Flows In High-speed Networks Research Articles

SPArch: A Hardware-oriented Sketch-based Architecture for High-speed Network Flow Measurements

Network flow measurement is an integral part of modern high-speed applications for network security and data-stream processing. However, processing at line rate while maintaining the required data structure within the on-chip memory of the hardware platform is a challenging task for measurement algorithms, especially when accuracy is of primary importance, such as in network security applications. Most of the existing measurement algorithms are no exception to such issues when deployed in high-speed networking environments and are also not tailored for efficient hardware implementation. Sketch-based measurement algorithms minimize the memory requirement and are suitable for high-speed networks but possess a low memory-accuracy trade-off and lack the versatility of individual flow mapping. To address these challenges, we present a hardware-friendly data structure named Sketch-based Pseudo-associative array Architecture (SPArch). SPArch is highly accurate and extremely memory-efficient, making it suitable for network flow measurement and security applications. The parallelism in SPArch ensures minimal and constant memory access cycles. Unlike other sketch architectures, SPArch provides the functionality of individual flow mapping similar to associative arrays, and the optimized version of SPArch allows the organization of counters in multiple buckets based on the flow sizes. An in-depth analysis of SPArch is carried out in this article and implemented SPArch on the Alveo data center accelerator card, demonstrating its suitability for high-speed networks.

Jigsaw-Sketch: a fast and accurate algorithm for finding top-k elephant flows in high-speed networks

Jigsaw-Sketch: a fast and accurate algorithm for finding top-k elephant flows in high-speed networks

ActiveKeeper: An Accurate and Efficient Algorithm for Finding Top-k Elephant Flows

Finding top- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> elephant flows in high-speed network is a crucial task for network traffic measurement. Since modern routers do not hold enough on-chip memory to precisely track all flow sizes, identifying elephant flows becomes more challenging than ever. Existing solutions mostly use compact data structures to fit in limited on-chip memory. However, they have to use large-size counters, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , 20-bit counter, to prevent overflows when recording flow sizes. This wastes significant on-chip memory since flow size distribution is highly skewed. Most counters only require 8 bits to track mouse flows rather than 20 bits. In this letter, we propose ActiveKeeper, an efficient top- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> finding solution that uses two-mode active counter to record flow sizes, reducing memory usage while ensuring high accuracy. We also introduce dynamically exponential decay to increase the precision in expelling the small flows while holding the large ones. Experimental results based on real-world Internet traces show that our method outperforms the state-of-the-art by achieving high accuracy and higher precision with small on-chip memory usage.

Frequent Items Mining Algorithm Over High Speed Network Flows Based on Double Hash Method

In the high-speed backbone network, with the increasing speed of network link, the number of network flows increase rapidly. Meanwhile, with restrictions on hardware computing and storage resources, so, how to identify and measure large flows timely and accurately in massive data become a hot issue in high speed network flow measurement area. In this paper, we propose a new algorithm based on double hash algorithm to realize large flow frequent items identification, according to the defect of MF algorithm which produces false positive easily and frequent updates to bring the huge pressure to the system. The complexity and false positive rate of the algorithm was analyzed. The effect of large flow frequent items statistical accuracy and discard rate for parameter configuration was analyzed through simulation. The theoretical analysis and the simulation result indicate that compare to MF algorithm, our algorithm can identify large flow frequent items more accurately, and satisfies the need of actual measurement.

Technique for Estimating the Number of Active Flows in High-Speed Networks

The online collection of coarse-grained traffic information, such as the total number of flows, is gaining in importance due to a wide range of applications, such as congestion control and network security. In this paper, we focus on an active queue management scheme called SRED since it estimates the number of active flows and uses the quantity to indicate the level of congestion. However, SRED has several limitations, such as instability in estimating the number of active flows and underestimation of active flows in the presence of non-responsive traffic. We present a Markov model to examine the capability of SRED in estimating the number of flows. We show how the SRED cache hit rate can be used to quantify the number of active flows. We then propose a modified SRED scheme, called hash-based two-level caching (HaTCh), which uses hashing and a two-level caching mechanism to accurately estimate the number of active flows under various workloads. Simulation results indicate that the proposed scheme provides a more accurate estimation of the number of active flows than SRED, stabilizes the estimation with respect to workload fluctuations, and prevents performance degradation by efficiently isolating non-responsive flows.

Performance Analysis and Improvement of HighSpeed TCP with TailDrop/RED Routers

Continuous and explosive growth of the Internet has shown that current TCP mechanisms can obstruct efficient use of high-speed, long-delay networks. To address this problem we propose an enhanced transport-layer protocol called gHSTCP, based on Highspeed TCP proposed by Sally Floyd. It uses two modes in the congestion avoidance phase based on the changing trend of RTT. Simulation results show gHSTCP can significantly improve performance in mixed environments, in terms of throughput and fairness against the traditional TCP Reno flows. However, the performance improvement is limited due to the nature of TailDrop router, and the RED/ARED routers can not alleviate the problem completely. Therefore, we present a modified version of Adaptive RED, called gARED, directed at the problem of simultaneous packet drops by multiple flows in high speed networks. gARED can eliminate weaknesses found in Adaptive RED by monitoring the trend in variation of the average queue length of the router buffer. Our approach, combining gARED and gHSTCP, is quite effective and fair to competing traffic than Adaptive RED with Highspeed TCP.

A probability-guaranteed adaptive timeout algorithm for high-speed network flow detection

Collecting network traffic is becoming a more challenging task in passive network measurement due to the rapid growth of link speed. Flow-based network traffic capture and storage provides an efficient way for high-speed network measurement. The paper concentrates on the flow detection issue which is also the premise for further flow-based traffic analysis and modeling in such challenging environment. Based on the statistical investigation of the correlations between flow size and the maximum packet interarrival time within a flow, we obtain the empirical conditional distribution functions for some popular TCP protocol-based application flows, and then propose a Probability-Guaranteed Adaptive Timeout algorithm (PGAT) for flow termination decision. The assessment criteria for flow termination decision algorithm is systematically developed. Comparisons on flow generation ratio, flow intact ratio, and mean flow extra retaining time metrics indicate that the PGAT algorithm can obtain more attractive performance than other related works.

Providing End-to-End Quality of Service with Optimal Least Weight Routing in Next-Generation Multiservice High-Speed Networks

Routing algorithms play a critical role in meeting both the stringent quality of service (QoS) requirements of guaranteed services and the certain QoS requirement of assured services over next-generation multiservice high-speed networks. In this paper, we propose the use of optimal least weight routing (OLWR) algorithm for routing QoS flows in high-speed networks. The main principle of our algorithm is that the choice of the most appropriate route is based on a set of parameters (least weight parameters) that estimate and consider the impact that the acceptance and routing decision of a call request belonging to a specific class would have on the network and other classes of service. Effective bandwidth, bandwidth and trunk reservation techniques, along with load balancing and packing trade-off considerations, are also introduced in the proposed routing algorithm. The performance evaluation of our algorithm is achieved via modeling and simulation of multiclass service routing in various network topologies. The performance results demonstrated that OLWR outperforms both the multihop least-loaded routing algorithms and the multihop most-loaded routing algorithms in terms of both revenue and carried load.

A stochastic reward net model for performance analysis of prioritized DQDB MAN

The performance of prioritized distributed queue dual bus (DQDB) metropolitan area network (MAN) under bursty traffic environment is studied in this article. The tagged node model is adopted to simplify the analysis. The processes of the packet arrivals to the tagged node, the empty-slot pattern from the upstream, and the request pattern from the downstream are assumed to be Markov Modulated Poisson Processes, a more general and appropriate model to capture the bursty characteristics existing in the traffic flows in high-speed networks. A stochastic reward nets model for the prioritized DQDB MAN is presented. With the help of the stochastic petri net package developed in Duke University, the steady state and as well as the transient behavior of packet loss ratio of tagged node are investigated. The influence of the burstiness of the empty-slot pattern, packet arrival and request pattern on the tagged node’s performance are examined.

Time-shift scheduling-fair scheduling of flows in high-speed networks

We present a scheduling protocol, called time-shift scheduling, to forward packets from multiple input flows to a single output channel. Each input flow is guaranteed a predetermined packet rate and an upper bound on packet delay. The protocol is an improvement over existing protocols because it satisfies the properties of rate-proportional delay, fairness, and efficiency, while existing protocols fail to satisfy at least one of these properties. In time-shift scheduling each flow is assigned an increasing timestamp, and the packet chosen for transmission is taken from the flow with the least timestamp. The protocol features the novel technique of time shifting, in which the scheduler's real-time clock is adjusted to prevent flow timestamps from increasing faster than the real-time clock. This bounds the difference between any pair of flow timestamps, thus ensuring the fair scheduling of flows.