Counter-intuitive throughput behaviors in networks under end-to-end control

It has been shown that as long as traffic sources adapt their rates to aggregate congestion measure in their paths, they implicitly maximize certain utility. In this paper we study some counter-intuitive throughput behaviors in such networks, pertaining to whether a fair allocation is always inefficient and whether increasing capacity always raises aggregate throughput. A bandwidth allocation policy can be defined in terms of a class of utility functions parameterized by a scalar a that can be interpreted as a quantitative measure of fairness. An allocation is fair if alpha is large and efficient if aggregate throughput is large. All examples in the literature suggest that a fair allocation is necessarily inefficient. We characterize exactly the tradeoff between fairness and throughput in general networks. The characterization allows us both to produce the first counter-example and trivially explain all the previous supporting examples. Surprisingly, our counter-example has the property that a fairer allocation is always more efficient. In particular it implies that maxmin fairness can achieve a higher throughput than proportional fairness. Intuitively, we might expect that increasing link capacities always raises aggregate throughput. We show that not only can throughput be reduced when some link increases its capacity, more strikingly, it can also be reduced when all links increase their capacities by the same amount. If all links increase their capacities proportionally, however, throughput will indeed increase. These examples demonstrate the intricate interactions among sources in a network setting that are missing in a single-link topology

Random Access Game in Fading Channels with Capture: Equilibria and Braess-like Paradoxes

The Nash equilibrium point of the transmission probabilities in a slotted ALOHA system with selfish nodes is analyzed. The system consists of a finite number of heterogeneous nodes, each trying to minimize its average transmission probability (or power investment) selfishly while meeting its average throughput demand over the shared wireless channel to a common base station (BS). We use a game-theoretic approach to analyze the network under two reception models: one is called power capture, the other is called signal to interference plus noise ratio (SINR) capture. It is shown that, in some situations, Braess-like paradoxes may occur. That is, the performance of the system may become worse instead of better when channel state information (CSI) is available at the selfish nodes. In particular, for homogeneous nodes, we analytically present that Braess-like paradoxes occur in the power capture model, and in the SINR capture model with the capture ratio larger than one and the noise to signal ratio sufficiently small.Comment: 30 pages, 5 figure

Effect of Selfish Behavior on Power Consumption in Mobile Ad Hoc Network

A multi hop mobile ad hoc network is a peer to peer network of wireless nodes where nodes are required to perform routing activity to provide end to end connectivity among nodes. As mobile nodes are constrained by battery power and bandwidth, some nodes may behave selfishly and deny forwarding packets for other nodes, even though they expect other nodes to forward packets to keep network connected. We simulate two selfish behaviors on top of Dynamic Source Routing (DSR) protocol: the first, selfish nodes do not forward data or control packets (routing packets) for other nodes and the second, selfish nodes turn off their network interface card when they have nothing to communicate. We compare the energy saving to the selfish nodes for both the misbehaviors and show that the second selfish behavior saves more energy. This is important result because most of the cooperation enforcement mechanisms in literature, except PCOM [2], address the first selfish behavior. Also, the second selfish behavior can be easily done by layman users without any protocol level changes. Secondly, with our simulation study we find that in dense mobile ad hoc networks where route breakages are frequent, routing control packets consume significant fraction of node energy and selfish behavior by certain number of nodes reduce the overall routing overhead in network which in turn result in energy saving for both, well behaving nodes and selfish nodes

Modeling Data-Plane Power Consumption of Future Internet Architectures

With current efforts to design Future Internet Architectures (FIAs), the evaluation and comparison of different proposals is an interesting research challenge. Previously, metrics such as bandwidth or latency have commonly been used to compare FIAs to IP networks. We suggest the use of power consumption as a metric to compare FIAs. While low power consumption is an important goal in its own right (as lower energy use translates to smaller environmental impact as well as lower operating costs), power consumption can also serve as a proxy for other metrics such as bandwidth and processor load. Lacking power consumption statistics about either commodity FIA routers or widely deployed FIA testbeds, we propose models for power consumption of FIA routers. Based on our models, we simulate scenarios for measuring power consumption of content delivery in different FIAs. Specifically, we address two questions: 1) which of the proposed FIA candidates achieves the lowest energy footprint; and 2) which set of design choices yields a power-efficient network architecture? Although the lack of real-world data makes numerous assumptions necessary for our analysis, we explore the uncertainty of our calculations through sensitivity analysis of input parameters

Enhancing Automated Network Management

Network management benefits from automated tools. With the recent advent of software-defined principles, automated tools have been proposed from both industry and academia to fulfill function components in the network management control loop. While automation aims to accommodate the ever increasing network diversity and dynamics with improved reliability and management efficiency, it also brings new concerns as it’s becoming more difficult to understand the control of the network and operators cannot rely on traditional troubleshooting tools. Meanwhile, how to effectively integrate new automation tools with existing legacy networks remains a question. This dissertationpresents efficient methods to address key functionalities within the control loop in the adaption of automated network management.Identifying the network-wide forwarding behaviors of a packet is essential for many network management tasks, including policy enforcement, rule verification, and fault localization. We start by presenting AP Classifier. AP Classifier was developed based on the concept of atomic predicates which can be used to characterize the forwarding behaviors of packets. There is an increasing trend that enterprises outsource their Network Function (NF) processing to a cloud to lower cost and ease management. To avoid threats to the enterprise’s private information, we propose SICS based on AP Classifier, a secure and dynamic NF outsourcing framework. Stateful NFs have become essential parts of modern networks, increasing the complexity in network management. A major step in network automation is to automatically translate high level network intents into low level configurations. To ensure those configurations and the states generated by automation match intents, we present Epinoia, a network intent checker for stateful networks. While the concept of auto-translation sounds promising, operators may not know what intents should be. To close the control loop, we present AutoInfer to automatically infer intents of running networks, which helps operators understand the network runtime states

High-Level Abstractions for Programming Network Policies

The emergence of network programmability enabled by innovations such as active network- ing, SDN and NFV offers tremendous flexibility to program network policies. However, it also poses a new demand to network operators on programming network policies. The motivation of this dissertation is to study the feasibility of using high-level abstractions to simplify the programming of network policies. First, we propose scenario-based programming, a framework that allows network operators to program stateful network policies by describing example behaviors in representative scenarios. Given these scenarios, our scenario-based programming tool NetEgg automatically infers the controller state that needs to be maintained along with the rules to process network events and update state. The NetEgg interpreter can execute the generated policy implementation on top of a centralized controller, but also automatically infers flow-table rules that can be pushed to switches to improve throughput. We study a range of policies considered in the literature and report our experience regarding specifying these policies using scenarios. We evaluate NetEgg based on the computational requirements of our synthesis algorithm as well as the overhead introduced by the generated policy implementation. Our results show that our synthesis algorithm can generate policy implementations in seconds, and the automatically generated policy implementations have performance comparable to their hand-crafted implementations. Our preliminary user study results show that NetEgg was able to reduce the programming time of the policies we studied. Second, we propose NetQRE, a high-level declarative language for programming quantitative network policies that require monitoring a stream of network packets. Based on a novel theoretical foundation of parameterized quantitative regular expressions, NetQRE integrates regular-expression-like pattern matching at flow-level as well as application-level payloads with aggregation operations such as sum and average counts. We describe a compiler for NetQRE that automatically generates an efficient implementation from the specification in NetQRE. Our evaluation results demonstrate that NetQRE is expressive to specify a wide range of quantitative network policies that cannot be naturally specified in other systems. The performance of the generated implementations is comparable with that of the manually-optimized low-level code. NetQRE can be deployed in different settings. Our proof-of-concept deployment shows that NetQRE can provide timely enforcement of quantitative network policies