=Paper=
{{Paper
|id=Vol-223/paper-30
|storemode=property
|title=Flexible Provisioning of Service Workflows
|pdfUrl=https://ceur-ws.org/Vol-223/14.pdf
|volume=Vol-223
|authors=Sebastian Stein (University of Southampton),Nicholas Jennings (University of Southampton),Terry Payne (University of Southampton)
|dblpUrl=https://dblp.org/rec/conf/eumas/SteinJP06
}}
==Flexible Provisioning of Service Workflows==
https://ceur-ws.org/Vol-223/14.pdf
Flexible Provisioning of Service Workflows
(Extended Abstract)1
Sebastian Stein Nicholas R. Jennings Terry R. Payne
University of Southampton, SO17 1BJ, United Kingdom
{ss04r,nrj,trp}@ecs.soton.ac.uk
1 Background
Computer systems are becoming increasingly complex, distributed and open in nature. In so doing,
they pose new challenges to contemporary software engineering techniques, as applications need to
deal with many widely distributed and dynamic resources. In this context, service-oriented com-
puting has gained popularity as an appropriate paradigm to build such systems. In this approach,
resources are offered as services, which are advertised to consumer applications using computer-
readable descriptions and then provisioned dynamically when needed, often as part of complex
workflows.
A defining feature of these distributed systems is that participants are usually heterogeneous,
self-interested agents that act autonomously in accordance with their own private goals. As such,
agents choose actions that maximise their own welfare, and so they cannot be assumed to honour
every request, to be available whenever they are needed or even to report their capabilities and
status truthfully. Additionally, network problems, competition for resources or even software bugs
exacerbate the potential for service failures. Thus, when interacting with service providing agents
and making real investments that depend on their performance (whether in terms of money, time
or other resources), dealing with service failures becomes a vital issue to consider.
Against this background, we believe that flexible service provisioning (i.e., selecting and allo-
cating service providers to specific tasks) is the key to handling and controlling service failures.
It allows the consumer to dynamically select service providers based on their performance charac-
teristics and provision replacement providers when services fail. Specifically, provisioning services
in the context of a workflow enables a consumer agent to identify particularly failure-prone tasks
and invest additional resources in them. To date, however, this provisioning process has received
comparatively little attention. In particular, the fact that a type of service might be offered by
multiple providers is seldom explored, and most research is concerned with finding and invoking
the first matching service that helps fulfil a given goal. Such a naı̈ve approach means that a single
service failure will result in the failure of the whole workflow, which is highly undesirable, especially
when success is critical to the interests of the consumer agent.
2 Flexible Provisioning
To address this issue, in our paper [1] we investigate the process of provisioning service workflows
in environments where service success is not generally guaranteed. We show that the current
approach of provisioning single providers is insufficient in such environments, and then advance the
state of the art by developing several strategies that deal proactively with unreliable providers. In
particular, we propose two key techniques:
Parallel Provisioning: To increase the probability of success, n agents that provide the same
service can be provisioned in parallel for a single task in the workflow. The task will then
succeed as long as at least one service completes successfully.
1 This is an extended abstract of [1].
Serial Provisioning: Providers can also be re-provisioned when it becomes obvious that they
have failed. As failure may not be communicated explicitly by the providers, this process
involves provisioning an initial set of agents, and then waiting for some time w, after which
a new set of agents is provisioned for the same task (and so on).
Now, the appropriate choice of n and w for each task depends on the performance characteristics
of the available services. For example, if services are cheap and unreliable, a high value for n might
be chosen. If services generally take a long time to complete, then a high w would reflect this.
Because finding the right level of parallelism and waiting times is non-trivial to perform man-
ually, and because it depends on the consumer’s valuation of the workflow and associated time-
constraints, we automate this choice by employing decision theory. To this end, we attach a utility
function to each workflow, in order to represent the value of completing the workflow after some
time t. As provisioning providers optimally (i.e., maximising the expected utility) is intractable,
we then use a hill-climbing algorithm that uses a heuristic estimation of the true expected utility
and explores a small subset of the decision space by iteratively modifying a candidate solution.
3 Results
In order to verify our approach, we also report our empirical results. These clearly demonstrate
the value of flexible service provisioning over current approaches. For example, Figure 1 (taken
from [1]) shows our provisioning strategies in environments with varying service uncertainty. In
particular, the naı̈ve strategy represents current approaches that rely on single providers, parallel(2)
and parallel(6) are strategies where the number of parallel providers (n) is fixed statically, and the
flexible strategy is our heuristic provisioning approach.
1000
naïve
800 parallel(2)
parallel(6)
flexible
Average Utility
600
400
200
0
−200
0 0.2 0.4 0.6 0.8 1
Failure Probability
Figure 1: Average utility in environments with increasing service failure probability.
The flexible strategy clearly dominates the others, and, in fact, we show that it successfully
completes over 90% of its workflows in most environments where current approaches complete
none. Furthermore, our results indicate that it achieves a 350% improvement in average utility
over the naı̈ve strategy.
References
[1] Sebastian Stein, Nicholas R. Jennings, and Terry R. Payne. Flexible provisioning of service
workflows. In Proceedings of 17th European Conference on Artificial Intelligence (ECAI-06),
Riva del Garda, Italy, pages 295–299. IOS Press, 2006.