3,838 research outputs found
Modeling cloud resources using machine learning
Cloud computing is a new Internet infrastructure paradigm where management optimization has become a challenge to be solved, as all current management systems are human-driven or ad-hoc automatic systems that must be tuned manually by experts. Management of cloud resources require accurate information about all the elements involved (host machines, resources, offered services, and clients), and some of this information can only be obtained a posteriori. Here we present the cloud and part of its architecture as a new scenario where data mining and machine learning can be applied to discover information and improve its management thanks to modeling and prediction. As a novel case of study we show in this work the modeling of basic cloud resources using machine learning, predicting resource requirements from context information like amount of load and clients, and also predicting the quality of service from resource planning, in order to feed cloud schedulers. Further, this work is an important part of our ongoing research program, where accurate models and predictors are essential to optimize cloud management autonomic systems.Postprint (published version
Towards Autonomic Service Provisioning Systems
This paper discusses our experience in building SPIRE, an autonomic system
for service provision. The architecture consists of a set of hosted Web
Services subject to QoS constraints, and a certain number of servers used to
run session-based traffic. Customers pay for having their jobs run, but require
in turn certain quality guarantees: there are different SLAs specifying charges
for running jobs and penalties for failing to meet promised performance
metrics. The system is driven by an utility function, aiming at optimizing the
average earned revenue per unit time. Demand and performance statistics are
collected, while traffic parameters are estimated in order to make dynamic
decisions concerning server allocation and admission control. Different utility
functions are introduced and a number of experiments aiming at testing their
performance are discussed. Results show that revenues can be dramatically
improved by imposing suitable conditions for accepting incoming traffic; the
proposed system performs well under different traffic settings, and it
successfully adapts to changes in the operating environment.Comment: 11 pages, 9 Figures,
http://www.wipo.int/pctdb/en/wo.jsp?WO=201002636
SLA-Oriented Resource Provisioning for Cloud Computing: Challenges, Architecture, and Solutions
Cloud computing systems promise to offer subscription-oriented,
enterprise-quality computing services to users worldwide. With the increased
demand for delivering services to a large number of users, they need to offer
differentiated services to users and meet their quality expectations. Existing
resource management systems in data centers are yet to support Service Level
Agreement (SLA)-oriented resource allocation, and thus need to be enhanced to
realize cloud computing and utility computing. In addition, no work has been
done to collectively incorporate customer-driven service management,
computational risk management, and autonomic resource management into a
market-based resource management system to target the rapidly changing
enterprise requirements of Cloud computing. This paper presents vision,
challenges, and architectural elements of SLA-oriented resource management. The
proposed architecture supports integration of marketbased provisioning policies
and virtualisation technologies for flexible allocation of resources to
applications. The performance results obtained from our working prototype
system shows the feasibility and effectiveness of SLA-based resource
provisioning in Clouds.Comment: 10 pages, 7 figures, Conference Keynote Paper: 2011 IEEE
International Conference on Cloud and Service Computing (CSC 2011, IEEE
Press, USA), Hong Kong, China, December 12-14, 201
Energy-Efficient Management of Data Center Resources for Cloud Computing: A Vision, Architectural Elements, and Open Challenges
Cloud computing is offering utility-oriented IT services to users worldwide.
Based on a pay-as-you-go model, it enables hosting of pervasive applications
from consumer, scientific, and business domains. However, data centers hosting
Cloud applications consume huge amounts of energy, contributing to high
operational costs and carbon footprints to the environment. Therefore, we need
Green Cloud computing solutions that can not only save energy for the
environment but also reduce operational costs. This paper presents vision,
challenges, and architectural elements for energy-efficient management of Cloud
computing environments. We focus on the development of dynamic resource
provisioning and allocation algorithms that consider the synergy between
various data center infrastructures (i.e., the hardware, power units, cooling
and software), and holistically work to boost data center energy efficiency and
performance. In particular, this paper proposes (a) architectural principles
for energy-efficient management of Clouds; (b) energy-efficient resource
allocation policies and scheduling algorithms considering quality-of-service
expectations, and devices power usage characteristics; and (c) a novel software
technology for energy-efficient management of Clouds. We have validated our
approach by conducting a set of rigorous performance evaluation study using the
CloudSim toolkit. The results demonstrate that 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: 12 pages, 5 figures,Proceedings of the 2010 International Conference
on Parallel and Distributed Processing Techniques and Applications (PDPTA
2010), Las Vegas, USA, July 12-15, 201
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