1,127 research outputs found
Elastic Business Process Management: State of the Art and Open Challenges for BPM in the Cloud
With the advent of cloud computing, organizations are nowadays able to react
rapidly to changing demands for computational resources. Not only individual
applications can be hosted on virtual cloud infrastructures, but also complete
business processes. This allows the realization of so-called elastic processes,
i.e., processes which are carried out using elastic cloud resources. Despite
the manifold benefits of elastic processes, there is still a lack of solutions
supporting them.
In this paper, we identify the state of the art of elastic Business Process
Management with a focus on infrastructural challenges. We conceptualize an
architecture for an elastic Business Process Management System and discuss
existing work on scheduling, resource allocation, monitoring, decentralized
coordination, and state management for elastic processes. Furthermore, we
present two representative elastic Business Process Management Systems which
are intended to counter these challenges. Based on our findings, we identify
open issues and outline possible research directions for the realization of
elastic processes and elastic Business Process Management.Comment: Please cite as: S. Schulte, C. Janiesch, S. Venugopal, I. Weber, and
P. Hoenisch (2015). Elastic Business Process Management: State of the Art and
Open Challenges for BPM in the Cloud. Future Generation Computer Systems,
Volume NN, Number N, NN-NN., http://dx.doi.org/10.1016/j.future.2014.09.00
A Taxonomy of Workflow Management Systems for Grid Computing
With the advent of Grid and application technologies, scientists and
engineers are building more and more complex applications to manage and process
large data sets, and execute scientific experiments on distributed resources.
Such application scenarios require means for composing and executing complex
workflows. Therefore, many efforts have been made towards the development of
workflow management systems for Grid computing. In this paper, we propose a
taxonomy that characterizes and classifies various approaches for building and
executing workflows on Grids. We also survey several representative Grid
workflow systems developed by various projects world-wide to demonstrate the
comprehensiveness of the taxonomy. The taxonomy not only highlights the design
and engineering similarities and differences of state-of-the-art in Grid
workflow systems, but also identifies the areas that need further research.Comment: 29 pages, 15 figure
QoS-aware predictive workflow scheduling
This research places the basis of QoS-aware predictive workflow scheduling. This research novel contributions will open up prospects for future research in handling complex big workflow applications with high uncertainty and dynamism. The results from the proposed workflow scheduling algorithm shows significant improvement in terms of the performance and reliability of the workflow applications
QoS-Aware Middleware for Web Services Composition
The paradigmatic shift from a Web of manual interactions to a Web of programmatic interactions driven by Web services is creating unprecedented opportunities for the formation of online Business-to-Business (B2B) collaborations. In particular, the creation of value-added services by composition of existing ones is gaining a significant momentum. Since many available Web services provide overlapping or identical functionality, albeit with different Quality of Service (QoS), a choice needs to be made to determine which services are to participate in a given composite service. This paper presents a middleware platform which addresses the issue of selecting Web services for the purpose of their composition in a way that maximizes user satisfaction expressed as utility functions over QoS attributes, while satisfying the constraints set by the user and by the structure of the composite service. Two selection approaches are described and compared: one based on local (task-level) selection of services and the other based on global allocation of tasks to services using integer programming
HPC Cloud for Scientific and Business Applications: Taxonomy, Vision, and Research Challenges
High Performance Computing (HPC) clouds are becoming an alternative to
on-premise clusters for executing scientific applications and business
analytics services. Most research efforts in HPC cloud aim to understand the
cost-benefit of moving resource-intensive applications from on-premise
environments to public cloud platforms. Industry trends show hybrid
environments are the natural path to get the best of the on-premise and cloud
resources---steady (and sensitive) workloads can run on on-premise resources
and peak demand can leverage remote resources in a pay-as-you-go manner.
Nevertheless, there are plenty of questions to be answered in HPC cloud, which
range from how to extract the best performance of an unknown underlying
platform to what services are essential to make its usage easier. Moreover, the
discussion on the right pricing and contractual models to fit small and large
users is relevant for the sustainability of HPC clouds. This paper brings a
survey and taxonomy of efforts in HPC cloud and a vision on what we believe is
ahead of us, including a set of research challenges that, once tackled, can
help advance businesses and scientific discoveries. This becomes particularly
relevant due to the fast increasing wave of new HPC applications coming from
big data and artificial intelligence.Comment: 29 pages, 5 figures, Published in ACM Computing Surveys (CSUR
Supporting Quality of Service in Scientific Workflows
While workflow management systems have been utilized in enterprises to support
businesses for almost two decades, the use of workflows in scientific environments
was fairly uncommon until recently. Nowadays, scientists use workflow systems to
conduct scientific experiments, simulations, and distributed computations. However,
most scientific workflow management systems have not been built using existing
workflow technology; rather they have been designed and developed from
scratch. Due to the lack of generality of early scientific workflow systems, many
domain-specific workflow systems have been developed. Generally speaking, those
domain-specific approaches lack common acceptance and tool support and offer
lower robustness compared to business workflow systems.
In this thesis, the use of the industry standard BPEL, a workflow language
for modeling business processes, is proposed for the modeling and the execution of
scientific workflows. Due to the widespread use of BPEL in enterprises, a number
of stable and mature software products exist. The language is expressive (Turingcomplete)
and not restricted to specific applications. BPEL is well suited for the
modeling of scientific workflows, but existing implementations of the standard lack
important features that are necessary for the execution of scientific workflows.
This work presents components that extend an existing implementation of the
BPEL standard and eliminate the identified weaknesses. The components thus provide
the technical basis for use of BPEL in academia. The particular focus is on
so-called non-functional (Quality of Service) requirements. These requirements include
scalability, reliability (fault tolerance), data security, and cost (of executing a
workflow). From a technical perspective, the workflow system must be able to interface
with the middleware systems that are commonly used by the scientific workflow
community to allow access to heterogeneous, distributed resources (especially Grid
and Cloud resources).
The major components cover exactly these requirements:
Cloud Resource Provisioner Scalability of the workflow system is achieved by
automatically adding additional (Cloud) resources to the workflow system’s
resource pool when the workflow system is heavily loaded.
Fault Tolerance Module High reliability is achieved via continuous monitoring
of workflow execution and corrective interventions, such as re-execution of a
failed workflow step or replacement of the faulty resource.
Cost Aware Data Flow Aware Scheduler The majority of scientific workflow
systems only take the performance and utilization of resources for the execution
of workflow steps into account when making scheduling decisions. The
presented workflow system goes beyond that. By defining preference values
for the weighting of costs and the anticipated workflow execution time,
workflow users may influence the resource selection process. The developed multiobjective
scheduling algorithm respects the defined weighting and makes both
efficient and advantageous decisions using a heuristic approach.
Security Extensions Because it supports various encryption, signature and authentication
mechanisms (e.g., Grid Security Infrastructure), the workflow
system guarantees data security in the transfer of workflow data.
Furthermore, this work identifies the need to equip workflow developers with
workflow modeling tools that can be used intuitively. This dissertation presents
two modeling tools that support users with different needs. The first tool, DAVO
(domain-adaptable, Visual BPEL Orchestrator), operates at a low level of abstraction
and allows users with knowledge of BPEL to use the full extent of the language.
DAVO is a software that offers extensibility and customizability for different application
domains. These features are used in the implementation of the second tool,
SimpleBPEL Composer. SimpleBPEL is aimed at users with little or no background
in computer science and allows for quick and intuitive development of BPEL workflows based on predefined components
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