977 research outputs found
Technical Report: A Trace-Based Performance Study of Autoscaling Workloads of Workflows in Datacenters
To improve customer experience, datacenter operators offer support for
simplifying application and resource management. For example, running workloads
of workflows on behalf of customers is desirable, but requires increasingly
more sophisticated autoscaling policies, that is, policies that dynamically
provision resources for the customer. Although selecting and tuning autoscaling
policies is a challenging task for datacenter operators, so far relatively few
studies investigate the performance of autoscaling for workloads of workflows.
Complementing previous knowledge, in this work we propose the first
comprehensive performance study in the field. Using trace-based simulation, we
compare state-of-the-art autoscaling policies across multiple application
domains, workload arrival patterns (e.g., burstiness), and system utilization
levels. We further investigate the interplay between autoscaling and regular
allocation policies, and the complexity cost of autoscaling. Our quantitative
study focuses not only on traditional performance metrics and on
state-of-the-art elasticity metrics, but also on time- and memory-related
autoscaling-complexity metrics. Our main results give strong and quantitative
evidence about previously unreported operational behavior, for example, that
autoscaling policies perform differently across application domains and by how
much they differ.Comment: Technical Report for the CCGrid 2018 submission "A Trace-Based
Performance Study of Autoscaling Workloads of Workflows in Datacenters
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
Many-Task Computing and Blue Waters
This report discusses many-task computing (MTC) generically and in the
context of the proposed Blue Waters systems, which is planned to be the largest
NSF-funded supercomputer when it begins production use in 2012. The aim of this
report is to inform the BW project about MTC, including understanding aspects
of MTC applications that can be used to characterize the domain and
understanding the implications of these aspects to middleware and policies.
Many MTC applications do not neatly fit the stereotypes of high-performance
computing (HPC) or high-throughput computing (HTC) applications. Like HTC
applications, by definition MTC applications are structured as graphs of
discrete tasks, with explicit input and output dependencies forming the graph
edges. However, MTC applications have significant features that distinguish
them from typical HTC applications. In particular, different engineering
constraints for hardware and software must be met in order to support these
applications. HTC applications have traditionally run on platforms such as
grids and clusters, through either workflow systems or parallel programming
systems. MTC applications, in contrast, will often demand a short time to
solution, may be communication intensive or data intensive, and may comprise
very short tasks. Therefore, hardware and software for MTC must be engineered
to support the additional communication and I/O and must minimize task dispatch
overheads. The hardware of large-scale HPC systems, with its high degree of
parallelism and support for intensive communication, is well suited for MTC
applications. However, HPC systems often lack a dynamic resource-provisioning
feature, are not ideal for task communication via the file system, and have an
I/O system that is not optimized for MTC-style applications. Hence, additional
software support is likely to be required to gain full benefit from the HPC
hardware
A Model for Scientific Workflows with Parallel and Distributed Computing
In the last decade we witnessed an immense evolution of the computing infrastructures
in terms of processing, storage and communication. On one hand, developments in hardware architectures have made it possible to run multiple virtual machines on a single physical machine. On the other hand, the increase of the available network communication bandwidth has enabled the widespread use of distributed computing infrastructures, for example based on clusters, grids and clouds. The above factors enabled different scientific communities to aim for the development and implementation of complex scientific applications possibly involving large amounts of data. However, due to their structural complexity, these applications require decomposition models to allow multiple tasks running in parallel and distributed environments.
The scientific workflow concept arises naturally as a way to model applications composed of multiple activities. In fact, in the past decades many initiatives have been
undertaken to model application development using the workflow paradigm, both in
the business and in scientific domains. However, despite such intensive efforts, current
scientific workflow systems and tools still have limitations, which pose difficulties to the
development of emerging large-scale, distributed and dynamic applications.
This dissertation proposes the AWARD model for scientific workflows with parallel
and distributed computing. AWARD is an acronym for Autonomic Workflow Activities
Reconfigurable and Dynamic.
The AWARD model has the following main characteristics.
It is based on a decentralized execution control model where multiple autonomic
workflow activities interact by exchanging tokens through input and output ports. The
activities can be executed separately in diverse computing environments, such as in a
single computer or on multiple virtual machines running on distributed infrastructures,
such as clusters and clouds.
It provides basic workflow patterns for parallel and distributed application decomposition and other useful patterns supporting feedback loops and load balancing. The model is suitable to express applications based on a finite or infinite number of iterations, thus allowing to model long-running workflows, which are typical in scientific experimention. A distintive contribution of the AWARD model is the support for dynamic reconfiguration
of long-running workflows. A dynamic reconfiguration allows to modify the
structure of the workflow, for example, to introduce new activities, modify the connections
between activity input and output ports. The activity behavior can also be modified,
for example, by dynamically replacing the activity algorithm.
In addition to the proposal of a new workflow model, this dissertation presents the
implementation of a fully functional software architecture that supports the AWARD
model. The implemented prototype was used to validate and refine the model across
multiple workflow scenarios whose usefulness has been demonstrated in practice clearly, through experimental results, demonstrating the advantages of the major characteristics and contributions of the AWARD model. The implemented prototype was also used to develop application cases, such as a workflow to support the implementation of the MapReduce model and a workflow to support a text mining application developed by an external user.
The extensive experimental work confirmed the adequacy of the AWARD model and
its implementation for developing applications that exploit parallelism and distribution
using the scientific workflows paradigm
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