=Paper=
{{Paper
|id=Vol-548/paper-8
|storemode=property
|title=Ensuring Cost-Optimal SLA Conformance for Composite Service Providers
|pdfUrl=https://ceur-ws.org/Vol-548/paper8.pdf
|volume=Vol-548
}}
==Ensuring Cost-Optimal SLA Conformance for Composite Service Providers==
https://ceur-ws.org/Vol-548/paper8.pdf
Ensuring Cost-Optimal SLA Conformance for
Composite Service Providers
Philipp Leitner
Supervised by: Schahram Dustdar
Distributed Systems Group
Vienna University of Technology
Argentinierstrasse 8/184-1
A-1040, Vienna, Austria
lastname@infosys.tuwien.ac.at
Abstract. For providers of composite services, service level agreements
(SLAs) provide a means to guarantee a certain service quality to prospec-
tive customers. Usually, violating SLAs is associated with costs. However,
the means necessary to ensure SLA conformance also generate costs. Of-
tentimes, it is therefore optimal from a business perspective to violate
certain SLAs sometimes, instead of trying for the high (and expensive)
road of always satisfying each one. In this paper we will sketch a frame-
work for the prediction of SLA violations and for determining whether
an adaptation of the process makes sense economically. If this is the case
adaptation actions are triggered, which adapt the composition on either
on instance, structural or environmental level. The ultimate goal is to
implement a closed-loop system, which self-optimizes the costs resulting
from SLA violations.
1 Introduction
Service-oriented architectures are at their core about the integration of systems.
This new paradigm is used by Software-as-a-Service providers, which deliver
basic IT functionality such as customer relations management or business intel-
ligence as composite services. One important notion for the seamless integration
of such outsourced IT services are agreements about the quality that these ser-
vices need to provide (QoS), typically defined within legally binding Service
Level Agreements (SLAs). SLAs contain Service Level Objectives (SLOs), con-
crete numerical QoS objectives which the service needs to fulfill. If SLOs are
violated, agreed upon consequences (usually taking the form of penalty pay-
ments) go into effect. However, fulfilling SLAs can also lead to costs for the
service provider (e.g., because the composite service provider needs to use more
expensive services itself, or because of the costs inherent to optimizing its service
composition). It is therefore not trivial for the provider to decide to what extend
the service’s SLAs should be fulfilled, or which SLAs should (temporarily) be
violated for economical reasons. Even more, these decisions should optimally be
automated, to allow for fast reactions to changes in the business environment.
In this overview paper we will present a high-level framework for optimiz-
ing adaptations of service compositions with regards to SLA violations. We use
techniques from the area of machine learning [1] to construct models allowing
the system to predict SLA violations at runtime and decide which adaptation
actions may be used to improve overall performance. Adaptation can happen
on instance level (for one instance only), on structural level (for all future in-
stances), or on environmental level (e.g., migrating the composition engine to
a machine with better hardware). An optimizer component decides if applying
these changes makes sense economically (i.e., whether the costs of violating the
SLAs are bigger than the adaptation costs). If this is the case the respective
actions are applied in an automated way. At its core, this system is a closed-
loop self-optimizing system [2], with the target of minimizing the total costs of
adaptations and SLA violations for the service provider.
The work described in this paper is currently ongoing. However, some impor-
tant fundamental work has already been published. In [3] earlier work regarding
the monitoring of QoS of Web services is presented. Our work on VRESCo [4]
forms the basis for the proposal presented here, providing core services such as
support for dynamic rebinding. Finally, in [5] we have presented first results re-
garding the identification of factors influence of business process performance,
which is related to the generation of prediction models. The remaining PhD re-
search will be led by two key research questions: (1) How can the factors that
influence the performance of a composition be identified, modeled and analyzed,
in order to enable prediction of SLA violations at runtime, and trigger adapta-
tions to prevent these violations? (2) How can the tradeoff between preventing
violations and the costs of doing so be best formalized, especially considering
that many adaptation actions may be interleaved and combined? Even though
first steps exist (see Section 3), to the best of our knowledge, these questions
have not been answered sufficiently in literature so far. We plan to validate the
outcomes of the thesis using a case study, by showcasing how self-optimization
can prevent SLA violations both short- and long-term, and comparing the total
costs for the service provider with and without the proposed system. We will
consider our work successful if using our system leads to significant financial
benefits for the provider, while at the same time reducing SLA violations (even
if not all violations are prevented).
The remainder of the paper will be structured as follows. Section 2 contains
the main contribution of the paper, a description of a system for cost-optimal
adaptation of composite services. In Section 3 we give a brief overview over
relevant related work. Finally, Section 4 will conclude the paper.
2 Approach Overview
A high-level overview of our approach is depicted in Figure 1. The system imple-
ments an optimization cycle in the Autonomic Computing [2] sense, i.e., it follows
the basic steps Monitor (monitoring the service composition, i.e., measuring QoS
values and process instance data), Analyze (generating prediction models), Plan
(evaluating based on the generated models, the available adaptation actions and
the current SLAs of the provider if there are possible optimizations for the com-
position), and Execute (applying these optimizations). The managed element is
the Service Composition, while four other components (Composition Monitor,
Composition Analyzer, Cost-Based Optimizer and Adaptation Executor ) imple-
ment the autonomic manager.
1. 2.
Monitor Analyze
Service
Composition Composition Composition
Monitor Analyzer
SLA
Database
Adaptation
Metrics
Actions
Database
Database
3x + 4y - 2z
Adaptation Cost-Based
Executor Optimizer
4. 3.
Execute Plan
Fig. 1: A Self-Optimizing System for Cost-Optimal SLA Conformance
Our approach is based on the following assumptions: (1) service providers
have explicit SLA(s) with their customers, including concrete numerical target
values for SLOs and penalty payments for SLA violations; these payments can be
staged, i.e., more severe SLA violations can lead to more severe penalties, and (2)
there is a database of possible adaptations available, including the costs of these
adaptations; costs can be both one-time costs (such as a downtime) or continually
(such as increased costs for using more expensive services). The implementation
of this database of possible adaptation actions needs to be supported by strong
tools, which allow for the generation of the most important actions in a semi-
automated way. Costs of adaptation actions can be derived using simulation or
analysis of historical data. In the following, we are assuming an autonomous
system, however, in some situations the role of the optimizer or executor can
also be adopted by a human.
Composition Monitor: The foundation of all work presented here is the ability
to gather accurate runtime data. This includes: (1) QoS metrics such as response
time or availability of the services used, (2) runtime payload data, such as cus-
tomer identifiers or ordered items, and (3) technical parameters of the execution
environment, such as the availability of the composition engine, or the CPU load
of the machine running the composition. The Composition Monitor component
is used to collect this monitoring data from various sources (e.g., an external
QoS monitor [3] or technologies such as Windows Performance Counters1 ), con-
solidate it and store it to a metrics database.
Composition Analyzer: The data collected by the Composition Monitor is then
used to generate prediction models for SLA violations. Prediction models are
used to estimate at runtime if a given running instance is going to violate one or
more of its SLAs. They are associated with checkpoints in the composition model,
at which the prediction is done. Simply put, a prediction model is a function
which uses all execution data which is already available at the checkpoint and, if
possible, estimations for all missing data, and produces a numerical estimation
for every target value in the provider’s SLAs as output. In earlier work we have
used simple decision trees to implement such models [5], however for future work
we investigate the usage of multi-layer perceptrons instead to improve accuracy
of predictions.
Cost-Based Optimizer: The Cost-Based Optimizer can be seen as the core of the
system. This component needs access to all SLAs, as well as a database of pos-
sible adaptation actions. The optimizer has to fulfill two important tasks in the
system. Firstly, it uses the prediction models generated before to predict con-
crete QoS values for every running instance and compares the predicted values
with the respective SLOs. If SLA violations would occur it checks the Adap-
tation Actions database for any possible action to prevent the violation, and
applies them if it is cost-efficient to do so. This involves solving an optimization
problem to decide which combination of actions both prevents most SLA viola-
tions and is cheapest to implement. Secondly, if more than a certain threshold
of SLA violations (in a given time frame) have been monitored, the component
tries to improve the composition itself, i.e., it tries to optimize the composition
for every future instance. The main difference is that on composition level more
possible adaptation actions exist (mainly because adaptation on this level is less
time-critical, so that adaptations which involve e.g., a system downtime are also
feasible).
Adaptation Executor: The Adaptation Executor is responsible for applying the
adaptation actions as planned by the Cost-Based Optimizer. Generally, we con-
sider the classes of adaptation actions (action classes) depicted in Figure 2.
As discussed before, adaptation can happen either on instance (level 1, i.e.,
adaptations which affect only a single instance) or structural level (level 2, i.e.,
adaptations which affect all future instances), and can consist of rebinding base
services (R*, i.e., switching from one used service to another), restructuring the
composition (S*, e.g., parallelizing some parts of the composition) or adapting
the execution environment (E, e.g., upgrading the virtual machine running ser-
vice composition). Generally, actions of type E always affect all future instances,
and are therefore only applicable on level 2.
1
http://msdn.microsoft.com/en-us/library/aa373083(VS.85).aspx
Adapt Adapt Adapt
Service Composition Execution
Binding Structure Environment
R1 S1
Instance
Level instance-level instance-level
rebinding structural change
E
R2 S2
Composition execution
Level composition-level composition-level environment
rebinding structural change change
Fig. 2: Classes of Adaptation Actions
Obviously, the concrete execution of adaptation actions depends greatly on
the action class. For applying adaptations of type R* we can use the means pro-
vided by the execution environment (such as dynamic rebinding as discussed in
earlier work [4]). For S* more complex means are necessary, for instance adap-
tation techniques such as AO4BPEL [6], or the more recent BPEL’n’Aspects [7]
approach. Currently, changes of class E are mostly executed manually. However,
for some changes of this class automation has been made possible by the recent
rise of Cloud Computing. If, for instance, the execution environment is hosted
in the Amazon Elastic Compute Cloud2 it is possible to automatically adapt
(some) parameters of the hosting environment via the Amazon S2 API.
3 Related Work
We will now briefly discuss some key related work. QoS monitoring of atomic
services is discussed in [3, 8]. Our Composition Monitor will partially be based
on these results. Monitoring of composition instance data has been discussed
in [9]. However, these works do not explicitly cover SLA monitoring, which is
the scope of [10]. The authors use an event-based approach to monitor QoS,
which is in line with the ideas we have used in [5]. This work also pioneers the
idea of SLA impact analysis, which is related to the tasks that our Composition
Analyzer has to fulfill. Another basic building block of this component is the
work presented in [11], which discusses the prediction of QoS (again using an
event-based model). Self-adaptation of compositions, another core topic in our
work, is discussed in [12]. The MASC system presented there adapts itself in
order to recover from failures and improve reliability, however, this system does
not try to predict problems and prevent them proactively. Optimizing service
compositions with regards to overall QoS is an often-discussed topic, with some
seminal work dating back to 2004 [13]. In contrast to this approaches, in our
system optimization is done with regard to specific SLOs, which are currently
violated, and taking into account the tradeoff between the costs of SLA violations
and the costs of adaptation.
2
http://aws.amazon.com/ec2/
4 Conclusions
In this paper we have sketched the architecture of a closed-loop system, which
autonomously optimizes service compositions with regard to SLA violations,
taking into account the costs caused by the adaptation. Our next steps will be the
implementation of an first end-to-end system, which includes prototypes for all
four main components of the system. Furthermore, we will define a preliminary
model for capturing the costs of adaptation actions.
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