10,198 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
High-Performance Cloud Computing: A View of Scientific Applications
Scientific computing often requires the availability of a massive number of
computers for performing large scale experiments. Traditionally, these needs
have been addressed by using high-performance computing solutions and installed
facilities such as clusters and super computers, which are difficult to setup,
maintain, and operate. Cloud computing provides scientists with a completely
new model of utilizing the computing infrastructure. Compute resources, storage
resources, as well as applications, can be dynamically provisioned (and
integrated within the existing infrastructure) on a pay per use basis. These
resources can be released when they are no more needed. Such services are often
offered within the context of a Service Level Agreement (SLA), which ensure the
desired Quality of Service (QoS). Aneka, an enterprise Cloud computing
solution, harnesses the power of compute resources by relying on private and
public Clouds and delivers to users the desired QoS. Its flexible and service
based infrastructure supports multiple programming paradigms that make Aneka
address a variety of different scenarios: from finance applications to
computational science. As examples of scientific computing in the Cloud, we
present a preliminary case study on using Aneka for the classification of gene
expression data and the execution of fMRI brain imaging workflow.Comment: 13 pages, 9 figures, conference pape
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
Cloud computing resource scheduling and a survey of its evolutionary approaches
A disruptive technology fundamentally transforming the way that computing services are delivered, cloud computing offers information and communication technology users a new dimension of convenience of resources, as services via the Internet. Because cloud provides a finite pool of virtualized on-demand resources, optimally scheduling them has become an essential and rewarding topic, where a trend of using Evolutionary Computation (EC) algorithms is emerging rapidly. Through analyzing the cloud computing architecture, this survey first presents taxonomy at two levels of scheduling cloud resources. It then paints a landscape of the scheduling problem and solutions. According to the taxonomy, a comprehensive survey of state-of-the-art approaches is presented systematically. Looking forward, challenges and potential future research directions are investigated and invited, including real-time scheduling, adaptive dynamic scheduling, large-scale scheduling, multiobjective scheduling, and distributed and parallel scheduling. At the dawn of Industry 4.0, cloud computing scheduling for cyber-physical integration with the presence of big data is also discussed. Research in this area is only in its infancy, but with the rapid fusion of information and data technology, more exciting and agenda-setting topics are likely to emerge on the horizon
Task Runtime Prediction in Scientific Workflows Using an Online Incremental Learning Approach
Many algorithms in workflow scheduling and resource provisioning rely on the
performance estimation of tasks to produce a scheduling plan. A profiler that
is capable of modeling the execution of tasks and predicting their runtime
accurately, therefore, becomes an essential part of any Workflow Management
System (WMS). With the emergence of multi-tenant Workflow as a Service (WaaS)
platforms that use clouds for deploying scientific workflows, task runtime
prediction becomes more challenging because it requires the processing of a
significant amount of data in a near real-time scenario while dealing with the
performance variability of cloud resources. Hence, relying on methods such as
profiling tasks' execution data using basic statistical description (e.g.,
mean, standard deviation) or batch offline regression techniques to estimate
the runtime may not be suitable for such environments. In this paper, we
propose an online incremental learning approach to predict the runtime of tasks
in scientific workflows in clouds. To improve the performance of the
predictions, we harness fine-grained resources monitoring data in the form of
time-series records of CPU utilization, memory usage, and I/O activities that
are reflecting the unique characteristics of a task's execution. We compare our
solution to a state-of-the-art approach that exploits the resources monitoring
data based on regression machine learning technique. From our experiments, the
proposed strategy improves the performance, in terms of the error, up to
29.89%, compared to the state-of-the-art solutions.Comment: Accepted for presentation at main conference track of 11th IEEE/ACM
International Conference on Utility and Cloud Computin
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