Utility Driven Service Routing over Large Scale Infrastructures

Utility Driven Service Routing over Large Scale Infrastructures

Pablo Chacin

Polytechnic University of Catalonia (UPC), Spain

Authors• Pablo Chacin, Polytechnic University of Catalonia, Spain (UPC)• Leandro Navarro, UPC• Pedro Garcia López, Rovira i Virgili University, Spain

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Key Points

• UDON is an Utility Driven Overlay Network for routing service requests to service instances that match some QoS requirements

• It is aimed for highly dynamic large-scale shared infrastructures.

• Combines an application provided utility function to express QoS with an epidemic protocol to disseminate the information that supports the routing

• Experimental analysis shows that UDON allocates requests meeting QoS with a high probability and low overhead; it is scalable, robust and adapts well to a wide range of conditions.

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Outline

• Defining the problem context• Design principles• Experimental evaluation• Conclusions

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Internet of Services

Source: Schroth, C., Janner, T.: Web 2.0 and soa: Converging concepts enabling the internet of services. IT Professional 9(3), 36–41 (May/June 2007)

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Service Deployment

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Challenges

• Non dedicated Servers– The QoS a server can offer is hard to predict

• Fluctuations in the demand

• Different QoS requirements for different users– e.g. free/paid; bronze/silver/gold

• Large scale

• Number of instances may vary – Activations/deactivations due to fluctuations on the

demand

– Failures

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Guiding principles

• Decentralized decisions using local information

– No global view; no single point of failure; more scalable and adaptable

• Representation of QoS as an Utility Function

– Compact representation

– Facilitate comparisons despite heterogeneity

• Model-less adaptation

– No need to elicit or learn a performance model for the systems

– If information is not exact, rationality may not help.

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System Model

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Utility Function• In economics, utility is a

measure of relative satisfaction

• Summarizes multiple attributes into a single scalar value

– F(a1,..an) → [0,1]

• Facilitates comparison, allow private evaluations

Cobb-Douglas utility functionU(t,c) = t(ac(1-a) t = execution timec = cost

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Epidemic Overlay• Simple maintenance algorithm

– Each node has a local view of the state of a set of neighbors

– Periodically choses some neighbors and sends its local view + own state

– Each node merges its local view with the received views keeping the most recently updated entries

• Disseminates information with low overhead

• Highly scalable and resilient

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Randomized Greedy Utility Routing

• Multi-hop routing using local information

– On each hop, ranks neighbors based on its (potentially outdated) utility

– Forward to the node with a probability based on ranking

• Simple concept. Allows multiple heuristics for ranking (evaluation is an ongoing work)

Image source: physics.orgGreedy Routing Enables Network Navigation Without a 'Map'http://www.physorg.com/news154093231.html

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Evaluation

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Simulation Model

• Network topology is abstracted

– One single cluster, 1000's of servers.

– Constant, negligible delays

• Utility Function simulated as a Random Process

– Make evaluation more general, not tied to a particular utility definition

– Evaluate the effect of different parameters

• Compared with other overlays of the same family

– Random: no organization (baseline)

– Gradient: keep instances with similar QoS close

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The Simulation of the Utility Function

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Metrics

• Overlay (information dissemination) – Age: how old is the information in the

local view (average)– Staleness: how accurate is the local view

with respect of real current information

• Routing– Satisfied demand: how effective and

reliable is the allocation (% of success)– Hops: how efficient

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Overlay

Maintains “fresh” information

Minimizes staleness

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Performance

Tolerance: maximum allowed difference between required QoS and node's utility:~ 1.0 any node with a higher utility matches~ 0.0 only node with the exact demanded utility matches

Allocates requests with high probability, and low number or hops, even under very demanding search criteria (low tolerance)

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Performance looking for scarce resources

Allocates requests even when target nodes are scarce.

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Churn

Performance “gracefully” degrades under high churn

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Variation in Utility

Allocates requests even under highly fluctuating conditions.

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Sensitivity to Operational Parameters

Optimal setup demands low communication overhead

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Discussion

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Conclusions

• Simple, principled solution for routing requests over large-scale cluster-based web services on shared infrastructures

• UDON meets requirements on scenarios of interest and shows desirable properties– Effective

– Low overhead

– Scalable

– Very adaptable

– Robust

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(Near) Future work

• Apply UDON to A concrete scenario– Simulated cluster based web services

– Use concrete utility functions

• Evaluate alternative routing heuristics

• Propagate information based on usefulness: see which QoS are more demanded and propagate information of nodes that offer it with higher probability

• Consider locality when selecting neighbors to adapt to wide area distributed clusters (multi-site)

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Questions? . . . Thanks.

pchacin@ac.upc.edu

ICSOC-ServiceWave 2009