https://www.cs.arizona.edu/~yic/paper/comcom-forwarding.pdf

A case for stateful forwarding plane

Network functions virtualization (NFV) together with software-defined networking (SDN) has the potential to help operators satisfy tight service level agreements, accurately monitor and manipulate network traffic, and minimize operating expenses. However, in scenarios that require packet processing to be redistributed across a collection of network function (NF) instances, simultaneously achieving all three goals requires a framework that provides efficient, coordinated control of both internal NF state and network forwarding state. To this end, we design a control plane called OpenNF. We use carefully designed APIs and a clever combination of events and forwarding updates to address race conditions, bound overhead, and accommodate a variety of NFs. Our evaluation shows that OpenNF offers efficient state control without compromising flexibility, and requires modest additions to NFs.

/pdf/opennf-enabling-innovation-in-network-function-control-43ydmfe1bv.pdf

OpenNF: enabling innovation in network function control

Cloud research to date has lacked data on the characteristics of the production virtual machine (VM) workloads of large cloud providers. A thorough understanding of these characteristics can inform the providers' resource management systems, e.g. VM scheduler, power manager, server health manager. In this paper, we first introduce an extensive characterization of Microsoft Azure's VM workload, including distributions of the VMs' lifetime, deployment size, and resource consumption. We then show that certain VM behaviors are fairly consistent over multiple lifetimes, i.e. history is an accurate predictor of future behavior. Based on this observation, we next introduce Resource Central (RC), a system that collects VM telemetry, learns these behaviors offline, and provides predictions online to various resource managers via a general client-side library. As an example of RC's online use, we modify Azure's VM scheduler to leverage predictions in oversubscribing servers (with oversubscribable VM types), while retaining high VM performance. Using real VM traces, we then show that the prediction-informed schedules increase utilization and prevent physical resource exhaustion. We conclude that providers can exploit their workloads' characteristics and machine learning to improve resource management substantially.

/pdf/resource-central-understanding-and-predicting-workloads-for-3etxuzxef2.pdf

Resource Central: Understanding and Predicting Workloads for Improved Resource Management in Large Cloud Platforms

In Named Data Networking (NDN) architecture, packets carry data names rather than source or destination addresses. This change of paradigm leads to a new data plane: data consumers send out Interest packets, routers forward them and maintain the state of pending Interests, which is used to guide Data packets back to the consumers. NDN routers' forwarding process is able to detect network problems by observing the two-way traffic of Interest and Data packets, and explore multiple alternative paths without loops. This is in sharp contrast to today's IP forwarding process which follows a single path chosen by the routing process, with no adaptability of its own. In this paper we outline the design of NDN's adaptive forwarding, articulate its potential benefits, and identify open research issues.

/pdf/adaptive-forwarding-in-named-data-networking-3sius9lskr.pdf

Adaptive forwarding in named data networking

A survey

Datacenter networks deal with a variety of workloads, ranging from latency-sensitive small flows to bandwidth-hungry large flows. Load balancing schemes based on flow hashing, e.g., ECMP, cause congestion when hash collisions occur and can perform poorly in asymmetric topologies. Recent proposals to load balance the network require centralized traffic engineering, multipath-aware transport, or expensive specialized hardware. We propose a mechanism that avoids these limitations by (i) pushing load-balancing functionality into the soft network edge (e.g., virtual switches) such that no changes are required in the transport layer, customer VMs, or networking hardware, and (ii) load balancing on fine-grained, near-uniform units of data (flowcells) that fit within end-host segment offload optimizations used to support fast networking speeds. We design and implement such a soft-edge load balancing scheme, called Presto, and evaluate it on a 10 Gbps physical testbed. We demonstrate the computational impact of packet reordering on receivers and propose a mechanism to handle reordering in the TCP receive offload functionality. Presto's performance closely tracks that of a single, non-blocking switch over many workloads and is adaptive to failures and topology asymmetry.

/pdf/presto-edge-based-load-balancing-for-fast-datacenter-4bf7h8gu42.pdf

Presto: Edge-based Load Balancing for Fast Datacenter Networks

Name-based route lookup is a key function for Named Data Networking (NDN). The NDN names are hierarchical and have variable and unbounded lengths, which are much longer than IPv4/6 address, making fast name lookup a challenging issue. In this paper, we propose an effective Name Component Encoding (NCE) solution with the following two techniques: (1) A code allocation mechanism is developed to achieve memory-efficient encoding for name components, (2) We apply an improved State Transition Arrays to accelerate the longest name prefix matching and design a fast and incremental update mechanism which satisfies the special requirements of NDN forwarding process, namely to insert, modify, and delete name prefixes frequently. Furthermore, we analyze the memory consumption and time complexity of NCE. Experimental results on a name set containing 3,000,000 names demonstrate that compared with the character trie NCE reduces overall 30% memory. Besides, NCE performs a few millions lookups per second (on an Intel 2.8 GHz CPU), a speedup of over 7 times compared with the character trie. Our evaluation results also show that NCE can scale up to accommodate the potential future growth of the name sets.

/pdf/scalable-name-lookup-in-ndn-using-effective-name-component-12e201quww.pdf

Scalable Name Lookup in NDN Using Effective Name Component Encoding

Timely interaction between an SDN controller and switches is crucial to many SDN applications---e.g., fast rerouting during link failure and fine-grained traffic engineering in data centers. However, it is not well understood how the control plane in SDN switches impacts these applications. To this end, we conduct a comprehensive measurement study using four types of production SDN switches. Our measurements show that control actions, such as rule installation, have surprisingly high latency, due to both software implementation inefficiencies and fundamental traits of switch hardware.

/pdf/measuring-control-plane-latency-in-sdn-enabled-switches-493q27rlo9.pdf

Measuring control plane latency in SDN-enabled switches

Multi-tenant datacenters are successful because tenants can seamlessly port their applications and services to the cloud. Virtual Machine (VM) technology plays an integral role in this success by enabling a diverse set of software to be run on a unified underlying framework. This flexibility, however, comes at the cost of dealing with out-dated, inefficient, or misconfigured TCP stacks implemented in the VMs. This paper investigates if administrators can take control of a VM's TCP congestion control algorithm without making changes to the VM or network hardware. We propose AC/DC TCP, a scheme that exerts fine-grained control over arbitrary tenant TCP stacks by enforcing per-flow congestion control in the virtual switch (vSwitch). Our scheme is light-weight, flexible, scalable and can police non-conforming flows. In our evaluation the computational overhead of AC/DC TCP is less than one percentage point and we show implementing an administrator-defined congestion control algorithm in the vSwitch (i.e., DCTCP) closely tracks its native performance, regardless of the VM's TCP stack.

https://dl.acm.org/doi/pdf/10.1145/2934872.2934903

AC/DC TCP: Virtual Congestion Control Enforcement for Datacenter Networks

An increasingly large fraction of Internet services are hosted on a cloud computing system such as Amazon EC2 or Windows Azure. But to date, no in-depth studies about cloud usage by Internet services has been performed. We provide a detailed measurement study to shed light on how modern web service deployments use the cloud and to identify ways in which cloud-using services might improve these deployments. Our results show that: 4% of the Alexa top million use EC2/Azure; there exist several common deployment patterns for cloud-using web service front ends; and services can significantly improve their wide-area performance and failure tolerance by making better use of existing regional diversity in EC2. Driving these analyses are several new datasets, including one with over 34 million DNS records for Alexa websites and a packet capture from a large university network.

/pdf/next-stop-the-cloud-understanding-modern-web-service-17gdlpd0ae.pdf

Keqiang He

Papers

Presto: Edge-based Load Balancing for Fast Datacenter Networks

Scalable Name Lookup in NDN Using Effective Name Component Encoding

Measuring control plane latency in SDN-enabled switches

AC/DC TCP: Virtual Congestion Control Enforcement for Datacenter Networks

Next stop, the cloud: understanding modern web service deployment in EC2 and azure