The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Network virtualization is the enabling technology for sharing resources on cloud. The efficiency of virtual network embedding determines the expense and revenue ratio of a data center. In this paper, we consider the virtual network embedding problem in fat-tree data centers. We design various schemes to embed Nonblocking Multicast Virtual Networks (NMVNs) which are dedicated to deliver premium experience...
With the rapid growth of application migration, the anonymity in data center networks becomes important in breaking attack chains and guaranteeing user privacy. However, existing anonymity systems are designed for the Internet environment, which suffer from high computational and network resource consumption and deliver low performance, thus failing to be directly deployed in data centers. In order...
It is challenging to allocate the network bandwidth to virtual machines(VMs) hosting communication-intensive applications. Due to the temporal and spatial variability of the hosted applications, it is crucial how much bandwidth to be reserved for each VM and when to adjust it. Prior approaches typically resort to predicting the applications' network demands, according to which the VMs are placed once...
This paper provides a detailed Message Passing Interface (MPI) communication characterization across representative HPC applications. It further evaluates performance and power efficiency improvement opportunities. Specifically, it shows that the traditional approach of active polling while waiting for MPI messages is extremely power inefficient, especially under a constrained cluster-level power...
In this paper, we propose a novel server-centric network for data centers, called RRect. Compared to existing server-centric networks, RRect has a linear diameter to the network order and abundant parallel paths with near-equal lengths, so that traffic in RRect enjoys a short and predictable communication latency. We present an efficient routing algorithm to find paths between any pair of servers...
It is a significant problem to efficiently identifythe frequently-occurring patterns in a given dataset, so as tounveil the trends hidden behind the dataset. This work ismotivated by the serious demands of a high-performance inmemoryfrequent-pattern mining strategy, with joint optimizationover the mining performance and system durability. While thewidely-used frequent-pattern tree (FP-tree) serves...
In order to satisfy the competition of multiple GPU accelerated applications and make full use of GPU resources, a lot of previous works propose spatial-multitasking to execute multiple GPGPU applications simultaneously on a single GPU device. However, when adopting the spatial-multitasking framework, the inter-application interference may slow down different applications differently, leading to the...
As one of the most important deep learning models, convolutional neural networks (CNNs) have achieved great successes in a number of applications such as image classification, speech recognition and nature language understanding. Training CNNs on large data sets is computationally expensive, leading to a flurry of research and development of open-source parallel implementations on GPUs. However, few...
Big data decision-making techniques take advantage of large-scale data to extract important insights from them. One of the most important classes of such techniques falls in the domain of graph applications, where data segments and their inherent relationships are represented as vertices and edges. Efficiently processing large-scale graphs involves many subtle tradeoffs and is still regarded as an...
Rollback is an effective technique to resume the system execution from a recorded intermediate state upon failures. However, in virtualized environments, rollback of a virtual machine cluster (VMC) produces high network traffic and long service disruption, consequentially imposing significant overhead both on network and applications. In this paper, we propose Piccolo, a fast and efficient rollback...
In recent years k-means++ has become a popular initialization technique for improved k-means clustering. To date, most of the work done to improve its performance has involved parallelizing algorithms that are only approximations of k-means++. In this paper we present a parallelization of the exact k-means++ algorithm, with a proof of its correctness. We develop implementations for three distinct...
We investigate an efficient parallelization of a class of algorithms for the well-known Tucker decomposition of general N-dimensional sparse tensors. The targeted algorithms are iterative and use the alternating least squares method. At each iteration, for each dimension of an N-dimensional input tensor, the following operations are performed: (i) the tensor is multiplied with (N -- 1) matrices (TTMc...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.