How to Measure the Killer Microsecond

Datacenter-networking research requires tools to both generate traffic and accurately measure latency and throughput. While hardware-based tools have long existed commercially, they are primarily used to validate ASICs and lack flexibility, e.g. to study new protocols. They are also too expensive for academics. The recent development of kernel-bypass networking and advanced NIC features such as hardware timestamping have created new opportunities for accurate latency measurements. This paper compares these two approaches, and in particular whether commodity servers and NICs, when properly configured, can measure the latency distributions as precisely as specialized hardware. Our work shows that well-designed commodity solutions can capture subtle differences in the tail latency of stateless UDP traffic. We use hardware devices as the ground truth, both to measure latency and to forward traffic. We compare the ground truth with observations that combine five latency-measuring clients and five different port forwarding solutions and configurations. State-of-the-art software such as MoonGen that uses NIC hardware timestamping provides sufficient visibility into tail latencies to study the effect of subtle operating system configuration changes. We also observe that the kernel-bypass-based TRex software, that only relies on the CPU to timestamp traffic, can also provide solid results when NIC timestamps are not available for a particular protocol or device

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

Inferring Presence Status on Mobile Devices

Automatsko određivanje statusa korisnika mobilnih uređaja bez interakcije sa samim korisnikom primjenjivo je u brojnim komunikacijskim VoIP uslugama i uslugama trenutnog poručivanja, ali u praksi nije još zaživjelo. Radi se o problemu koji zahtijeva točne i energetski učinkovite metode strojnog učenja te ispravno odabran podskup značajki. Cilj rada bio je isprobane klasifikacijske algoritme usporediti po sljedećim kriterijima: točnost, trajanje učenja, trajanje testiranja te mogućnost nadoučavanja na zadanom skupu podataka. Za najpogodniji algoritam s obzirom na zadane kriterije odabran je algoritam AdaBoost temeljen na stablima odluke sa samo jednom razinom, odnosno njegova inačica koja nudi mogućnost postupnog učenja, Learn++. C4.5 i 1-NN također su mogući kandidati, ali uz određene prilagodbe. Isprobano je nekoliko varijanti genetskog programiranja te su stabla ostvarila najveću točnost, ali i najduže učenje, stoga je GP pogodan isključivo za primjenu na mobilnim uređajima nakon završenog proces učenja. Točnost na agregiranom skupu podataka od različitih korisnika ne smanjuje značajno. Iscrpnom pretragom podskupova značajki kod više od 80% testiranih korisnika jedino su energetski učinkovite značajke bile potrebne kako bi se ostvarila najbolja moguća točnost.Automatically inferring presence status on smartphones without user intervention is applicable in many IM and VoIP communication services, but it has not yet been implemented in any of them. It is a problem that requires accurate and energy efficient machine learning methods and a well-chosen subset of features. The objective of this thesis was to test several classification algorithms and to compare them by different criteria: accuracy, training time, test time and the possibility of incremental learning on the given dataset. All the given criteria meet in boosting algorithm AdaBoost based on decision stumps, in fact in its incremental edition called Learn++. C4.5 and 1-NN are also possible candidates, with some necessary adjustments. Several GP variants were tried for a potential use after offline training phase and decision tree based GP showed the highest accuracy, but the slowest training time. The accuracy on accumulated datasets from different users does not decrease significantly. The exhaustive feature selection method was performed and within more than 80% of users only the energy efficient features are necessary to achieve the best possible accuracy

Inferring Presence Status on Mobile Devices

Automatsko određivanje statusa korisnika mobilnih uređaja bez interakcije sa samim korisnikom primjenjivo je u brojnim komunikacijskim VoIP uslugama i uslugama trenutnog poručivanja, ali u praksi nije još zaživjelo. Radi se o problemu koji zahtijeva točne i energetski učinkovite metode strojnog učenja te ispravno odabran podskup značajki. Cilj rada bio je isprobane klasifikacijske algoritme usporediti po sljedećim kriterijima: točnost, trajanje učenja, trajanje testiranja te mogućnost nadoučavanja na zadanom skupu podataka. Za najpogodniji algoritam s obzirom na zadane kriterije odabran je algoritam AdaBoost temeljen na stablima odluke sa samo jednom razinom, odnosno njegova inačica koja nudi mogućnost postupnog učenja, Learn++. C4.5 i 1-NN također su mogući kandidati, ali uz određene prilagodbe. Isprobano je nekoliko varijanti genetskog programiranja te su stabla ostvarila najveću točnost, ali i najduže učenje, stoga je GP pogodan isključivo za primjenu na mobilnim uređajima nakon završenog proces učenja. Točnost na agregiranom skupu podataka od različitih korisnika ne smanjuje značajno. Iscrpnom pretragom podskupova značajki kod više od 80% testiranih korisnika jedino su energetski učinkovite značajke bile potrebne kako bi se ostvarila najbolja moguća točnost.Automatically inferring presence status on smartphones without user intervention is applicable in many IM and VoIP communication services, but it has not yet been implemented in any of them. It is a problem that requires accurate and energy efficient machine learning methods and a well-chosen subset of features. The objective of this thesis was to test several classification algorithms and to compare them by different criteria: accuracy, training time, test time and the possibility of incremental learning on the given dataset. All the given criteria meet in boosting algorithm AdaBoost based on decision stumps, in fact in its incremental edition called Learn++. C4.5 and 1-NN are also possible candidates, with some necessary adjustments. Several GP variants were tried for a potential use after offline training phase and decision tree based GP showed the highest accuracy, but the slowest training time. The accuracy on accumulated datasets from different users does not decrease significantly. The exhaustive feature selection method was performed and within more than 80% of users only the energy efficient features are necessary to achieve the best possible accuracy

Inferring Presence Status on Mobile Devices

Automatsko određivanje statusa korisnika mobilnih uređaja bez interakcije sa samim korisnikom primjenjivo je u brojnim komunikacijskim VoIP uslugama i uslugama trenutnog poručivanja, ali u praksi nije još zaživjelo. Radi se o problemu koji zahtijeva točne i energetski učinkovite metode strojnog učenja te ispravno odabran podskup značajki. Cilj rada bio je isprobane klasifikacijske algoritme usporediti po sljedećim kriterijima: točnost, trajanje učenja, trajanje testiranja te mogućnost nadoučavanja na zadanom skupu podataka. Za najpogodniji algoritam s obzirom na zadane kriterije odabran je algoritam AdaBoost temeljen na stablima odluke sa samo jednom razinom, odnosno njegova inačica koja nudi mogućnost postupnog učenja, Learn++. C4.5 i 1-NN također su mogući kandidati, ali uz određene prilagodbe. Isprobano je nekoliko varijanti genetskog programiranja te su stabla ostvarila najveću točnost, ali i najduže učenje, stoga je GP pogodan isključivo za primjenu na mobilnim uređajima nakon završenog proces učenja. Točnost na agregiranom skupu podataka od različitih korisnika ne smanjuje značajno. Iscrpnom pretragom podskupova značajki kod više od 80% testiranih korisnika jedino su energetski učinkovite značajke bile potrebne kako bi se ostvarila najbolja moguća točnost.Automatically inferring presence status on smartphones without user intervention is applicable in many IM and VoIP communication services, but it has not yet been implemented in any of them. It is a problem that requires accurate and energy efficient machine learning methods and a well-chosen subset of features. The objective of this thesis was to test several classification algorithms and to compare them by different criteria: accuracy, training time, test time and the possibility of incremental learning on the given dataset. All the given criteria meet in boosting algorithm AdaBoost based on decision stumps, in fact in its incremental edition called Learn++. C4.5 and 1-NN are also possible candidates, with some necessary adjustments. Several GP variants were tried for a potential use after offline training phase and decision tree based GP showed the highest accuracy, but the slowest training time. The accuracy on accumulated datasets from different users does not decrease significantly. The exhaustive feature selection method was performed and within more than 80% of users only the energy efficient features are necessary to achieve the best possible accuracy

Understanding and Mitigating Latency Variability of Latency-Critical Applications

A major theme of IT in the past decade has been the shift from on-premise hardware to cloud computing. Running a service in the public cloud is practical, because a large number of resources can be bought on-demand, but this shift comes with its own set of challenges, e.g., customers having less control over their environment. In scale-out deployments of on-line data-intensive services, each client request typically executes queries on many servers in parallel, and its final response time is often lower-bounded by the slowest query execution. There are numerous hard-to-trace reasons why some queries finish later than others. Latency variability appears at different timescales. To understand its cause and impact we need to measure it correctly. It is clear that we need hardware support to measure latencies on the nanosecond-scale, and that software-based tools are good-enough for the millisecond- scale, but it is not clear whether software-based tools are also reliable at the microsecond-scale, which is of growing interest to a large research and industry community. Measuring is a first step towards understanding the problems that cause latency variability, but what if some of them are extremely complex, or fixing them is out of reach given a service providerâs restrictions? Even large companies that own datacenters and build their own software and hardware stack sometimes suffer from hard-to-understand or hard-to-fix performance issues. Medium-sized companies that simply use shared public infrastructure, and do not themselves develop most of the code running on the machines, have limited capabilities and often cannot rule out each and every source of system interference. This reality inspired the line of research on what is known as hedging policies. By using redundant requests we can reduce overall latency at the cost of consuming additional resources. This dissertation characterizes latency variability of interactive services, shows how to measure it and how to mitigate its effect on end-to-end latency without sacrificing system capacity. Concretely, this dissertation makes three contributions: First, it shows how to measure microsecond-scale latency variability with both low cost and high precision, with the help of kernel bypass and hardware timestamps. Second, it empirically derives a lower bound on the tail latency that any implementable hedging policy might achieve for a given workload. Through evaluating our lower bound on a large parameter space, we determine when hedging is beneficial. Lastly, we describe and evaluate a practical policy, LAEDGE, that approximates our theoretical lower bound and achieves as much as half of its hedging potential. We show the applicability of our solution to real applications deployed in the public cloud where LAEDGE outperforms the state-of-the-art scheduling policies by up to 49%, averaged on low to medium load

Repatriation of sailors according to the Maritime Legislation Amendment Act of 2011

Rad obrađuje pravnu problematiku repatrijacije kao jedno od temeljnih prava pomoraca. Uspoređujući međunarodnopravna rješenja koja predmetnu problematiku normiraju Konvencijom o radu pomoraca iz 2006. god. u ovom radu dan je prikaz na koji su način i u kojem obimu, predložena rješenja primijenjena u hrvatskom nacionalnom zakonodavstvu. Budući da je dana 11. lipnja 2011. god. stupio na snagu Zakon o izmjenama i dopunama Pomorskog zakonika u ovom radu dan je sustavan i cjelovit uvid u promjene u pravnom normiranju repatrijacije pomoraca u odnosu na dosadašnja rješenja.The paper deals with legal issues of repatriation as one of the fundamental rights of seafarers. Comparing international legal solutions to relevant issues of norm the Maritime Labour Convention of 2006. in this paper it was shown in which way and in which extent, the proposed solutions applied in the Croatian national legislation. Considering that The Law of Changes and Additions of the Maritime Code from 2011 came into force on June, 11 2011 in this paper it was in a systematic and integral way represented the changes in the legal standardization of repatriation of seafarers in relation to the previous solution

When to Hedge in Interactive Services

In online data-intensive (OLDI) services, each client request typically executes on multiple servers in parallel; as a result, “system hiccups”, although rare within a single server, can interfere with many client requests and cause violations of service-level objectives. Service providers have long been fighting this “tail at scale” problem through “hedging”, i.e., issuing redundant queries to mask system hiccups. This, however, can potentially cause congestion that is more detrimental to tail latency than the hiccups themselves. This paper asks: when does it make sense to hedge in OLDI services, and how can we hedge enough to mask system hiccups but not as much as to cause congestion? First, we show that there are many realistic scenarios where hedging can have no benefit—where any hedging-based scheduling policy, including the state-of-the-art, yields no latency reduction compared to optimal load balancing without hedging. Second, we propose LÆDGE, a scheduling policy that combines optimal load balancing with work-conserving hedging, and evaluate it in an AWS cloud deployment. We show that LÆDGE strikes the right balance: first, unlike the state of the art, it never causes unnecessary congestion; second, it performs close to an ideal scheduling policy, improving the 99th percentile latency by as much as 49%, measured on 60% system utilization—without any difficult parameter training as found in the state of the art

FCR: Fast and Consistent Controller-Replication in Software Defined Networking

We consider the problem of coordination among replicated SDN controllers, where the challenge is to ensure a consistent view of the network while reacting to network events in a prompt manner. Existing solutions are either consensus-based, which achieve consistency at the expense of high latency; or eventual-consistency-based, which have low latency at the expense of severe limitations on the types of applications and policies implementable by the controller. We propose the Fast and Consistent Controller-Replication (FCR) scheme. FCR is based on a deterministic agreement mechanism that performs agreement on the input of controllers, instead of agreement on the output as done in consensus mechanisms. We formally prove that FCR provides the same guarantees in terms of implementable applications and network policies, as any deterministic single-image controller. Through simulation and implementation, we show that these guarantees can be implemented with little latency overhead, compared to eventual-consistency approaches, and can be achieved significantly faster than consensus-based approaches