502 research outputs found
Budget-aware scheduling algorithm for scientific workflow applications across multiple clouds. A Mathematical Optimization-Based Approach
Scientific workflows have become a prevailing means of achieving significant scientific advances at an ever-increasing rate. Scheduling mechanisms and approaches are vital to automating these large-scale scientific workflows efficiently. On the other hand, with the advent of cloud computing and its easier availability and lower cost of use, more attention has been paid to the execution and scheduling of scientific workflows in this new paradigm environment. For scheduling large-scale workflows, a multi-cloud environment will typically have a more significant advantage in various computing resources than a single cloud provider. Also, the scheduling makespan and cost can be reduced if the computing resources are used optimally in a multi-cloud environment. Accordingly, this thesis addressed the problem of scientific workflow scheduling in the multi-cloud environment under budget constraints to minimize associated makespan. Furthermore, this study tries to minimize costs, including fees for running VMs and data transfer, minimize the data transfer time, and fulfill budget and resource constraints in the multi-clouds scenario. To this end, we proposed Mixed-Integer Linear Programming (MILP) models that can be solved in a reasonable time by available solvers. We divided the workflow tasks into small segments, distributed them among VMs with multi-vCPU, and formulated them in mathematical programming. In the proposed mathematical model, the objective of a problem and real and physical constraints or restrictions are formulated using exact mathematical functions. We analyzed the treatment of optimal makespan under variations in budget, workflow size, and different segment sizes. The evaluation's results signify that our proposed approach has achieved logical and expected results in meeting the set objectives
funcX: A Federated Function Serving Fabric for Science
Exploding data volumes and velocities, new computational methods and
platforms, and ubiquitous connectivity demand new approaches to computation in
the sciences. These new approaches must enable computation to be mobile, so
that, for example, it can occur near data, be triggered by events (e.g.,
arrival of new data), be offloaded to specialized accelerators, or run remotely
where resources are available. They also require new design approaches in which
monolithic applications can be decomposed into smaller components, that may in
turn be executed separately and on the most suitable resources. To address
these needs we present funcX---a distributed function as a service (FaaS)
platform that enables flexible, scalable, and high performance remote function
execution. funcX's endpoint software can transform existing clouds, clusters,
and supercomputers into function serving systems, while funcX's cloud-hosted
service provides transparent, secure, and reliable function execution across a
federated ecosystem of endpoints. We motivate the need for funcX with several
scientific case studies, present our prototype design and implementation, show
optimizations that deliver throughput in excess of 1 million functions per
second, and demonstrate, via experiments on two supercomputers, that funcX can
scale to more than more than 130000 concurrent workers.Comment: Accepted to ACM Symposium on High-Performance Parallel and
Distributed Computing (HPDC 2020). arXiv admin note: substantial text overlap
with arXiv:1908.0490
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
Partitioning workflow applications over federated clouds to meet non-functional requirements
PhD ThesisWith cloud computing, users can acquire computer resources when they need them
on a pay-as-you-go business model. Because of this, many applications are now being
deployed in the cloud, and there are many di erent cloud providers worldwide. Importantly,
all these various infrastructure providers o er services with di erent levels
of quality. For example, cloud data centres are governed by the privacy and security
policies of the country where the centre is located, while many organisations have
created their own internal \private cloud" to meet security needs.
With all this varieties and uncertainties, application developers who decide to host their
system in the cloud face the issue of which cloud to choose to get the best operational
conditions in terms of price, reliability and security. And the decision becomes even
more complicated if their application consists of a number of distributed components,
each with slightly di erent requirements.
Rather than trying to identify the single best cloud for an application, this thesis
considers an alternative approach, that is, combining di erent clouds to meet users'
non-functional requirements. Cloud federation o ers the ability to distribute a single
application across two or more clouds, so that the application can bene t from the
advantages of each one of them. The key challenge for this approach is how to nd the
distribution (or deployment) of application components, which can yield the greatest
bene ts. In this thesis, we tackle this problem and propose a set of algorithms, and a
framework, to partition a work
ow-based application over federated clouds in order to
exploit the strengths of each cloud. The speci c goal is to split a distributed application
structured as a work
ow such that the security and reliability requirements of each
component are met, whilst the overall cost of execution is minimised.
To achieve this, we propose and evaluate a cloud broker for partitioning a work
ow
application over federated clouds. The broker integrates with the e-Science Central
cloud platform to automatically deploy a work
ow over public and private clouds.
We developed a deployment planning algorithm to partition a large work
ow appli-
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cation across federated clouds so as to meet security requirements and minimise the
monetary cost.
A more generic framework is then proposed to model, quantify and guide the partitioning
and deployment of work
ows over federated clouds. This framework considers
the situation where changes in cloud availability (including cloud failure) arise during
work
ow execution
The Contemporary Affirmation of Taxonomy and Recent Literature on Workflow Scheduling and Management in Cloud Computing
The Cloud computing systemspreferred over the traditional forms of computing such as grid computing, utility computing, autonomic computing is attributed forits ease of access to computing, for its QoS preferences, SLA2019;s conformity, security and performance offered with minimal supervision. A cloud workflow schedule when designed efficiently achieves optimalre source sage, balance of workloads, deadline specific execution, cost control according to budget specifications, efficient consumption of energy etc. to meet the performance requirements of today2019; svast scientific and business requirements. The businesses requirements under recent technologies like pervasive computing are motivating the technology of cloud computing for further advancements. In this paper we discuss some of the important literature published on cloud workflow scheduling
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
Allocation of Virtual Machines in Cloud Data Centers - A Survey of Problem Models and Optimization Algorithms
Data centers in public, private, and hybrid cloud settings make it possible to provision virtual machines
(VMs) with unprecedented flexibility. However, purchasing, operating, and maintaining the underlying physical
resources incurs significant monetary costs and also environmental impact. Therefore, cloud providers must
optimize the usage of physical resources by a careful allocation of VMs to hosts, continuously balancing between
the conflicting requirements on performance and operational costs. In recent years, several algorithms have been
proposed for this important optimization problem. Unfortunately, the proposed approaches are hardly comparable
because of subtle differences in the used problem models. This paper surveys the used problem formulations and
optimization algorithms, highlighting their strengths and limitations, also pointing out the areas that need further
research in the future
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