Simulation and experimental evaluation of a flexible time triggered ethernet architecture applied in satellite nano/micro launchers

The success of small satellites has led to the study of new technologies for the realization of Nano and Micro Launch Vehicle (NMLV) in order to make competitive launch costs. The paper has the objective to define and experimentally investigate the performance of a communication system for NMLV interconnecting the End Systems as On-Board Computer (OBC), telemetry apparatus, Navigation Unit...we propose a low cost Ethernet-based solution able to provide the devices with high interconnection bandwidth. To guarantee hard delays to the Guide, Navigation and Control applications we propose some architectural changes of the traditional Ethernet network with the introduction of a layer implemented in the End Systems and allow for the lack of any contention on the network links. We show how the proposed solution has comparable performance to the one of TTEthernet standard that is a very expensive solution. An experimental test-bed equipped with Ethernet switches and Hercules boards by Texas Instruments is also provided to prove the feasibility of the proposed solution

evaluation of power saving and feasibility study of migrations solutions in a virtual router network

The power consumption of the network equipment has increased significantly and some strategies to contain the power used in the IP network are needed. Among the green networking strategies, the virtualization class and in particular the deployment of migrating virtual routers can lead to a high energy saving. It consists in migrating virtual routers in fewer physical nodes when the traffic decreases allowing for a power consumption saving. In this paper we formulate the problem of minimizing the power consumption as a Mixed Integer Linear Programming (MILP) problem. Due to the hard complexity of the introduced MILP problem, we propose a heuristic for the migration of virtual routers among physical devices in order to turn off as many nodes as possible and save power according to the compliance with network node and link capacity constraints. We show that 50% of nodes may be turned off in the case of a real provider network when traffic percentage reduction of 80% occurs. Finally we also perform a feasibility study by means of an experimental test-bed to evaluate migration time of a routing plane based on QUAGGA routing software

SOA-Based Optical Packet Switching Architectures

The service evolution and the rapid increase in traffic levels fuel the interest toward switching paradigms enabling the fast allocation of Wavelength Division Multiplexing WDM channels in an on demand fashion with fine granularities (microsecond scales). For this reason, in the last years, different optical switching paradigms have been proposed: optical-packet switching (OPS), optical-burst switching (OBS), wavelength-routed OBS, etc. Among the various all-optical switching paradigms, OPS attracts increasing attention. Owing to the high switching rate, Semiconductor Optical Amplifier (SOA) is a key technology to realize Optical Packet Switches. We propose some Optical Packet Switch (OPS) architectures and illustrate their realization in SOA technology. The effectiveness of the technology in reducing the power consumption is also analyzed. The chapter is organized in three sections. The main blocks (Switching Fabric, Wavelength Conversion stage, Synchronization stage) of an OPS are illustrated in Section 2 where we also show some examples of realizing wavelength converters and synchronizers in SOA technology. Section 3 introduces SOA-based single-stage and multi-stage switching fabrics. Finally the SOA-based OPS power consumption is investigated in Section 4

Reconfiguration of optical-NFV network architectures based on cloud resource allocation and QoS degradation cost-aware prediction techniques

The high time required for the deployment of cloud resources in Network Function Virtualization network architectures has led to the proposal and investigation of algorithms for predicting trafc or the necessary processing and memory resources. However, it is well known that whatever approach is taken, a prediction error is inevitable. Two types of prediction errors can occur that have a different impact on the increase in network operational costs. In case the predicted values are higher than the real ones, the resource allocation algorithms will allocate more resources than necessary with the consequent introduction of an over-provisioning cost. Conversely, when the predicted values are lower than the real values, the allocation of fewer resources will lead to a degradation of QoS and the introduction of an under-provisioning cost. When over-provisioning and under-provisioning costs are different, most of the prediction algorithms proposed in the literature are not adequate because they are based on minimizing the mean square error or symmetric cost functions. For this reason we propose and investigate a forecasting methodology in which it is introduced an asymmetric cost function capable of weighing the costs of over-provisioning and under-provisioning differently. We have applied the proposed forecasting methodology for resource allocation in a Network Function Virtualization architectures where the Network Function Virtualization Infrastructure Point-of-Presences are interconnected by an elastic optical network.We have veried a cost savings of 40% compared to solutions that provide a minimization of the mean square error

Proposal and investigation of an artificial intelligence (Ai)-based cloud resource allocation algorithm in network function virtualization architectures

The high time needed to reconfigure cloud resources in Network Function Virtualization network environments has led to the proposal of solutions in which a prediction based-resource allocation is performed. All of them are based on traffic or needed resource prediction with the minimization of symmetric loss functions like Mean Squared Error. When inevitable prediction errors are made, the prediction methodologies are not able to differently weigh positive and negative prediction errors that could impact the total network cost. In fact if the predicted traffic is higher than the real one then an over allocation cost, referred to as over-provisioning cost, will be paid by the network operator; conversely, in the opposite case, Quality of Service degradation cost, referred to as under-provisioning cost, will be due to compensate the users because of the resource under allocation. In this paper we propose and investigate a resource allocation strategy based on a Long Short Term Memory algorithm in which the training operation is based on the minimization of an asymmetric cost function that differently weighs the positive and negative prediction errors and the corresponding over-provisioning and under-provisioning costs. In a typical traffic and network scenario, the proposed solution allows for a cost saving by 30% with respect to the case of solution with symmetric cost function

Dynamic in-network classification for service function chaining ready SDN networks

Service Function Chaining (SFC) paradigm consists in steering traffic flows through an ordered set of Service Functions (SFs) so that to realize complex end to end services. SFC architecture introduces all the logical functions that need to be developed in order to provide the required service. The SFC overlay infrastructure can be built on top of many different underlay network technologies. The high flexibility and centrally controlled feature of Software Defined Networking (SDN), make SDN networks to be a perfect underlay to build the SFC architecture. Due to Ternary Content Address Memory (TCAM) limited size, SDN switches have a limitation in the number of flow rules that can be hosted. This constraint is particularly penalizing in case of the SFC classifier function, since it requires to manage a high number of different flows. The limitation imposed by the TCAM size on the SFC classifier can be a bottleneck for the number of SFC requests that the SDN-based SFC architecture can handle. In this paper we define the Dynamic Chain Request Classification Offloading (D-CRCO) problem, as the one of maximizing the number of accepted SFC requests, having the possibility of: i) implement the SFC classifier also in a node that is internal to the SDN-based SFC domain, and ii) install classification rules in a reactive fashion. Furthermore, we propose the Dynamic Nearest Node (DNN) heuristic to solve the D-CRCO problem. Performance evaluation shows that by using DNN heuristic it is possible to triple the number of accepted requests, with respect to existing solutions

Comparative analysis of power consumption in asynchronous wavelength modular optical switching fabrics

Next-generation optical routers will be designed to support the flexibility required by Future Internet services and, at the same time, to overcome the power consumption bottleneck which appears to limit throughput scalability in today routers. A model to evaluate average power consumption in asynchronous optical switching fabrics is here presented to compare these architectures with other synchronous and asynchronous solutions. The combination of wavelength modular switching fabrics with low spatial complexity and asynchronous operation is demonstrated to be the most power-efficient solution among those considered which employ wavelength converters, through presentation and discussion of a thorough set of numerical results. © 2011 Elsevier B.V. All rights reserved

Optical delay control of large-spectral-bandwidth laser pulses

In this letter we report the first experimental observation of temporal delay control of large-spectral-bandwidth multimode laser pulses by means of electromagnetically induced transparency (EIT). We achieved controllable retardation with limited temporal distortion of optical pulses with an input spectral bandwidth of 3.3 GHz. The experimental results compare favorably with theoretical predictions.Comment: Submitted to Optics Letters (January 2009

Elimination of quiescent/slow-proliferating cancer stem cells by Bcl-XL inhibition in non-small cell lung cancer

Lung cancer is the most common cause of cancer-related mortality worldwide, urging the discovery of novel molecular targets and therapeutic strategies. Stem cells have been recently isolated from non-small cell lung cancer (NSCLC), thus allowing the investigation of molecular pathways specifically active in the tumorigenic population. We have found that Bcl-XL is constantly expressed by lung cancer stem cells (LCSCs) and has a prominent role in regulating LCSC survival. Whereas chemotherapeutic agents were scarcely effective against LCSC, the small molecule Bcl-2/Bcl-XL inhibitor ABT-737, but not the selective Bcl-2 inhibitor ABT-199, induced LCSC death at nanomolar concentrations. Differently from gemcitabine, which preferentially eliminated proliferating LCSC, ABT-737 had an increased cytotoxic activity in vitro towards quiescent/slow-proliferating LCSC, which expressed high levels of Bcl-XL. In vivo, ABT-737 as a single agent was able to inhibit the growth of LCSC-derived xenografts and to reduce cancer stem cell content in treated tumors. Altogether, these results indicate that quiescent/slow-proliferating LCSC strongly depend on Bcl-XL for their survival and indicate Bcl-XL inhibition as a potential therapeutic avenue in NSCLC

The Side Population in Human Lung Cancer Cell Line NCI-H460 Is Enriched in Stem-Like Cancer Cells

Lung cancer is among the most lethal malignancies with a high metastasis and recurrence rate. Recent studies indicate that tumors contain a subset of stem-like cancer cells that possess certain stem cell properties. Herein, we used Hoechst 33342 dye efflux assay and flow cytometry to isolate and characterize the side population (SP) cells from human lung cancer cell line NCI-H460 (H460). We show that the H460 SP cells harbor stem-like cells as they can readily form anchorage-independent floating spheres, possess great proliferative potential, and exhibit enhanced tumorigenicity. Importantly, the H460 SP cells were able to self-renew both in vitro and in vivo. Finally, we show that the H460 SP cells preferentially express ABCG2 as well as SMO, a critical mediator of the Hedgehog (HH) signaling, which seems to play an important role in H460 lung cancer cells as its blockage using Cyclopamine greatly inhibits cell-cycle progression. Collectively, our results lend further support to the existence of lung cancer stem cells and also implicate HH signaling in regulating large-cell lung cancer (stem) cells