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Utility Driven Service Routing over Large Scale Infrastructures
Pablo Chacin
Polytechnic University of Catalonia (UPC), Spain
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Authors• Pablo Chacin, Polytechnic University of Catalonia, Spain (UPC)• Leandro Navarro, UPC• Pedro Garcia López, Rovira i Virgili University, Spain
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13-15 December 2010 ServiceWave 2010
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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ICSOC-ServiceWave 2009