540 research outputs found

    Generalized and Resource-Efficient VNet Embeddings with Migrations

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    This paper attends to the problem of embedding flexibly specified CloudNets, virtual networks connecting cloud resources (such as storage or computation). We attend to a scenario where customers can request CloudNets at short notice, and an infrastructure provider (or a potential itermediate broker or reseller) first embeds the CloudNet fast (e.g., using a simple heuristic). Later, however, long-lived CloudNets embeddings are optimized by migrating them to more suitable locations, whose precise definition depends on a given objective function. For instance, such migrations can be useful to reduce the peak resource loads in the network by spreading CloudNets across the infrastructure, to save energy by moving CloudNets together and switching off unused components, or for maintenance purposes. We present a very generic algorithm to compute optimal embeddings of CloudNets: It allows for different objective functions (such as load minimization or energy conservation), supports cost-aware migration, and can deal with all link types that arise in practice (e.g., full-duplex or even wireless or wired broadcast links with multiple endpoints). Our evaluation shows that such a rigorous optimization is even feasible in order to optimize a moderate-size CloudNet of full flexibility (e.g., a router site, a small physical infrastructure or virtual provider network)

    Efficient Virtual Network Embedding Via Exploring Periodic Resource Demands

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    Cloud computing built on virtualization technologies promises provisioning elastic computing and communication resources to enterprise users. To share cloud resources efficiently, embedding virtual networks of different users to a distributed cloud consisting of multiple data centers (a substrate network) poses great challenges. Motivated by the fact that most enterprise virtual networks usually operate on long-term basics and have the characteristics of periodic resource demands, in this paper we study the virtual network embedding problem by embedding as many virtual networks as possible to a substrate network such that the revenue of the service provider of the substrate network is maximized, while meeting various Service Level Agreements (SLAs) between enterprise users and the cloud service provider. For this problem, we propose an efficient embedding algorithm by exploring periodic resource demands of virtual networks, and employing a novel embedding metric that models the workloads on both substrate nodes and communication links if the periodic resource demands of virtual networks are given; otherwise, we propose a prediction model to predict the periodic resource demands of these virtual networks based on their historic resource demands. We also evaluate the performance of the proposed algorithms by experimental simulation. Experimental results demonstrate that the proposed algorithms outperform existing algorithms, improving the revenue from 10% to 31%

    Cloud based multicasting using fat tree data confidential recurrent neural network

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    With the progress of cloud computing, more users are attracted by its strong and cost-effective computation potentiality. Nevertheless, whether Cloud Service Providers can efficiently protect Cloud Users data confidentiality (DC) remains a demanding issue. The CU may execute several applications with multicast needs. In Cloud different techniques were used to provide DC with multicast necessities. In this work, we aim at ensuring DC in the cloud. This is achieved using a two-step technique, called Fat Tree Data Confidential Recurrent Neural Network (FT-DCRNN) in a cloud environment. The first step performs the construction of Fat Tree based on Multicast model. The aim to use Fat Tree with Multicast model is that the multicast model propagates traffic on multiple links. With the Degree Restrict Multicast Fat Tree construction algorithm using a reference function, the minimum average between two links is measured. With these measured links, multicast is said to be performed that in turn improves the throughput and efficiency of cloud service. Then, with the objective of providing DC for the multi-casted data or messages, DCRNN model is applied. With the Non-linear Recurrent Neural Network using Logistic Activation Function, by handling complex non-linear relationships, average response time is said to be reduced

    QSpike tools: a generic framework for parallel batch preprocessing of extracellular neuronal signals recorded by substrate microelectrode arrays

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    Micro-Electrode Arrays (MEAs) have emerged as a mature technique to investigate brain (dys)functions in vivo and in in vitro animal models. Often referred to as “smart” Petri dishes, MEAs have demonstrated a great potential particularly for medium-throughput studies in vitro, both in academic and pharmaceutical industrial contexts. Enabling rapid comparison of ionic/pharmacological/genetic manipulations with control conditions, MEAs are employed to screen compounds by monitoring non-invasively the spontaneous and evoked neuronal electrical activity in longitudinal studies, with relatively inexpensive equipment. However, in order to acquire sufficient statistical significance, recordings last up to tens of minutes and generate large amount of raw data (e.g., 60 channels/MEA, 16 bits A/D conversion, 20 kHz sampling rate: approximately 8 GB/MEA,h uncompressed). Thus, when the experimental conditions to be tested are numerous, the availability of fast, standardized, and automated signal preprocessing becomes pivotal for any subsequent analysis and data archiving. To this aim, we developed an in-house cloud-computing system, named QSpike Tools, where CPU-intensive operations, required for preprocessing of each recorded channel (e.g., filtering, multi-unit activity detection, spike-sorting, etc.), are decomposed and batch-queued to a multi-core architecture or to a computers cluster. With the commercial availability of new and inexpensive high-density MEAs, we believe that disseminating QSpike Tools might facilitate its wide adoption and customization, and inspire the creation of community-supported cloud-computing facilities for MEAs users

    Design and Investigation of Genetic Algorithmic and Reinforcement Learning Approaches to Wire Crossing Reductions for pNML Devices

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    Perpendicular nanomagnet logic (pNML) is an emerging post-CMOS technology which encodes binary data in the polarization of single-domain nanomagnets and performs operations via fringing field interactions. Currently, there is no complete top-down workflow for pNML. Researchers must instead simultaneously handle place-and-route, timing, and logic minimization by hand. These tasks include multiple NP-Hard subproblems, and the lack of automated tools for solving them for pNML precludes the design of large-scale pNML circuits

    Study, evaluation and contributions to new algorithms for the embedding problem in a network virtualization environment

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    Network virtualization is recognized as an enabling technology for the future Internet. It aims to overcome the resistance of the current Internet to architectural change and to enable a new business model decoupling the network services from the underlying infrastructure. The problem of embedding virtual networks in a substrate network is the main resource allocation challenge in network virtualization and is usually referred to as the Virtual Network Embedding (VNE) problem. VNE deals with the allocation of virtual resources both in nodes and links. Therefore, it can be divided into two sub-problems: Virtual Node Mapping where virtual nodes have to be allocated in physical nodes and Virtual Link Mapping where virtual links connecting these virtual nodes have to be mapped to paths connecting the corresponding nodes in the substrate network. Application of network virtualization relies on algorithms that can instantiate virtualized networks on a substrate infrastructure, optimizing the layout for service-relevant metrics. This class of algorithms is commonly known as VNE algorithms. This thesis proposes a set of contributions to solve the research challenges of the VNE that have not been tackled by the research community. To do that, it performs a deep and comprehensive survey of virtual network embedding. The first research challenge identified is the lack of proposals to solve the virtual link mapping stage of VNE using single path in the physical network. As this problem is NP-hard, existing proposals solve it using well known shortest path algorithms that limit the mapping considering just one constraint. This thesis proposes the use of a mathematical multi-constraint routing framework called paths algebra to solve the virtual link mapping stage. Besides, the thesis introduces a new demand caused by virtual link demands into physical nodes acting as intermediate (hidden) hops in a path of the physical network. Most of the current VNE approaches are centralized. They suffer of scalability issues and provide a single point of failure. In addition, they are not able to embed virtual network requests arriving at the same time in parallel. To solve this challenge, this thesis proposes a distributed, parallel and universal virtual network embedding framework. The proposed framework can be used to run any existing embedding algorithm in a distributed way. Thereby, computational load for embedding multiple virtual networks is spread across the substrate network Energy efficiency is one of the main challenges in future networking environments. Network virtualization can be used to tackle this problem by sharing hardware, instead of requiring dedicated hardware for each instance. Until now, VNE algorithms do not consider energy as a factor for the mapping. This thesis introduces the energy aware VNE where the main objective is to switch off as many network nodes and interfaces as possible by allocating the virtual demands to a consolidated subset of active physical networking equipment. To evaluate and validate the aforementioned VNE proposals, this thesis helped in the development of a software framework called ALgorithms for Embedding VIrtual Networks (ALEVIN). ALEVIN allows to easily implement, evaluate and compare different VNE algorithms according to a set of metrics, which evaluate the algorithms and compute their results on a given scenario for arbitrary parameters
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