Pablo Chacin (Polytechnic University of Catalonia, Spain): 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

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.

13-15 December 2010 ServiceWave 2010

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


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)

Service Deployment


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


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.


System Model


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 function
U(t,c) = t(ac(1-a)
t = execution time
c = cost

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


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 Image source: physics.org
ranking (evaluation is an Greedy Routing Enables Network Navigation
Without a 'Map'
ongoing work) http://www.physorg.com/news154093231.html


Evaluation


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


The Simulation of the Utility
Function


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

Overlay
Maintains “fresh”
information

Minimizes
staleness


Performance

Tolerance: maximum allowed difference
between required QoS and node's utility: Allocates requests with high
~ 1.0 any node with a higher utility matches probability, and low number or
~ 0.0 only node with the exact demanded
utility matches hops, even under very
demanding search criteria (low
tolerance)

Performance looking for
scarce resources
Allocates requests
even when target
nodes are scarce.


Churn

Performance
“gracefully” degrades
under high churn


Variation in Utility

Allocates requests even under
highly fluctuating conditions.


Sensitivity to Operational
Parameters

Optimal setup demands low
communication overhead


Discussion


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


(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)

Questions? . . . Thanks.

pchacin@ac.upc.edu


Pablo Chacin (Polytechnic University of Catalonia, Spain): Utility Driven Service Routing over Large Scale Infrastructures

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