Since its formal introduction to IP networks in 1993 as a viable complementary approach for congestion control, there has been a steady stream of research output with respect to Active Queue Management (AQM). This survey attempts to travel the trajectory of AQM research from 1993 with the first algorithm, Random Early Detection (RED), to current work in 2011. In this survey we discuss the general attributes of AQM schemes, and the design approaches taken such as heuristic, control-theoretic and deterministic optimization. Of interest is the role of AQM in QoS provisioning particularly in the DiffServ context, as well as the role of AQM in the wireless domain. For each section, example algorithms from the research literature are presented.

Active Queue Management: A Survey

In recent years service curves have proven a powerful and versatile model for performance analysis of network elements, such as schedulers, links, and traffic shapers, up to entire computer networks, like the Internet. The elegance of the concept of service curve is due to intuitive convolution formulas that determine the data departures of a system from its arrivals and its service curve. This fundamental relation constitutes the basis of the network calculus and relates it to systems theory, however, under a different, so-called min-plus algebra. As in systems theory, the particular strength of the minplus convolution is the ability to concatenate tandem systems along a network path. This facilitates the notion of network service curve that has the expressiveness to characterize whole networks by a single transfer function. This paper surveys the state-of-the-art of the deterministic and the recent probabilistic network calculus. It discusses the concept of service curves, its use in the network calculus, and the relation to systems theory under the min-plus algebra. Service curve models of common schedulers and different types of networks are reviewed and methods for identification of a system's service curve representation from measurements are discussed. After recapitulating the state of knowledge on time-varying min-plus systems theory, stochastic service curve models are surveyed. These models allow utilizing the statistical multiplexing gain in a network calculus framework that features end-to-end network analysis by convolution of service curves.

Survey of deterministic and stochastic service curve models in the network calculus

The configuration of large-scale networks is known to be difficult and error-prone. It is a low-level device-specific task and has to deal with subtle dependencies between multiple devices across a network. Network misconfiguration is a key cause of network disruptions and may also lead to security problems in networks. The complexity of network configuration is rapidly increasing as configurations change over time; as a result, there are more human errors that greatly degrade the connectivity of networks and increase management costs. 
To reduce the complexity of network configuration, we propose four network management modules: Verification, Simplification, Correlation/Visualization, and Classification. The Verification module consists of a complete configuration model and an automatic policy inference system. Using the model and the policy inference system, the Verification module evaluates a variety of network-wide policies, both within a single technology and across multiple technologies (e.g., packet filtering and routing policies). The Simplification module streamlines policies in a configuration so that the configurations are easier to understand and update; in this manner, it demonstrates the potential for improving comprehensibility of network configurations. The Correlation/Visualization module visualizes high-level, intended policies by correlating low-level configurations. This module helps operators to understand distributed low-level configurations more quickly and accurately. Finally, the Classification module identifies critical elements in a network. This identification allows operators to focus their time on higher-priority problems, thus reducing the complexity of network management. 
We implement the four network management modules and evaluate their effectiveness with configurations from four production networks. The Verification module discovers more than a hundred errors that are confirmed and corrected by the network administrators. Some of these misconfigurations can result in loss of connectivity, access to protected networks, and financial implications by providing free transit services. The Simplification module reduces up to 70% of commands related to routing policies. We also go over a few reduction types and show that such simplification does improve the manageability of the configuration. The Correlation/Visualization module decreases operation and service deployment time from hours to minutes and increased its accuracy from 70% to nearly 100%. The Classification module identifies configurations that impact route advertisements to more than 100 peers. This module also finds routing sessions that result in more than 100 GB of loss within a few seconds if not properly protected. 
We believe that our systems significantly improve accuracy and reduce time of network configuration. The proposed ideas can benefit many different types of networks, especially in large installations, such as service provider networks, enterprise networks, data-center networks, and power grids.

Reducing complexity of large-scale network configuration management

Internet routers today can be overwhelmed by a large number of BGP updates triggered by events such as session resets, link failures, and policy changes. Such excessive updates can delay routing convergence, which, in turn, degrades the performance of delay- and jitter-sensitive applications. This paper proposes a simple and novel idea of differentiated processing of BGP updates to reduce routers' load and improve routing convergence without changing the protocol semantics. Based on a set of criteria, BGP updates are grouped into different priority classes. Higher-priority updates are processed and propagated sooner, while lower-priority ones, not affecting routing decisions, can be delayed to both reduce routers' load and improve routing convergence. We first present a general methodology for update classification, update processing, and priority-state inference. By analyzing real BGP data obtained from Route Views, we show that our update classification is feasible and beneficial. We further propose two differentiated update processing (DUP) algorithms and evaluate them using the SSFNet BGP simulator on several realistic network topologies. The algorithms are shown to be very effective for large networks, yielding 30% fewer updates and reducing convergence time by 80%. Our scheme is simple and light-weight with little added processing overhead. It can be deployed incrementally, since BGP messages are not modified and every BGP router makes routing decisions independently.

https://www.ieee-icnp.org/2006/papers/s8a2.pdf

Differentiated BGP Update Processing for Improved Routing Convergence

This paper proposes a channel-adapted and buffer-aware scheduling algorithm in LTE wireless communication system. The scheme allocates system resource jointly PHY-MAC layer according to CQI feed back with the uplink, considers UE buffer status to avoid buffer overflow, and guarantees certain fairness among users. Comparing with traditional scheduling algorithms such as RR, PO and PF, the simulation results show that the proposed mechanism can achieve higher system throughput and less packet loss.

Channel-Adapted and Buffer-Aware Packet Scheduling in LTE Wireless Communication System

In this paper, we studied TCP performance under aggregate fair queueing (FQ). We first showed the livelock problem of FQ under the DropFront scheme, and proposed a simple solution to the problem. Then, to improve the fairness of aggregate FQ, we proposed a new active buffer-management scheme called ALQD+ (approximated longest queue drop), which is fairer than ALQD. Based on these two enhancements, via simulation, we showed that aggregate FQ provides not only excellent scalability (small scheduling and state-maintenance overheads) but also good performance, especially throughput. In addition to the simulation-based evaluation, we provided a simple performance analysis.

TCP performance under aggregate fair queueing

This paper evaluates, via both analysis and simulation, the end-to-end (e2e) delay performance of aggregate scheduling with guaranteed-rate (GR) algorithms. Deterministic e2e delay bounds for a single aggregation are derived under the assumption that all incoming flows at an aggregator conform to the token bucket model. An aggregator can use any of three types of GR scheduling algorithms: stand-alone GR, two-level hierarchical GR, and rate-controlled two-level hierarchical GR. E2e delay bounds are also derived for the case of multiple aggregations within an aggregation region when aggregators use the rate-controlled two-level hierarchical GR. By using the GR scheduling algorithms for traffic aggregates, we show not only the existence of delay bounds for each flow, but also the fact that, under certain conditions (e.g., when the aggregate traverses a long path after the aggregation point), the bounds are smaller than that of per-flow scheduling. We then compare the analytic delay bounds numerically and conduct in-depth simulation to: 1) confirm the analytic results and 2) compare the e2e delays of aggregate and per-flow scheduling. The simulation results have shown that aggregate scheduling is very robust and can exploit statistical multiplexing gains. It performs better than per-flow scheduling in most of the simulation scenarios we considered. Overall, aggregate scheduling is shown theoretically to provide bounded e2e delays and practically to provide excellent e2e delay performance. Moreover, it incurs lower scheduling and state-maintenance overheads at routers than per-flow scheduling. All of these salient features make aggregate scheduling very attractive for use in Internet core networks.

/pdf/end-to-end-delay-bounds-for-traffic-aggregates-under-m7bjio46lb.pdf

End-to-end delay bounds for traffic aggregates under guaranteed-rate scheduling algorithms

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Differentiated BGP Update Processing for Improved Routing Convergence

TCP performance under aggregate fair queueing

End-to-end delay bounds for traffic aggregates under guaranteed-rate scheduling algorithms