Improving Network I/O Virtualization for Cloud Computing

Bourguiba, Manel; Haddadou, Kamel; Korbi, Inès El; Pujolle, Guy

doi:10.1109/tpds.2013.29

Cited by 46 publications

(15 citation statements)

References 13 publications

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“…For I/O virtualization performed on VMs instantiated on a single physical machine, VM throughput is almost ten times lower than the throughput in the server domain likely because of the costly communication between the server domain and VMs in the client domain [26]. Especially when dealing with small packets but high packet rate, the throughput is even lower since the software stack does not have enough CPU resources to process [67].…”

Section: Bandwidth Speed Distributed Storage and Computationmentioning

confidence: 99%

Cloud-enabled prognosis for manufacturing

et al. 2015

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Advanced manufacturing depends on the timely acquisition, distribution, and utilization of information from machines and processes across spatial boundaries. These activities can improve accuracy and reliability in predicting resource needs and allocation, maintenance scheduling, and remaining service life of equipment. As an emerging infrastructure, cloud computing provides new opportunities to achieve the goals of advanced manufacturing. This paper reviews the historical development of prognosis theories and techniques and projects their future growth enabled by the emerging cloud infrastructure. Techniques for cloud computing are highlighted, as well as the influence of these techniques on the paradigm of cloud-enabled prognosis for manufacturing. Finally, this paper discusses the envisioned architecture and associated challenges of cloud-enabled prognosis for manufacturing

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Section: Bandwidth Speed Distributed Storage and Computationmentioning

confidence: 99%

Cloud-enabled prognosis for manufacturing

et al. 2015

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“…In order to meet both requirements, E3-R employed two different fitness functions for different kinds of individuals. In [10], the authors focused on the bottleneck of network I/O and the aggregation on the packets delay and proposed a mechanism based on packet aggregation to achieve the best tradeoff between the throughput and packets delay. In [11], the authors investigated the elastic resource provisioning problem under the burstiness of incoming requests and energy consumption, employed the ON-OFF Markov chain and queueing theory to describe burstiness, and proposed a VM consolidation mechanism for each PM to solve the problem.…”

Section: Related Workmentioning

confidence: 99%

“…With the simulations to the real data sets, the offline training based on the basic learning algorithm is employed to acquire the varied job arrival rates, the numbers of the VMs, and the value table, the approximate function relation between resource provisioning. During offline learning process, multiple instances can run parallelly in order to learn by dividing the state space, so for each state space partition do (4) set upper and low bound of CPU, memory and bandwidth (5) Obtain running state of cloud computing platform (6) Obtain performance index (7) for each resources do (8) u s i n gAlgorithm 1 (9) end for (10) that the acquired relation can be approximately formulated with the regression function. The pseudocode of the value offline learning algorithm is illustrated in Algorithm 2.…”

Section: Offline Learningmentioning

confidence: 99%

Research on Cloud Computing Resources Provisioning Based on Reinforcement Learning

Peng¹,

Cui²,

Zuo³

et al. 2015

Mathematical Problems in Engineering

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As one of the core issues for cloud computing, resource management adopts virtualization technology to shield the underlying resource heterogeneity and complexity which makes the massive distributed resources form a unified giant resource pool. It can achieve efficient resource provisioning by using the rational implementing resource management methods and techniques. Therefore, how to manage cloud computing resources effectively becomes a challenging research topic. By analyzing the executing progress of a user job in the cloud computing environment, we proposed a novel resource provisioning scheme based on the reinforcement learning and queuing theory in this study. With the introduction of the concepts of Segmentation Service Level Agreement (SSLA) and Utilization Unit Time Cost (UUTC), we viewed the resource provisioning problem in cloud computing as a sequential decision issue, and then we designed a novel optimization object function and employed reinforcement learning to solve it. Experiment results not only demonstrated the effectiveness of the proposed scheme, but also proved to outperform the common methods of resource utilization rate in terms of SLA collision avoidance and user costs.

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“…Other important researches are that of the Ad Hoc Network and Wireless Mesh Network [19,20], channel allocation [21], Internet of things (IoT) [22], cloud computing [23], and some other fields.…”

Section: Multi-tier Architecture For the Internet Of Internets (Mtmentioning

confidence: 99%

Basic Internet Foundation and Cloud Computing

Suciu¹,

Butca²,

Suciu³

et al. 2015

2015 10th International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC)

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Basic Internet Foundation is an institute that aims to ensure optimized content delivery for capacity-limited networks. In this paper we describe the foundation's initiative in offering free access to low capacity Internet to people in areas with low admission and economic problems and/or no Internet coverage. The main contribution of this paper consists in pointing out solutions that this foundation proposes, as well as what other programs or companies have found encouraging the Internet development and adoption of Cloud Computing.

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Improving Network I/O Virtualization for Cloud Computing

Cited by 46 publications

References 13 publications

Cloud-enabled prognosis for manufacturing

Cloud-enabled prognosis for manufacturing

Research on Cloud Computing Resources Provisioning Based on Reinforcement Learning

Basic Internet Foundation and Cloud Computing

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