On the Efficacy of Fine-Grained Traffic Splitting Protocols in Data Center Networks

Multi-rooted tree topologies are commonly used to construct high-bandwidth data center network fabrics. In these networks, switches typically rely on equal-cost multipath (ECMP) routing techniques to split traffic across multiple paths, such that packets within a flow traverse the same end-to-end path. Unfortunately, since ECMP splits traffic based on flow-granularity, it can cause load imbalance across paths resulting in poor utilization of network resources. More finegrained traffic splitting techniques are typically not preferred because they can cause packet reordering that can, according to conventional wisdom, lead to severe TCP throughput degradation. In this work, we revisit this fact in the context of regular data center topologies such as fat-tree architectures. We argue that packet-level traffic splitting, where packets of a flow are sprayed through all available paths, would lead to a better load-balanced network, which in turn leads to significantly more balanced queues and much higher throughput compared to ECMP

A Framework for Efficient Class-based Sampling

Abstract—With an increasing requirement for network monitoring tools to classify traffic and track security threats, newer and efficient ways are needed for collecting traffic statistics and monitoring of network flows. However, traditional solutions based on random packet sampling treat all flows as equal and therefore, do not provide the flexibility required for these applications. In this paper, we propose a novel architecture called CLAMP that provides an efficient framework to implement size-based sampling. At the heart of CLAMP is a novel data structure called Composite Bloom filter (CBF) that consists of a set of Bloom filters that work together to encapsulate various class definitions. In comparison to previous approaches that implement simple size-based sampling, our architecture requires substantially lower memory (upto 80x) and results in higher flow coverage (upto 8x more flows) under specific configurations. I

The power of slicing in internet flow measurement

Abstract – Network service providers use high speed flow measurement solutions in routers to track dominant applications, compute traffic matrices and to perform other such operational tasks. These solutions typically need to operate within the constraints of the three precious router resources – CPU, memory and bandwidth. Cisco’s Net-Flow, a widely deployed flow measurement solution, uses a configurable static sampling rate to control these resources. In this paper, we propose Flow Slices, a solution inspired from previous enhancements to NetFlow such as Smart Sampling [8], Adaptive NetFlow (ANF) [10]. Flow Slices, in contrast to NetFlow, controls the three resource bottlenecks at the router using separate “tuning knobs”; it uses packet sampling to control CPU usage, flow sampling to control memory usage and finally multi-factor smart sampling to control reporting bandwidth. The resulting solution has smaller resource requirements than current proposals (up to 80 % less memory usage than ANF), enables more accurate traffic analysis results (up to 10 % less error than ANF) and balances better the error in estimates of byte, packet and flow counts (flow count estimates up to 8 times more accurate than after Smart Sampling). We provide theoretical analyses of the unbiasedness and variances of the estimators based on Flow Slices and experimental comparisons with other flow measurement solutions such as ANF.

Practical lazy scheduling in sensor networks

ABSTRACT Experience has shown that the power consumption of sensors andother wireless computational devices is often dominated by their communication patterns. We present a practical realization of lazypacket scheduling that attempts to minimize the total transmission energy in a broadcast network by dynamically adjusting each node'stransmission power and rate on a per-packet basis. Lazy packet scheduling leverages the fact that many channel coding schemesare more efficient at lower transmission rates; that is, the energy required to send a fixed amount of data can be reduced by transmit-ting the data at a lower bit rate and transmission power. The optimal per-packet transmission rate in a multi-node net-work is governed in practice by the available bit rates of the given transceiver(s), the nodes ' delay tolerance, and the offered load atevery node contending for the shared broadcast channel. We propose an extension to the traditional CSMA/CA MAC scheme calledL-CSMA/CA that allows individual nodes to continually estimate the current demand for a broadcast channel and adjust their trans-mission schedules accordingly. Our simulation results show that L-CSMA/CA can provide improved energy efficiency in a single-hop, broadcast network (20-25 % with more than 10 nodes, and up to 99 % for four nodes with a standard power function) for bothPoisson and bursty arrivals with only minor degradation the capacity of the channel. 1