ترغب بنشر مسار تعليمي؟ اضغط هنا

Performance-Driven Internet Path Selection

74   0   0.0 ( 0 )
 نشر من قبل Maria Apostolaki
 تاريخ النشر 2020
  مجال البحث الهندسة المعلوماتية
والبحث باللغة English




اسأل ChatGPT حول البحث

Internet routing can often be sub-optimal, with the chosen routes providing worse performance than other available policy-compliant routes. This stems from the lack of visibility into route performance at the network layer. While this is an old problem, we argue that recent advances in programmable hardware finally open up the possibility of performance-aware routing in a deployable, BGP-compatible manner. We introduce ROUTESCOUT, a hybrid hardware/software system supporting performance-based routing at ISP scale. In the data plane, ROUTESCOUT leverages P4-enabled hardware to monitor performance across policy-compliant route choices for each destination, at line-rate and with a small memory footprint. ROUTESCOUTs control plane then asynchronously pulls aggregated performance metrics to synthesize a performance-aware forwarding policy. We show that ROUTESCOUT can monitor performance across most of an ISPs traffic, using only 4 MB of memory. Further, its control can flexibly satisfy a variety of operator objectives, with sub-second operating times.



قيم البحث

اقرأ أيضاً

By delegating path control to end-hosts, future Internet architectures offer flexibility for path selection. However, there is a concern that the distributed routing decisions by end-hosts, in particular load-adaptive routing, can lead to oscillation s if path selection is performed without coordination or accurate load information. Prior research has addressed this problem by devising path-selection policies that lead to stability. However, little is known about the viability of these policies in the Internet context, where selfish end-hosts can deviate from a prescribed policy if such a deviation is beneficial fromtheir individual perspective. In order to achieve network stability in future Internet architectures, it is essential that end-hosts have an incentive to adopt a stability-oriented path-selection policy. In this work, we perform the first incentive analysis of the stability-inducing path-selection policies proposed in the literature. Building on a game-theoretic model of end-host path selection, we show that these policies are in fact incompatible with the self-interest of end-hosts, as these strategies make it worthwhile to pursue an oscillatory path-selection strategy. Therefore, stability in networks with selfish end-hosts must be enforced by incentive-compatible mechanisms. We present two such mechanisms and formally prove their incentive compatibility.
In various contexts of networking research, end-host path selection has recently regained momentum as a design principle. While such path selection has the potential to increase performance and security of networks, there is a prominent concern that it could also lead to network instability (i.e., flow-volume oscillation) if paths are selected in a greedy, load-adaptive fashion. However, the extent and the impact vectors of instability caused by path selection are rarely concretized or quantified, which is essential to discuss the merits and drawbacks of end-host path selection. In this work, we investigate the effect of end-host path selection on various metrics of networks both qualitatively and quantitatively. To achieve general and fundamental insights, we leverage the recently introduced axiomatic perspective on congestion control and adapt it to accommodate joint algorithms for path selection and congestion control, i.e., multi-path congestion-control protocols. Using this approach, we identify equilibria of the multi-path congestion-control dynamics and analytically characterize these equilibria with respect to important metrics of interest in networks (the axioms) such as efficiency, fairness, and loss avoidance. Moreover, we analyze how these axiomatic ratings for a general network change compared to a scenario without path selection, thereby obtaining an interpretable and quantititative formalization of the performance impact of end-host path-selection. Finally, we show that there is a fundamental trade-off in multi-path congestion-control protocol design between efficiency, stability, and loss avoidance on one side and fairness and responsiveness on the other side.
In paper the method for estimation of available bandwidth is supposed which does not demand the advanced utilities. Our method is based on the measurement of network delay $D$ for packets of different sizes $W$. The simple expression for available ba ndwidth $B_{av} =(W_2-W_1)/(D_2-D_1)$ is substantiated. For the experimental testing the measurement infrastructure for Russian segment of Internet was installed in framework of RFBR grant 06-07-89074.
To keep up with demand, servers will scale up to handle hundreds of thousands of clients simultaneously. Much of the focus of the community has been on scaling servers in terms of aggregate traffic intensity (packets transmitted per second). However, bottlenecks caused by the increasing number of concurrent clients, resulting in a large number of concurrent flows, have received little attention. In this work, we focus on identifying such bottlenecks. In particular, we define two broad categories of problems; namely, admitting more packets into the network stack than can be handled efficiently, and increasing per-packet overhead within the stack. We show that these problems contribute to high CPU usage and network performance degradation in terms of aggregate throughput and RTT. Our measurement and analysis are performed in the context of the Linux networking stack, the the most widely used publicly available networking stack. Further, we discuss the relevance of our findings to other network stacks. The goal of our work is to highlight considerations required in the design of future networking stacks to enable efficient handling of large numbers of clients and flows.
323 - Jinfa Wang , Siyuan Jia , Hai Zhao 2017
Detecting the anomaly behaviors such as network failure or Internet intentional attack in the large-scale Internet is a vital but challenging task. While numerous techniques have been developed based on Internet traffic in past years, anomaly detecti on for structured datasets by complex network have just been of focus recently. In this paper, a anomaly detection method for large-scale Internet topology is proposed by considering the changes of network crashes. In order to quantify the dynamic changes of Internet topology, the network path changes coefficient(NPCC) is put forward which will highlight the Internet abnormal state after it is attacked continuously. Furthermore we proposed the decision function which is inspired by Fibonacci Sequence to determine whether the Internet is abnormal or not. That is the current Internet is abnormal if its NPCC is beyond the normal domain which structured by the previous k NPCCs of Internet topology. Finally the new Internet anomaly detection method was tested over the topology data of three Internet anomaly events. The results show that the detection accuracy of all events are over 97%, the detection precision of each event are 90.24%, 83.33% and 66.67%, when k = 36. According to the experimental values of the index F_1, we found the the better the detection performance is, the bigger the k is, and our method has better performance for the anomaly behaviors caused by network failure than that caused by intentional attack. Compared with traditional anomaly detection, our work may be more simple and powerful for the government or organization in items of detecting large-scale abnormal events.
التعليقات
جاري جلب التعليقات جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
mircosoft-partner

هل ترغب بارسال اشعارات عن اخر التحديثات في شمرا-اكاديميا