IX Open-source version 1.1 - Deployment and Evaluation Guide

This Technical Report provides the deployment and evaluation guide of the IX dataplane operating system, as of its first open-source release on May 27, 2016. To facilitate the reproduction of our results, we include in this report the precise steps needed to install, deploy and configure IX and its workloads. We reproduce all benchmarks previously published in two peer-reviewed publications at OSDI '14 and SoCC '15 using this up-to-date, open-source code base

miRGen 2.0: a database of microRNA genomic information and regulation

MicroRNAs are small, non-protein coding RNA molecules known to regulate the expression of genes by binding to the 3′UTR region of mRNAs. MicroRNAs are produced from longer transcripts which can code for more than one mature miRNAs. miRGen 2.0 is a database that aims to provide comprehensive information about the position of human and mouse microRNA coding transcripts and their regulation by transcription factors, including a unique compilation of both predicted and experimentally supported data. Expression profiles of microRNAs in several tissues and cell lines, single nucleotide polymorphism locations, microRNA target prediction on protein coding genes and mapping of miRNA targets of co-regulated miRNAs on biological pathways are also integrated into the database and user interface. The miRGen database will be continuously maintained and freely available at http://www.microrna.gr/mirgen/

Metronome: adaptive and precise intermittent packet retrieval in DPDK

DPDK (Data Plane Development Kit) is arguably today's most employed framework for software packet processing. Its impressive performance however comes at the cost of precious CPU resources, dedicated to continuously poll the NICs. To face this issue, this paper presents Metronome, an approach devised to replace the continuous DPDK polling with a sleep&wake intermittent mode. Metronome revolves around two main innovations. First, we design a microseconds time-scale sleep function, named hr_sleep(), which outperforms Linux' nanosleep() of more than one order of magnitude in terms of precision when running threads with common time-sharing priorities. Then, we design, model, and assess an efficient multi-thread operation which guarantees service continuity and improved robustness against preemptive thread executions, like in common CPU-sharing scenarios, meanwhile providing controlled latency and high polling efficiency by dynamically adapting to the measured traffic load

R2P2: Making RPCs first-class datacenter citizens

Remote Procedure Calls are widely used to connect datacenter applications with strict tail-latency service level objectives in the scale of µs. Existing solutions utilize streaming or datagram-based transport protocols for RPCs that impose overheads and limit the design flexibility. Our work exposes the RPC abstraction to the endpoints and the network, making RPCs first-class datacenter citizens and allowing for in-network RPC scheduling. We propose R2P2, a UDP-based transport protocol specifically designed for RPCs inside a datacenter. R2P2 exposes pairs of requests and responses and allows efficient and scalable RPC routing by separating the RPC target selection from request and reply streaming. Leveraging R2P2, we implement a novel join-bounded-shortest-queue (JBSQ) RPC load balancing policy, which lowers tail latency by centralizing pending RPCs in the router and ensures that requests are only routed to servers with a bounded number of outstanding requests. The R2P2 router logic can be implemented either in a software middlebox or within a P4 switch ASIC pipeline. Our evaluation, using a range of microbenchmarks, shows that the protocol is suitable for µs-scale RPCs and that its tail latency outperforms both random selection and classic HTTP reverse proxies. The P4-based implementation of R2P2 on a Tofino ASIC adds less than 1µs of latency whereas the software middlebox implementation adds 5µs latency and requires only two CPU cores to route RPCs at 10 Gbps linerate. R2P2 improves the tail latency of web index searching on a cluster of 16 workers operating at 50% of capacity by 5.7× over NGINX. R2P2 improves the throughput of the Redis key-value store on a 4-node cluster with master/slave replication for a tail-latency service-level objective of 200µs by more than 4.8× vs. vanilla Redis

Performance Evaluation of a Communication Protocol for Vital Signs Sensors Used for the Monitoring of Athletes

Monitoring vital signs in athletes, mainly during training, is of crucial importance for both the athlete and the coach, in order to avoid overtraining. Overtraining is an extreme state of fatigue that forces athletes to rest for several weeks having a negative impact on athlete's performance, health, and daily life. A wireless sensor network (WSN) combines embedded computing technology with communication technology in order to collect information of the network coverage area and send it to the observer. In this paper, we present results of system's performance evaluation for the IEEE 802.15.4 standard, including the physical (PHY) layer and media access control (MAC) sublayer, in order to collect and store large sets of athletes’ data as well as providing results about network values such as end-to-end delay, load, and throughput captured from global and objects statistics

Open access to the Proceedings of the 11th USENIX Symposium on Operating Systems Design and Implementation is sponsored by USENIX. IX: A Protected Dataplane Operating System for High Throughput and Low Latency IX: A Protected Dataplane Operating System fo

Abstract The conventional wisdom is that aggressive networking requirements, such as high packet rates for small messages and microsecond-scale tail latency, are best addressed outside the kernel, in a user-level networking stack. We present IX, a dataplane operating system that provides high I/O performance, while maintaining the key advantage of strong protection offered by existing kernels. IX uses hardware virtualization to separate management and scheduling functions of the kernel (control plane) from network processing (dataplane). The dataplane architecture builds upon a native, zero-copy API and optimizes for both bandwidth and latency by dedicating hardware threads and networking queues to dataplane instances, processing bounded batches of packets to completion, and by eliminating coherence traffic and multi-core synchronization. We demonstrate that IX outperforms Linux and state-of-the-art, user-space network stacks significantly in both throughput and end-to-end latency. Moreover, IX improves the throughput of a widely deployed, key-value store by up to 3.6× and reduces tail latency by more than 2×