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Active Energy-Aware Management of
Business-Process Based Applications
Position Paper
Danilo Ardagna, Cinzia Cappiello, Marco Lovera,
Barbara Pernici, Mara Tanelli
Politecnico di Milano
Agenda
Motivations
Energy-Aware Resource Allocation
Framework
Process, Infrastructure, and Control
Layers
Preliminary results
Conclusions and Future Work
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Energy Management in Service Centers
■ Energy consumption
■ 2% of CO2 emission
■ By 2012 energy costs will be 40% of TCO
■ Related costs: cooling, UPS, …
■ QoS guarantees and workload variability
■ Dynamic resource managment
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Motivations
Current work in sustainable and energy aware
computing suggests to provide services with a trade-off
between performance and energy consumption
Service centers energy efficiency efforts
■ Servers consolidation
■ Servers virtualization
Storage remains a gaping hole in the enterprise service
center: the same principles that govern server energy
savings should be applied to the storage sub-system as
well
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Our work
Develop novel energy-aware resource allocation
mechanisms and policies for SOA, and business
process-based applications via an interdisciplinary
approach
Goal: provide services with QoS guarantees, while
minimizing the energy consumption of the computing
infrastructure
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Active Energy Aware Framework
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Active Energy Aware Framework
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Process Layer

Manages business process end user applications
In advanced SOA systems, complex applications are
described as abstract business processes which are
executed by invoking a number of available Web
services
End users can specify different preferences and
constraints and service selection can be performed by
dynamically identifying the best set of services available
at run time
Web service components characterized by QoS profiles
and energy cost
Maximization of the QoS for the end User
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Process Layer

Web service selection results in an optimization problem
whose goal is to optimize a single process instance
Performance issues are usually not considered and
energy consumption has always been neglected
QoS optimization does not analyze the process
efficiency in terms of accesses and management of
business objects
Data deduplication techniques can be applied in order to
identify and merge different copies of the same object
Green IT calls for a new approach to data management
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Process Layer
In [1], we have proposed an optimization
technique for QoS maximization based on mixed
integer linear programming
Approach demonstrated to be efficient under
stringent constraints and for large processes
instances
In current work we are extending the solution in
order to include explicitly energy issues and
object replica management in the QoS
evaluation

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Infrastructure Layer

Focuses on workload variations and on the trade-off
between the performance of Web service components
and energy consumption
Web service components invoked by business
processes are mapped to multi-tier server applications
which are currently executed by independent Virtual
Machines
Each VM is usually dedicated to serve a single
application
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Autonomic self-managing techniques are currently
implemented by network controllers which can establish
■ Application placement: The set of applications (VMs) executed
by each server
■ Load balancing: The request volumes at various servers
■ Capacity Allocation: The capacity devoted to the execution of
each application (VM) at each server
■ Server provisioning: Decide to turn on or off servers depending
on the system load
■ Frequency scaling: Reduce the frequency of operation of servers

Goal: maximize the SLA profits (including revenues and
penalties), while balancing the cost of using resources
(including energy and air conditioning)
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In [2] we have designed resource allocation techniques
for the management of multi-tier virtualized systems
Allocation policies provide a joint solution to the server
provisioning, frequency scaling, VMs placement, load
balancing and capacity allocations problems
The joint problem has been formalized as a mixed
integer non linear programming problem
The problem is NP-hard and the inclusion of energy
costs in the objective function keeps its solution very
challenging
Heuristic approach based on local-search
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Energy efficiency in storage can be achieved by
adopting data deduplication also at this layer
Basic idea is to store only data changes on storage
devices, while redundant data is replaced with a pointer
to the unique data copy
Data deduplication can be also applied for archiving
purposes focusing on high level application requirements
Loss of information can be avoided by detecting and
preserving important objects
Data quality techniques for the identification of the only
relevant copy to be preserved
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Control layer

Differentiation between the Infrastructure and
the Control layers characterized by different time
scales:
■ Server provisioning and VM placement decisions
taken about every half an hour
■ Load balancing, capacity allocation, and frequency
scaling problems imply a relatively low computation
overhead

Infrastructure models based on the assumption
that the overall system is at a steady state and
cannot accurately model system transients
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Control layer

Aims at tackling workload variations and adjusting the
system configuration within a very short time frame (e.g.,
every minute)
Adoption of dynamical models which can accurately
represent system transients under varying workload
conditions and genuine control-theoretic approaches for
the design of server controllers
Control layer is viewed as a feedback loop, where the
SLA objectives are translated into set-points for the
response time of the servers and tracking performance is
traded-off with energy savings
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Control layer

In [14] we identified a control-oriented dynamic model of
an application server based on the Linear Parameter
Varying (LPV) framework
LPV models are able of capturing system behavior at a
very fine-grained time resolution, with an accuracy
suitable for control purposes
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Need for an Integrated Approach
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Infrastructure Layer Preliminary Results
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Infrastructure Layer Preliminary Results
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Control Layer Preliminary Results
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Conclusions and Future Work

Climate debate and sustainable growth concern over
energy use will strive green computing in the Service
area research agenda
We have provided solutions able to determine QoS and
energy trade-off at the individual layers of our framework
Ongoing work is focusing on the analysis of the different
time scales and the interrelations which characterize the
resource managers working at the different layers
Exploit information from the lower layers to quantitatively
estimate the energy consumption required for business
processes and component Web services execution
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References

[1] D. Ardagna and B. Pernici. Adaptive Service Composition in Flexible
Processes. IEEE Transactions on Software Engineering, 33(6):369–384,
June 2007
[2] D. Ardagna, M. Trubian, and L. Zhang. Energy-Aware Autonomic
Resource Allocation in Multi-tier Virtualized Environments. Politecnico di
Milano, Dipartimento di Elettronica e Informazione Technical report number
2008.13, July 2008
[3] D. Ardagna, M. Trubian, and L. Zhang. SLA based resource allocation
policies in autonomic environments. Journal of Parallel and Distributed
Computing, 67(3):259–270, 2007
[14] M. Tanelli, D. Ardagna, and M. Lovera. LPV model identification for
power management of web services. In IEEE Multi-conference on Systems
and Control, 2008
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