1,300 research outputs found
Strategic and operational services for workload management in the cloud
In hosting environments such as Infrastructure as a Service (IaaS) clouds, desirable application performance is typically guaranteed through the use of Service Level Agreements (SLAs), which specify minimal fractions of resource capacities that must be allocated by a service provider for unencumbered use by customers to ensure proper operation of their workloads. Most IaaS offerings are presented to customers as fixed-size and fixed-price SLAs, that do not match well the needs of specific applications. Furthermore, arbitrary colocation of applications with different SLAs may result in inefficient utilization of hosts' resources, resulting in economically undesirable customer behavior.
In this thesis, we propose the design and architecture of a Colocation as a Service (CaaS) framework: a set of strategic and operational services that allow the efficient colocation of customer workloads. CaaS strategic services provide customers the means to specify their application workload using an SLA language that provides them the opportunity and incentive to take advantage of any tolerances they may have regarding the scheduling of their workloads. CaaS operational services provide the information necessary for, and carry out the reconfigurations mandated by strategic services. We recognize that it could be the case that there are multiple, yet functionally equivalent ways to express an SLA. Thus, towards that end, we present a service that allows the provably-safe transformation of SLAs from one form to another for the purpose of achieving more efficient colocation. Our CaaS framework could be incorporated into an IaaS offering by providers or it could be implemented as a value added proposition by IaaS resellers. To establish the practicality of such offerings, we present a prototype implementation of our proposed CaaS framework
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
Autonomous grid scheduling using probabilistic job runtime scheduling
Computational Grids are evolving into a global, service-oriented architecture â
a universal platform for delivering future computational services to a range of
applications of varying complexity and resource requirements. The thesis focuses
on developing a new scheduling model for general-purpose, utility clusters
based on the concept of user requested job completion deadlines. In such a
system, a user would be able to request each job to finish by a certain deadline,
and possibly to a certain monetary cost. Implementing deadline scheduling is
dependent on the ability to predict the execution time of each queued job, and
on an adaptive scheduling algorithm able to use those predictions to maximise
deadline adherence. The thesis proposes novel solutions to these two problems
and documents their implementation in a largely autonomous and self-managing
way.
The starting point of the work is an extensive analysis of a representative
Grid workload revealing consistent workflow patterns, usage cycles and correlations between the execution times of jobs and its properties commonly collected
by the Grid middleware for accounting purposes. An automated approach is
proposed to identify these dependencies and use them to partition the highly
variable workload into subsets of more consistent and predictable behaviour.
A range of time-series forecasting models, applied in this context for the first
time, were used to model the job execution times as a function of their historical
behaviour and associated properties. Based on the resulting predictions of job
runtimes a novel scheduling algorithm is able to estimate the latest job start
time necessary to meet the requested deadline and sort the queue accordingly to
minimise the amount of deadline overrun.
The testing of the proposed approach was done using the actual job trace
collected from a production Grid facility. The best performing execution time
predictor (the auto-regressive moving average method) coupled to workload
partitioning based on three simultaneous job properties returned the median
absolute percentage error centroid of only 4.75%. This level of prediction
accuracy enabled the proposed deadline scheduling method to reduce the average deadline overrun time ten-fold compared to the benchmark batch scheduler.
Overall, the thesis demonstrates that deadline scheduling of computational
jobs on the Grid is achievable using statistical forecasting of job execution times
based on historical information. The proposed approach is easily implementable,
substantially self-managing and better matched to the human workflow making
it well suited for implementation in the utility Grids of the future
A heuristic approach for the allocation of resources in large-scale computing infrastructures
An increasing number of enterprise applications are intensive in their consumption of IT, but are infrequently used. Consequently, organizations either host an oversized IT infrastructure or they are incapable of realizing the benefits of new applications. A solution to the challenge is provided by the large-scale computing infrastructures of Clouds and Grids which allow resources to be shared. A major challenge is the development of mechanisms that allow efficient sharing of IT resources. Market mechanisms are promising, but there is a lack of research in scalable market mechanisms. We extend the Multi-Attribute Combinatorial Exchange mechanism with greedy heuristics to address the scalability challenge. The evaluation shows a trade-off between efficiency and scalability. There is no statistical evidence for an influence on the incentive properties of the market mechanism. This is an encouraging result as theory predicts heuristics to ruin the mechanismâs incentive properties. Copyright © 2015 John Wiley & Sons, Ltd
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