Scheduling under Piecewise Linear SLAs
Yun Chi, Hyun Jin Moon, Hakan Hacigumus
NEC Laboratories America
Cupertino, USA
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2 NECLA Data Management Research VLDB 2011
Outline of the Talk
Motivation and background iCBS with O(log N) time complexity iCBS with O(log^2 N) time complexity Experimental results Conclusion and future work
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3 NECLA Data Management Research VLDB 2011
Outline of the Talk
Motivation and background iCBS with O(log N) time complexity iCBS with O(log^2 N) time complexity Experimental results Conclusion and future work
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4 NECLA Data Management Research VLDB 2011
Motivation
Cost-aware scheduling each query has its cost scheduling considers costs
Important for a cloud service provider query deadline (Web queries) service level (gold vs. silver customer) explicit SLAs (often piecewise linear)
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5 NECLA Data Management Research VLDB 2011
Motivation—CBS [Peh91]
The good cost/deadline aware very good cost performance
Low S
yste
m Loa
d
High
Syst
em Loa
d0
0.2
0.4
0.6
ASETS*FirstRewardsCBS
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6 NECLA Data Management Research VLDB 2011
Motivation—CBS
The bad, at each time t, O(N) scores are computed each score involves an integration:
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7 NECLA Data Management Research VLDB 2011
Our Contributions
Investigate CBS under piecewise linear SLAs how things change over time
Develop efficient iCBS uses above observations maintains scores incrementally no integration used achieves O(log^2 N) time complexity
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8 NECLA Data Management Research VLDB 2011
Piecewise Linear SLAs
Agreement on query response time cost function f(t) is finite segments over time each segment is a linear function
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9 NECLA Data Management Research VLDB 2011
Outline of the Talk
Motivation and background iCBS with O(log N) time complexity iCBS with O(log^2 N) time complexity Experimental results Conclusion and future work
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10 NECLA Data Management Research VLDB 2011
iCBS—Easy Cases, SLA (a)
CBS score is constant for this SLA
Refer to as in α stage
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11 NECLA Data Management Research VLDB 2011
iCBS—Easy Cases, SLA (b)
CBS score is time-variant
However, only relative order is needed Refer to as β stage
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12 NECLA Data Management Research VLDB 2011
iCBS—Easy Cases, SLAs (c),(d)
CBS scores are time-variant in special ways
β stage, and then α stage
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13 NECLA Data Management Research VLDB 2011
Outline of the Talk
Motivation and background iCBS with O(log N) time complexity iCBS with O(log^2 N) time complexity Experimental results Conclusion and future work
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14 NECLA Data Management Research VLDB 2011
iCBS—Hard Cases, SLAs (e),(f)
CBS scores are time-variant
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15 NECLA Data Management Research VLDB 2011
iCBS—Hard Cases, Solution
Put the scores in the dual space
time-invariant in the dual space
At time t’, find , search in dual space
atat ewithfetf ,)()(
),()( f
'
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16 NECLA Data Management Research VLDB 2011
iCBS—Revisit Easy Cases
Why the easy ones are easy Either in α stage, or β stage
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17 NECLA Data Management Research VLDB 2011
iCBS—Incremental Maintenance
In the dual space time-variant CBS a point position changes K times
Highest score on the convex hull
O(log^2 N) dynamic convex hull algorithm [PS85]
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18 NECLA Data Management Research VLDB 2011
Outline of the Talk
Motivation and background iCBS with O(log N) time complexity iCBS with O(log^2 N) time complexity Experimental results Conclusion and future work
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19 NECLA Data Management Research VLDB 2011
Experiment—Effectiveness
Compare iCBS’s cost per query with cost-unaware FCFS and SJF ASETS* by Guirguis et al. [GSC+09] FirstReward by Irwin et al. [IGC04]
Using different SLAs weighted tardiness (ASETS* [GSC+09]) tardiness with upper bound (FirstReward [IGC04])
Over a variety of SLA parameters decay skew factor value skew factor
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20 NECLA Data Management Research VLDB 2011
Experiment—Effectiveness, SLA-1
ASETS* designed for this SLA CBS (iCBS) has best performance, especially
with skewed SLAs, and high system load
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21 NECLA Data Management Research VLDB 2011
Experiment—Effectiveness, SLA-2
FirstReward designed for this SLA CBS (iCBS) has best performance ASETS* cannot be finished (days)
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22 NECLA Data Management Research VLDB 2011
Experiment—Efficiency
iCBS with CBS: time vs. queue length Query execution time
exponential distribution (OLTP) Pareto (long-tail) distribution (OLAP)
Detailed setting Xeon PC, 3GHz CPU, 4GB memory Fedora 11 Linux implemented in Java
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23 NECLA Data Management Research VLDB 2011
Experiment—Efficiency, Exponential
CBS: obviously O(N) iCBS: relatively constant
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24 NECLA Data Management Research VLDB 2011
Experiment—Efficiency, Pareto
With long queue, CBS takes >10ms iCBS still 10-20 us
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25 NECLA Data Management Research VLDB 2011
Related Work
Haritsa et al. [HCL93], value-based scheduling Guirguis et al. [GSC+09], tardiness minimization Irwin et al. [IGC04], balance risk and reward Chi et al. [CMHT11], step-wise cost functions Peha [Peh91], cost-based scheduling (CBS)
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26 NECLA Data Management Research VLDB 2011
Outline of the Talk
Motivation and background iCBS with O(log N) time complexity iCBS with O(log^2 N) time complexity Experimental results Conclusion and future work
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27 NECLA Data Management Research VLDB 2011
Conclusion and Future Work
Conclusion incremental cost-based scheduling under piecewise linear SLAs
Future directions query execution time: certain uncertain MPL: 1 M what to schedule: queries transactions
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28 NECLA Data Management Research VLDB 2011
Reference
[CMHT11] Y. Chi, H. J. Moon, H. Hacigumus, and J. Tatemura. SLA-tree: A framework for efficiently supporting SLA-based decisions in cloud computing. In EDBT, pages 129–140, 2011.
[GSC+09] Shenoda Guirguis, Mohamed A. Sharaf, Panos K. Chrysanthis, Alexandros Labrinidis, and Kirk Pruhs. Adaptive scheduling of web transactions. In ICDE, pages 357–368, 2009.
[HCL93] Jayant R. Haritsa, Michael J. Carey, and Miron Livny. Value-based scheduling in real-time database systems. The VLDB Journal, 2:117–152, 1993.
[IGC04] David E. Irwin, Laura E. Grit, and Jeffrey S. Chase. Balancing risk and reward in a market-based task service. In HPDC, pages 160–169, 2004.
[Peh91] Jon Michael Peha. Scheduling and dropping algorithms to support integrated services in packet-switched networks. PhD thesis, Stanford University, 1991.
[PS85] Franco P. Preparata and Michael I. Shamos. Computational geometry: an introduction. Springer-Verlag, Inc., New York, NY, USA, 1985.
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29 NECLA Data Management Research VLDB 2011
Backup Slide
Cost SLAs and profit SLAs are equivalent
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30 NECLA Data Management Research VLDB 2011
Backup Slide
Performance for the most general SLAs