12,317 research outputs found
Predicting Scheduling Failures in the Cloud
Cloud Computing has emerged as a key technology to deliver and manage
computing, platform, and software services over the Internet. Task scheduling
algorithms play an important role in the efficiency of cloud computing services
as they aim to reduce the turnaround time of tasks and improve resource
utilization. Several task scheduling algorithms have been proposed in the
literature for cloud computing systems, the majority relying on the
computational complexity of tasks and the distribution of resources. However,
several tasks scheduled following these algorithms still fail because of
unforeseen changes in the cloud environments. In this paper, using tasks
execution and resource utilization data extracted from the execution traces of
real world applications at Google, we explore the possibility of predicting the
scheduling outcome of a task using statistical models. If we can successfully
predict tasks failures, we may be able to reduce the execution time of jobs by
rescheduling failed tasks earlier (i.e., before their actual failing time). Our
results show that statistical models can predict task failures with a precision
up to 97.4%, and a recall up to 96.2%. We simulate the potential benefits of
such predictions using the tool kit GloudSim and found that they can improve
the number of finished tasks by up to 40%. We also perform a case study using
the Hadoop framework of Amazon Elastic MapReduce (EMR) and the jobs of a gene
expression correlations analysis study from breast cancer research. We find
that when extending the scheduler of Hadoop with our predictive models, the
percentage of failed jobs can be reduced by up to 45%, with an overhead of less
than 5 minutes
Energy-Aware Cloud Management through Progressive SLA Specification
Novel energy-aware cloud management methods dynamically reallocate
computation across geographically distributed data centers to leverage regional
electricity price and temperature differences. As a result, a managed VM may
suffer occasional downtimes. Current cloud providers only offer high
availability VMs, without enough flexibility to apply such energy-aware
management. In this paper we show how to analyse past traces of dynamic cloud
management actions based on electricity prices and temperatures to estimate VM
availability and price values. We propose a novel SLA specification approach
for offering VMs with different availability and price values guaranteed over
multiple SLAs to enable flexible energy-aware cloud management. We determine
the optimal number of such SLAs as well as their availability and price
guaranteed values. We evaluate our approach in a user SLA selection simulation
using Wikipedia and Grid'5000 workloads. The results show higher customer
conversion and 39% average energy savings per VM.Comment: 14 pages, conferenc
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
Agent and cyber-physical system based self-organizing and self-adaptive intelligent shopfloor
The increasing demand of customized production results in huge challenges to the traditional manufacturing systems. In order to allocate resources timely according to the production requirements and to reduce disturbances, a framework for the future intelligent shopfloor is proposed in this paper. The framework consists of three primary models, namely the model of smart machine agent, the self-organizing model, and the self-adaptive model. A cyber-physical system for manufacturing shopfloor based on the multiagent technology is developed to realize the above-mentioned function models. Gray relational analysis and the hierarchy conflict resolution methods were applied to achieve the self-organizing and self-adaptive capabilities, thereby improving the reconfigurability and responsiveness of the shopfloor. A prototype system is developed, which has the adequate flexibility and robustness to configure resources and to deal with disturbances effectively. This research provides a feasible method for designing an autonomous factory with exception-handling capabilities
InterCloud: Utility-Oriented Federation of Cloud Computing Environments for Scaling of Application Services
Cloud computing providers have setup several data centers at different
geographical locations over the Internet in order to optimally serve needs of
their customers around the world. However, existing systems do not support
mechanisms and policies for dynamically coordinating load distribution among
different Cloud-based data centers in order to determine optimal location for
hosting application services to achieve reasonable QoS levels. Further, the
Cloud computing providers are unable to predict geographic distribution of
users consuming their services, hence the load coordination must happen
automatically, and distribution of services must change in response to changes
in the load. To counter this problem, we advocate creation of federated Cloud
computing environment (InterCloud) that facilitates just-in-time,
opportunistic, and scalable provisioning of application services, consistently
achieving QoS targets under variable workload, resource and network conditions.
The overall goal is to create a computing environment that supports dynamic
expansion or contraction of capabilities (VMs, services, storage, and database)
for handling sudden variations in service demands.
This paper presents vision, challenges, and architectural elements of
InterCloud for utility-oriented federation of Cloud computing environments. The
proposed InterCloud environment supports scaling of applications across
multiple vendor clouds. We have validated our approach by conducting a set of
rigorous performance evaluation study using the CloudSim toolkit. The results
demonstrate that federated Cloud computing model has immense potential as it
offers significant performance gains as regards to response time and cost
saving under dynamic workload scenarios.Comment: 20 pages, 4 figures, 3 tables, conference pape
Survey on Additive Manufacturing, Cloud 3D Printing and Services
Cloud Manufacturing (CM) is the concept of using manufacturing resources in a
service oriented way over the Internet. Recent developments in Additive
Manufacturing (AM) are making it possible to utilise resources ad-hoc as
replacement for traditional manufacturing resources in case of spontaneous
problems in the established manufacturing processes. In order to be of use in
these scenarios the AM resources must adhere to a strict principle of
transparency and service composition in adherence to the Cloud Computing (CC)
paradigm. With this review we provide an overview over CM, AM and relevant
domains as well as present the historical development of scientific research in
these fields, starting from 2002. Part of this work is also a meta-review on
the domain to further detail its development and structure
Algorithms for advance bandwidth reservation in media production networks
Media production generally requires many geographically distributed actors (e.g., production houses, broadcasters, advertisers) to exchange huge amounts of raw video and audio data. Traditional distribution techniques, such as dedicated point-to-point optical links, are highly inefficient in terms of installation time and cost. To improve efficiency, shared media production networks that connect all involved actors over a large geographical area, are currently being deployed. The traffic in such networks is often predictable, as the timing and bandwidth requirements of data transfers are generally known hours or even days in advance. As such, the use of advance bandwidth reservation (AR) can greatly increase resource utilization and cost efficiency. In this paper, we propose an Integer Linear Programming formulation of the bandwidth scheduling problem, which takes into account the specific characteristics of media production networks, is presented. Two novel optimization algorithms based on this model are thoroughly evaluated and compared by means of in-depth simulation results
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