=Paper=
{{Paper
|id=Vol-1360/paper11
|storemode=property
|title=Privacy-Aware Scheduling for Inter-Organizational Processes
|pdfUrl=https://ceur-ws.org/Vol-1360/paper11.pdf
|volume=Vol-1360
|dblpUrl=https://dblp.org/rec/conf/zeus/Hochreiner15
}}
==Privacy-Aware Scheduling for Inter-Organizational Processes==
https://ceur-ws.org/Vol-1360/paper11.pdf
Privacy-Aware Scheduling for
Inter-Organizational Processes
Christoph Hochreiner
Distributed Systems Group, Vienna University of Technology, Austria
c.hochreiner@infosys.tuwien.ac.at
Abstract Due to the increasing specialization of companies in a global-
ized world, inter-organizational process enactments have become increas-
ingly relevant in recent years. Nevertheless there are hardly any scheduling
approaches that meet the requirements of these inter-organizational pro-
cesses, especially in terms of privacy aspects. In this paper we present a
privacy-aware scheduling approach for hybrid clouds, which represents
a vital starting point to design a holistic execution environment for
inter-organizational process enactments.
Keywords: Cloud Computing, Business Process Management, Hybrid Clouds
1 Introduction
In the last couple of years Business Process Management (BPM) has become a well-
adopted approach for companies to provide value-added services to customers [8].
Business processes are composed of software- as well as human-based services and
their design ranges from simple sequences to complex structures involving loops,
splits or choices [11]. The process enactment is conducted by a Business Process
Management System (BPMS) [12] which is considered as a generic software
system that manages operational business processes [2]. The management of
operational business processes covers the assignment of the different process steps
to the designated services which are required to realize a process enactment and
schedules their instantiation. Apart from the process scheduling, a BPMS may also
manage the provisioning of computational resources to instantiate the software-
based services. Since BPMS are used to execute business processes, the BPMS as
well as the services are often deployed on fixed resources within the company’s
premises, where the companies may combine their computational resources to
implement a resource pool, i.e. a private cloud [7]. The most important reason for
this internal hosting solution are security and privacy restrictions, since services
may deal with sensitive information, e.g., health data or execute algorithms that
are considered as trade secrets [10].
This paper proposes a privacy-aware scheduling approach for hybrid cloud
environments. To obtain an optimal and privacy-aware scheduling respectively re-
source provisioning approach, we extend the Service Instance Placement Problem
(SIPP) [4] which applies Mixed Integer Linear Programming (MILP).
T. S. Heinze, T. M. Prinz (Eds.): Services and their Composition, 7th Central European Workshop,
ZEUS 2015, Jena, Germany, 19-20 February 2015, Proceedings – published at http://ceur-ws.org
64 Christoph Hochreiner
The remainder of this paper is structured as follows: In Sect. 2 we state the
motivation for our work and discuss some preliminaries in Sect. 3. We further
present our privacy-aware scheduling approach in Sect. 4 and Sect. 5 concludes
the paper with an outlook on our future work.
2 Motivation
Business process enactments are usually triggered by process requests. These
process requests are issued by external events, e.g., customer interactions, which
lead to alternating amounts of business process requests respectively changing
resource requirements. In peak-times, when external events issue an extraordinary
amount of process requests, a BPMS may run into an underprovisioning scenario,
since there are not enough resources to enact the process requests according to
their Service Level Agreements (SLAs) [9]. This leads to a lower Quality of Service
(QoS), e.g., longer response times and SLA violations may also trigger penalty
cost that increase the overall cost for process enactment. Besides the peak-times,
a system with fixed resources is also likely to run into overprovisioning scenarios,
since the computational resources will not be used adequately. This leads to
economically inefficient cost structures for the companies.
Public clouds, e.g., Amazon EC2, offer a promising solution to the resource
usage challenges for varying process requests. A cloud-aware BPMS is able to
obtain the required resources on demand in an utility like fashion. This enables
the BPMS to obtain resource elasticity by scaling the computational resources up
and down, based on the changing requirements. Measured services further allow an
exact billing of the computational resources based on the actual resource usage [7].
This elastic resource provisioning strategy avoids underprovisioning scenarios,
since the public cloud provides enough resources to cover the peak-requirements.
A cloud environment also avoids overprovisioning scenarios, because not required
resources can be released as soon as they are not needed any more.
Besides the resource allocation there are also other challenges for BPMS,
like privacy issues for service instantiations of inter-organizational processes, i.e.,
service choreographies. Inter-organizational processes are structured similarly to
business processes. The major difference is that their process steps are assigned
to software services which are provided by different companies instead of only one.
Therefore software services for inter-organizational processes can be executed
on a community cloud [7]. Nevertheless this common execution environment
is not acceptable for some software services due to privacy restrictions. The
most promising approach to tackle these issues is the creation of a hybrid cloud
which consists of a community cloud and dedicated private clouds for each
company [3]. Although resource scheduling for hybrid clouds already raised some
attention in terms of scheduling [1] as well as privacy aware deployments [13],
there are surprisingly little efforts towards privacy-aware scheduling approaches
for BPMSs [6].
Privacy-Aware Scheduling for Inter-Organizational Processes 65
Hybrid Cloud
Community Cloud
Common VM
BPMS VM VM
Data VM VM VM
VM VM VM VM
Private Cloud 2 Private Cloud 3
VM Sensitive VM Sensitive
VM VM VM VM
VM VM VM Data VM VM VM Data
Figure 1: Cloud Landscape
3 Preliminaries
In our previous work we presented the Service Instance Placement Problem
(SIPP) [4], which provides a cost-optimized scheduling and resource provisioning
plan for multiple parallel process enactments. SIPP represents a multi-objective
scheduling strategy which is presented in Sect. 4.1. Up to now the SIPP only
considers a single cloud for process enactments. Therefore it does not consider
any security nor privacy related aspects which are relevant for process enactments
in hybrid cloud environments. Before we describe the privacy related concepts in
detail in Sect. 4.2, we define some preliminaries.
The execution environment for inter-organizational processes consists of a a
community cloud and dedicated private clouds for privacy sensitive services, as
illustrated in Fig. 1. The community cloud hosts all non privacy sensitive services
as well as the BPMS. The BPMS schedules process steps, provisions resources
for the software-based services on the community cloud and also triggers the
deployment of the privacy sensitive services in the dedicated private clouds, based
on the privacy restrictions issued for the services. These restrictions are described
in detail in Sect. 4.2. In terms of computational resources, we assume that the
private clouds offer a limited amount of computational resources, which are only
sufficient to run the privacy sensitive services whereas the community cloud offers
theoretically unlimited resources.
The inter-organizational processes are composed of multiple process steps that
represent the software-based services provided by the participating companies.
Fig. 2 represents an exemplary inter-organizational process, which shows the
collaboration among 3 companies. Step 3 and 5 are annotated as privacy sensitive
and must only be executed in the dedicated private clouds, whereas all other steps
can be executed in the community cloud. To execute a process step, the BPMS
triggers the deployment of the software-based service on a Virtual Machine (VM)
either on the community cloud or on a private cloud. This deployment results in
a service instance that can be invoked by the BPMS to execute the service and
therefore execute the process step.
66 Christoph Hochreiner
Company 1
Step 1 privacy Step 6
sensitive
Company 2
Step 2 Step 3
Company 3
Step 4 Step 5
Figure 2: Inter-organizational Process Incorporating 3 Companies
4 Privacy-Aware Scheduling
4.1 Service Instance Placement Problem
The SIPP is represented by a set of different constraints and equations, which are
described in detail in [4]. In Eq. 1 the objective for the SIPP optimization model,
i.e., the minimization of the overall execution cost, is shown. This objective
comprises four terms, where the first term represents the overall leasing cost of
the computational resources by summing up the amount of leased VMs γ(v,t)
multiplied by their cost cv . The second term shows the penalty cost, which arise,
if a process is not finished within the given time. Hereby it sums up all delayed
process instances epip and multiplies them with predefined penalty cost cpip . In
order to keep the overall cost as low as possible, the optimization model penalizes
idle resources (CPU (fkCv ) and RAM (fkRv )) which are multiplied by the constants
ωfC and ωfR . The last term is designed to prioritize process steps x(jip ,kv ,t) , so
that steps with a closer deadline DLip are executed first.
X X X X X
min cv · γ(v,t) + cpip · epip + (ωfC · fkCv + ωfR · fkRv )
v∈V p∈P ip ∈Ip v∈V kv ∈Kv
X X X 1 (1)
− x(jip ,kv ,t)
DLip − τt
p∈P ip ∈Ip jip ∈Ji∗
p
4.2 Privacy Extensions
Since SIPP is designed for a single cloud, we introduced additional constraints and
additional SLA policies to enable the enactment of inter-organizational processes
while respecting the privacy constraints of the services.
We extended the set of VM types to distinguish between different deployment
locations with their different privacy policies.
[
V = Vloc (2)
loc∈Loc
[
K= Kloc (3)
loc∈Loc
Privacy-Aware Scheduling for Inter-Organizational Processes 67
This differentiation is possible by introducing the identifier loc (loc ∈ Loc),
which represents the type of the cloud, e.g., community cloud or 1 of the private
clouds. The new set of available VMs is then defined by the union of all VMs,
which can be instantiated in the different clouds (Eq. 2). Analogously we also
extended the set of all currently instantiated VMs (Eq. 3).
In terms of privacy restrictions, there are 2 specification possibilities to restrict
the execution of services regarding the type of the cloud. The first approach
is blacklisting: the SLA lists for every process all services, which must not be
executed on specific clouds. The major downside of this approach is that the
SLA needs to be updated when additional clouds are added.
The alternative approach is whitelisting, i.e., the SLA for each process lists all
service instantiation possibilities in the different clouds. Since this SLA pursues
a defensive permission approach, there is no need to update the SLA in contrast
to the blacklisting approach, when the cloud environment grows by additional
private or community clouds. Eq. 4 shows an exemplary SLA for the process
presented in Fig. 2.
communityCloud (Service 1, Service 2, Service 4)
SLAP = privateCloud1 (Service 3) (4)
privateCloud2 (Service 5)
Based on this SLA for services, a MILP-solver for the optimization problem is
able to generate the instantiation possibilities according to Eq. 5. The constraints
in Eq. 5 evaluate whether a specific process step jip can be instantiated on a
specific VM kvloc by querying whether the process step is listed on the whitelist
for the given cloud loc. If the SLA does not explicitly allow the instantiation
of the process step on the specific VM, the constraint rules out the deployment
option. Otherwise the MILP-solver decides based on other constraints whether the
service is deployed on the specific VM (1) or not (0) as stated in the alternative
branch of the constraint.
(
0 , if jip ∈
/ SLAPloc , loc ∈ Loc
x(jip ,kvloc ,t) = (5)
{0, 1} , else
5 Outlook
In this paper we focused on the formalization of privacy constraints for the
deployment and enactment of inter-organizational processes in hybrid cloud
environments. Although these privacy extensions are relevant for process en-
actment, they only represent a small step towards a holistic process scheduling
and resource allocation approach for inter-organizational processes in hybrid
cloud environments. Other relevant topics are data transfer aspects among the
different clouds, which become increasingly relevant for big data applications or a
cost-efficient resource allocation across the cloud environment. In our future work
we will evaluate the proposed privacy constraints in a hybrid cloud environment
68 Christoph Hochreiner
based on the Vienna Platform for Elastic Processes (ViePEP) [5]. Here we plan
to evaluate our approach against other privacy ensuring methods, e.g., encryption
of privacy-sensitive data in terms of performance and cost-efficiency. Further we
will also investigate other areas, like data transfer aspects or pricing policies for
hybrid clouds to enable the enactment of inter-organizational processes in an
economically efficient manner.
Acknowledgments This paper is partially supported by TU Vienna research
funds and supported by the European Union within the SIMPLI-CITY FP7-ICT
project (Grant agreement no. 318201).
References
1. van den Bossche, R., Vanmechelen, K., Broeckhove, J.: Cost-optimal scheduling in
hybrid iaas clouds for deadline constrained workloads. In: 2010 IEEE 3rd Interna-
tional Conference on Cloud Computing (CLOUD). pp. 228–235. IEEE (2010)
2. van Der Aalst, W.M., Ter Hofstede, A.H., Weske, M.: Business process management:
A survey. In: Business process management, pp. 1–12. Springer (2003)
3. Goyal, P.: Enterprise usability of cloud computing environments: issues and chal-
lenges. In: 2010 19th IEEE International Workshop on Enabling Technologies:
Infrastructures for Collaborative Enterprises (WETICE). pp. 54–59. IEEE (2010)
4. Hoenisch, P., Schuller, D., Hochreiner, C., Schulte, S., Dustdar, S.: Elastic process
optimization - the service instance placement problem. Tech. Rep. TUV-1841-
2014-01, Distributed Systems Group, Vienna University of Technology (October
2014)
5. Hoenisch, P., Schulte, S., Dustdar, S., Venugopal, S.: Self-Adaptive Resource Allo-
cation for Elastic Process Execution. In: 6th International Conference on Cloud
Computing (CLOUD 2013). pp. 220–227. IEEE (2013)
6. Huang, Z., van der Aalst, W.M.P., Lu, X., Duan, H.: Reinforcement learning based
resource allocation in business process management. Data & Knowledge Engineering
70(1), 127–145 (2011)
7. Mell, P., Grance, T.: The NIST Definition of Cloud Computing. Recommendations
of the National Institute of Standards and Technology (2011)
8. Rosemann, M., vom Brocke, J.: The Six Core Elements of Business Process Man-
agement. In: Handbook on Business Process Management 1, pp. 107–122. Springer
(2010)
9. Schulte, S., Janiesch, C., Venugopal, S., Weber, I., Hoenisch, P.: Elastic Business
Process Management: State of the Art and Open Challenges for BPM in the Cloud.
Future Generation Computer Systems 46, 36–50 (2015)
10. Subashini, S., Kavitha, V.: A survey on security issues in service delivery models
of cloud computing. Journal of Network and Computer Applications 34(1), 1 – 11
(2011)
11. van der Aalst, W.M.P., ter Hofstede, A.H.M., Kiepuszewski, B., Barros, A.P.:
Workflow Patterns. Distributed and Parallel Databases 14(1), 5–51 (2003)
12. Weske, M.: Business Process Management: Concepts, Languages, Architectures.
Springer, 2nd edn. (2012)
13. Zhang, K., Zhou, X., Chen, Y., Wang, X., Ruan, Y.: Sedic: privacy-aware data
intensive computing on hybrid clouds. In: Proceedings of the 18th ACM conference
on Computer and communications security. pp. 515–526. ACM (2011)