Outline Shared computational infrastructures Tragedy of the
commons PlanetLab as case study Computational markets What is
mising? Main contributions Problem Approach Results 2
Slide 3
Contributions 3 Model and analysis of a large scale
infrastructure Xavier Len, Tuan Anh Trinh, and Leandro Navarro.
Modeling resource usage in planetary-scale shared infrastructures:
PlanetLab's case study. Comput. Netw., Vol. 55 (15), pp. 3394-3407
(2011). ISSN: 1389- 1286. Impact Factor: 1.176 (JCR 2010) External
regulation entity to improve load balancing Xavier Len, Tuan Anh
Trinh, and Leandro Navarro. Using economic regulation to prevent
resource congestion in large-scale shared infrastructures. Future
Generation Computer Systems, Vol. 26 (4), pp. 599 - 607 (2010).
ISSN: 0167-739X. Impact Factor: 2.365 (JCR 2010) Resource provider
as regulator to improve energy efficiency Xavier Len, and Leandro
Navarro. Limits of Energy Saving for the Allocation of Data Center
Resources to Networked Applications. In proceedings of the The 30th
IEEE International Mini-conference on Computer Communications (IEEE
INFOCOM'11), pp. 216-220 (2011), Shanghai, China. ISSN: 0743-166X.
Xavier Len, and Leandro Navarro. Stackelberg Game to Derive the
Limits of Energy Saving for the Allocation of Data Center
Resources. Future Generation Computer Systems, Vol. 29 (1), pp.
74-83 (2012). ISSN: 0167-739X. Impact Factor: 2.371 (JCR 2011) User
self-regulation for energy efficiency and dynamic allocation Xavier
Len and Leandro Navarro. Incentives for Dynamic and Energy-aware
Capacity Allocation for Multi- tenant Clusters. Submitted to
International Conference on the Economics of Grids, Clouds,
Systems, and Services. [P1] [Ch 5] [Ch 6] [Ch 7] [Ch 8] [P2] [P3]
[P4] [P5]
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4 Where is the problem?
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The Tragedy of the Commons 5 The benefits of exploitation of a
finite resource are perceived by individuals while the costs are
shared by the group
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Shared computational infrastructures 6
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7 Case study: Planetlab * Data from Codeen Host Status
03-08-2008 Consumption vs Contribution Cumulative Load of
nodes
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Computational markets as a solution 8 Good at matching demand
and supply Effectively summarize information through prices
User-oriented priorities when demand exceeds supply
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Gaps on current research 9 Mainly focused on utility
optimization User efficiency Profit optimizing Lack of control over
externalities Multiple system-wide objectives What about Load
balancing Energy-efficiency
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Problem statement 10 How to incorporate multiple system-wide
objectives into computational markets?
Some results 15 Same efficiency Better load balance Small loss
on fairness
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16 Conclusions Resource hot spots may be present Need a
mechanism to internalize this problem into the market Influence
users choice Resource taxes through 3 rd party regulation entity
Suitable in decentralized scenarios with no central control
Improves load balancing at the cost of fairness Same efficiency
Utility uniformity and Envy-freeness reduced
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[P3] [P4] [Ch 7] 17
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18 Sharing model Proportional share in datacenters x 1 = 1 x 2
= 2 x 3 = 1
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Problem Increasing energy costs 19 Power (Watts) Server
utilization Idle power usage Active power usage Good for Energy
Proportionality Still high idle power!
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20 Problem Increasing energy costs Given a specific user
demand, how many resources should the resource provider shut down
without reducing the QoS?
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Let users optimize utility 21 Node state (on/off) Minimum share
Minimum parallelism Proportional share model of competition
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and provider optimize profit by minimizing energy related costs
22 Node state (on/off) Cost of resource Price paid by users Every
user is satisfied Revenue cost model
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How to solve it? Stackelberg game 23 Resource Provider Leader /
Resource supply Regulator Selfish users Followers How many
resources to shutdown? Given k available resources, is every user
satisfied? User demand ? Sorted by power consumption (ascending)
Optimal # of resources found! Shutdown remaining resources
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Some results 24
Slide 25
25 Significant costs related to energy consumption Idle power
usage of IT equipment becomes a key factor Resource provider in
control of resource supply Stackelberg model of competition
Suitable when resource provider has full control over supply Upper
bound on energy savings Minimize switched on machines while
satisfying users requirements Conclusions
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[P5] [Ch 8] 26
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Fair share/Capacity scheduler Hadoop 27 Pool of resources (time
slots in Hadoop) 1/n Spare resources Fixed resource allocation
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Dynamic shares market 28 Pool of resources (time slots in
Hadoop) Capacity saving Spare resources that can Be shutdown Reused
by others
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29 Dynamic shares market Application A Application B
Application C Cluster Savings proportional to Time and share not
used No credits -> default to fair share Allow more than fair
share if enough credits If competition -> Proportional
share
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Some results 30
Slide 31
31 Issues we address No control over share guarantees Lack of
dynamic and elastic resource allocation No notion of energy-related
costs Provide incentives for user self-regulation Decentralize
decision making Dynamic and energy-aware resource allocation
Incentive compatible Conclusions
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32 Wrapping up Conclusions and Future work 32
Slide 33
33 Conclusions How to incorporate multiple system-wide
objectives into computational markets?
Slide 34
34 Future work What about other system-wide metrics? Reward the
social benefit of executions Control monopolies on federated
infrastructures Explore these mechanisms on real deployments Are
they feasible? Easy of use? Need for computer-aided decision
making?
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