Availability is one of the most important requirements in production system. Keeping a persistent level of high availability in the Infrastructure-as-a-Service (iaas) cloudcomputing is a challenge due to the complexi...
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Availability is one of the most important requirements in production system. Keeping a persistent level of high availability in the Infrastructure-as-a-Service (iaas) cloudcomputing is a challenge due to the complexity of service providing. By definition, the availability can be maintained by coupling with the fault tolerance approaches. Recently, many fault tolerance methods have been developed, but few of them adequately consider the fault detection aspect, which is critical to issue the appropriate recovery actions just in time. In this paper, based on a rigorous analysis on the nature of failures, we would like to introduce a method to early identify the faults occurring in the iaas system. By engaging fuzzy logic algorithm and prediction technique, the proposed approach can provide better performance in terms of accuracy and reaction rate, which subsequently enhances the system reliability.
Infrastructure as a Service (iaas) is one of the three important fundamental service models provided by cloudcomputing. It provides users with computing resource and storage resource in terms of virtual machine insta...
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Infrastructure as a Service (iaas) is one of the three important fundamental service models provided by cloudcomputing. It provides users with computing resource and storage resource in terms of virtual machine instances. Because of the rapid development of cloudcomputing, more and more application systems have been deployed on the iaas cloud computing platforms. Therefore, once anomalies incur in the iaas cloud computing platforms, all the application systems cannot work normally. In order to enhance the dependability of iaas cloud computing platform, a virtual machine instance anomaly detection system is proposed for iaas cloud computing platform to detect virtual machine instances that exhibit abnormal behaviors. The proposed virtual machine instance system consists of four modules that are the data collection, the data transmission, the data storage, and the anomaly detection. In order to reduce the computing complexity and improve the detection precision, the anomaly detection module introduces the principal components analysis to reprocess the collected data and then adopts the Bayesian decision theory to detect the abnormal data. Experimental results show that the proposed virtual machine instance anomaly detection system is effective.
In recent years, power consumption has become one of the hottest research trends in computer science and industry. Most of the reasons are related to the operational budget and the environmental issues. In this paper,...
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In recent years, power consumption has become one of the hottest research trends in computer science and industry. Most of the reasons are related to the operational budget and the environmental issues. In this paper, we would like to propose an energy-efficient solution for orchestrating the resource in cloudcomputing. In nature, the proposed approach firstly predicts the resource utilization of the upcoming period based on the Gaussian process regression method. Subsequently, the convex optimization technique is engaged to compute an appropriate quantity of physical servers for each monitoring window. This quantity of interest is calculated to ensure that a minimum number of servers can still provide an acceptable quality of service. Finally, a corresponding migrating instruction is issued to stack the virtual machines and turn off the idle physical servers to achieve the objective of energy savings. In order to evaluate the proposed method, we conduct the experiments using synthetic data from 29-day period of Google traces and real workload from the Montage open-source toolkit Through the evaluation, we show that the proposed approach can achieve a significant result in reducing the energy consumption as well as maintaining the system performance. (C) 2016 Elsevier Inc. All rights reserved.
Infrastructure-as-a-service (iaas) clouds offer diverse instance purchasing options. A user can either run instances on demand and pay only for what it uses, or it can prepay to reserve instances for a long period, du...
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Infrastructure-as-a-service (iaas) clouds offer diverse instance purchasing options. A user can either run instances on demand and pay only for what it uses, or it can prepay to reserve instances for a long period, during which a usage discount is entitled. An important problem facing a user is how these two instance options can be dynamically combined to serve time-varying demands at minimum cost. Existing strategies in the literature, however, require either exact knowledge or the distribution of demands in the long-term future, which significantly limits their use in practice. Unlike existing works, we propose two practical online algorithms, one deterministic and another randomized, that dynamically combine the two instance options online without any knowledge of the future. We show that the proposed deterministic (resp., randomized) algorithm incurs no more than 2 - alpha (resp., e/(e - 1 + alpha) times the minimum cost obtained by an optimal offline algorithm that knows the exact future a priori, where alpha is the entitled discount after reservation. Our online algorithms achieve the best possible competitive ratios in both the deterministic and randomized cases, and can be easily extended to cases when short-term predictions are reliable. Simulations driven by a large volume of real-world traces show that significant cost savings can be achieved with prevalent iaas prices.
It is a challenging job to carry out effective experiments in LSDIS with limited resources in laboratory. In this paper, we based on virtualization technology and software defined network (SDN) architecture, study the...
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ISBN:
(纸本)9789811026690;9789811026683
It is a challenging job to carry out effective experiments in LSDIS with limited resources in laboratory. In this paper, we based on virtualization technology and software defined network (SDN) architecture, study the mechanism of constructing iaas platform for LSDIS evaluation and testing environment, proposed virtual resources optimization mapping algorithm to balance the loads of the hardware, built prototype system and designed some simulation testing use case to verify the proposed system. The simulation results shows that the proposed approach employs similar performances as physical environment for LSDIS simulation tasks.
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