Seamless virtual network for international business continuity in presence of intentional blocks

東京電機大学201

SWIFT: Predictive Fast Reroute

Network operators often face the problem of remote outages in transit networks leading to significant (sometimes on the order of minutes) downtimes. The issue is that BGP, the Internet routing protocol, often converges slowly upon such outages, as large bursts of messages have to be processed and propagated router by router. In this paper, we present SWIFT, a fast-reroute framework which enables routers to restore connectivity in few seconds upon remote outages. SWIFT is based on two novel techniques. First, SWIFT deals with slow outage notification by predicting the overall extent of a remote failure out of few control-plane (BGP) messages. The key insight is that significant inference speed can be gained at the price of some accuracy. Second, SWIFT introduces a new data-plane encoding scheme, which enables quick and flexible update of the affected forwarding entries. SWIFT is deployable on existing devices, without modifying BGP. We present a complete implementation of SWIFT and demonstrate that it is both fast and accurate. In our experiments with real BGP traces, SWIFT predicts the extent of a remote outage in few seconds with an accuracy of ~90% and can restore connectivity for 99% of the affected destinations

Cross-Layer Optimization for Power-Efficient and Robust Digital Circuits and Systems

With the increasing digital services demand, performance and power-efficiency become vital requirements for digital circuits and systems. However, the enabling CMOS technology scaling has been facing significant challenges of device uncertainties, such as process, voltage, and temperature variations. To ensure system reliability, worst-case corner assumptions are usually made in each design level. However, the over-pessimistic worst-case margin leads to unnecessary power waste and performance loss as high as 2.2x. Since optimizations are traditionally confined to each specific level, those safe margins can hardly be properly exploited. To tackle the challenge, it is therefore advised in this Ph.D. thesis to perform a cross-layer optimization for digital signal processing circuits and systems, to achieve a global balance of power consumption and output quality. To conclude, the traditional over-pessimistic worst-case approach leads to huge power waste. In contrast, the adaptive voltage scaling approach saves power (25% for the CORDIC application) by providing a just-needed supply voltage. The power saving is maximized (46% for CORDIC) when a more aggressive voltage over-scaling scheme is applied. These sparsely occurred circuit errors produced by aggressive voltage over-scaling are mitigated by higher level error resilient designs. For functions like FFT and CORDIC, smart error mitigation schemes were proposed to enhance reliability (soft-errors and timing-errors, respectively). Applications like Massive MIMO systems are robust against lower level errors, thanks to the intrinsically redundant antennas. This property makes it applicable to embrace digital hardware that trades quality for power savings.Comment: 190 page

Detection and Mitigation of Impairments for Real-Time Multimedia Applications

Measures of Quality of Service (QoS) for multimedia services should focus on phenomena that are observable to the end-user. Metrics such as delay and loss may have little direct meaning to the end-user because knowledge of specific coding and/or adaptive techniques is required to translate delay and loss to the user-perceived performance. Impairment events, as defined in this dissertation, are observable by the end-users independent of coding, adaptive playout or packet loss concealment techniques employed by their multimedia applications. Methods for detecting real-time multimedia (RTM) impairment events from end-to-end measurements are developed here and evaluated using 26 days of PlanetLab measurements collected over nine different Internet paths. Furthermore, methods for detecting impairment-causing network events like route changes and congestion are also developed. The advanced detection techniques developed in this work can be used by applications to detect and match response to network events. The heuristics-based techniques for detecting congestion and route changes were evaluated using PlanetLab measurements. It was found that Congestion events occurred for 6-8 hours during the days on weekdays on two paths. The heuristics-based route change detection algorithm detected 71\% of the visible layer 2 route changes and did not detect the events that occurred too close together in time or the events for which the minimum RTT change was small. A practical model-based route change detector named the parameter unaware detector (PUD) is also developed in this deissertation because it was expected that model-based detectors would perform better than the heuristics-based detector. Also, the optimal detector named the parameter aware detector (PAD) is developed and is useful because it provides the upper bound on the performance of any detector. The analysis for predicting the performance of PAD is another important contribution of this work. Simulation results prove that the model-based PUD algorithm has acceptable performance over a larger region of the parameter space than the heuristics-based algorithm and this difference in performance increases with an increase in the window size. Also, it is shown that both practical algorithms have a smaller acceptable performance region compared to the optimal algorithm. The model-based algorithms proposed in this dissertation are based on the assumption that RTTs have a Gamma density function. This Gamma distribution assumption may not hold when there are wireless links in the path. A study of CDMA 1xEVDO networks was initiated to understand the delay characteristics of these networks. During this study, it was found that the widely deployed proportional-fair (PF) scheduler can be corrupted accidentally or deliberately to cause RTM impairments. This is demonstrated using measurements conducted over both in-lab and deployed CDMA 1xEVDO networks. A new variant to PF that solves the impairment vulnerability of the PF algorithm is proposed and evaluated using ns-2 simulations. It is shown that this new scheduler solution together with a new adaptive-alpha initialization stratergy reduces the starvation problem of the PF algorithm

Detection and Mitigation of Impairments for Real-Time Multimedia Applications

Measures of Quality of Service (QoS) for multimedia services should focus on phenomena that are observable to the end-user. Metrics such as delay and loss may have little direct meaning to the end-user because knowledge of specific coding and/or adaptive techniques is required to translate delay and loss to the user-perceived performance. Impairment events, as defined in this dissertation, are observable by the end-users independent of coding, adaptive playout or packet loss concealment techniques employed by their multimedia applications. Methods for detecting real-time multimedia (RTM) impairment events from end-to-end measurements are developed here and evaluated using 26 days of PlanetLab measurements collected over nine different Internet paths. Furthermore, methods for detecting impairment-causing network events like route changes and congestion are also developed. The advanced detection techniques developed in this work can be used by applications to detect and match response to network events. The heuristics-based techniques for detecting congestion and route changes were evaluated using PlanetLab measurements. It was found that Congestion events occurred for 6-8 hours during the days on weekdays on two paths. The heuristics-based route change detection algorithm detected 71\% of the visible layer 2 route changes and did not detect the events that occurred too close together in time or the events for which the minimum RTT change was small. A practical model-based route change detector named the parameter unaware detector (PUD) is also developed in this deissertation because it was expected that model-based detectors would perform better than the heuristics-based detector. Also, the optimal detector named the parameter aware detector (PAD) is developed and is useful because it provides the upper bound on the performance of any detector. The analysis for predicting the performance of PAD is another important contribution of this work. Simulation results prove that the model-based PUD algorithm has acceptable performance over a larger region of the parameter space than the heuristics-based algorithm and this difference in performance increases with an increase in the window size. Also, it is shown that both practical algorithms have a smaller acceptable performance region compared to the optimal algorithm. The model-based algorithms proposed in this dissertation are based on the assumption that RTTs have a Gamma density function. This Gamma distribution assumption may not hold when there are wireless links in the path. A study of CDMA 1xEVDO networks was initiated to understand the delay characteristics of these networks. During this study, it was found that the widely deployed proportional-fair (PF) scheduler can be corrupted accidentally or deliberately to cause RTM impairments. This is demonstrated using measurements conducted over both in-lab and deployed CDMA 1xEVDO networks. A new variant to PF that solves the impairment vulnerability of the PF algorithm is proposed and evaluated using ns-2 simulations. It is shown that this new scheduler solution together with a new adaptive-alpha initialization stratergy reduces the starvation problem of the PF algorithm

Profile-based Resource Allocation for Virtualized Network Functions

Accepted in IEEE TNSM Journalhttps://ieeexplore.ieee.org/document/8848599International audienceThe virtualization of compute and network resources enables an unseen flexibility for deploying network services. A wide spectrum of emerging technologies allows an ever-growing range of orchestration possibilities in cloud-based environments. But in this context it remains challenging to rhyme dynamic cloud configurations with deterministic performance. The service operator must somehow map the performance specification in the Service Level Agreement (SLA) to an adequate resource allocation in the virtualized infrastructure. We propose the use of a VNF profile to alleviate this process. This is illustrated by profiling the performance of four example network functions (a virtual router, switch, firewall and cache server) under varying workloads and resource configurations. We then compare several methods to derive a model from the profiled datasets. We select the most accurate method to further train a model which predicts the services' performance, in function of incoming workload and allocated resources. Our presented method can offer the service operator a recommended resource allocation for the targeted service, in function of the targeted performance and maximum workload specified in the SLA. This helps to deploy the softwarized service with an optimal amount of resources to meet the SLA requirements, thereby avoiding unnecessary scaling steps

Smart Traffic Operation: from Human-Driven Cars to Mixed Vehicle Autonomy

The goal of my research is to enhance urban mobility by developing reliable and efficient traffic control and management strategies. As cities grow everywhere, and urban roadways become overburdened, the need for the development of such strategies becomes more evident. With the prevalence of smart sensing devices, such as smart phones and smart intersections, cities are becoming smart. Moreover, with the emergence of new and inevitable technologies, such as autonomous and connected vehicles, electric vehicles, and mobility on demand systems, smart cities are rapidly evolving. As we experience the arrival of such technologies, there is an opportunity to reclaim urban mobility. However, a blind utilization of these technologies may deflect us from reaching this goal. In this dissertation, we study the efficient operation of smart cities via management strategies that can guarantee overall societal benefits both in the cities of today and future.We focus on two natural instances of this agenda. In the first part, we tackle some of the existing challenges in the smart operation of traffic networks which are solely shared by human-driven cars. If all vehicles are human-driven, there is room for improving the efficiency of traffic networks by appropriate coordination and control of traffic signal lights. For these networks, we develop signal control algorithms that are capable of minimizing the number of stop-and-go movements, encoding fairness among vehicular arrivals, and are robust to the knowledge of system parameters. In the second part, we analyze fundamentals of traffic networks with mixed vehicle autonomy, where both human-driven and autonomous cars coexist on roadways. We study the mobility implications of selfish autonomy, i.e. autonomous cars that are not concerned about their overall impact and simply attempt to optimize their own travel benefits. Having shown the negative consequences that the increased deployment of selfish autonomy may have, we develop a pricing mechanism which can guarantee the overall societal-scale efficiency of traffic networks with mixed vehicle autonomy. Finally, we show how autonomy can act altruistically, i.e. by taking into account the decision making process of humans, autonomous cars can potentially plan for their actions in the favor of the overall good