Teaching rigorous distributed systems with efficient model checking Michael et al., EuroSys’19 On the surface you might think today’s paper selection an odd pick. It describes the labs enviro… | Continue reading
Time protection: the missing OS abstraction Ge et al., EuroSys’19 Ever since the prominent emergence of timing-based microarchitectural attacks (e.g. Spectre, Meltdown, and friends) I’ve been… | Continue reading
Thou shalt not depend on me: analysing the use of outdated JavaScript libraries on the web Lauinger et al., NDSS 2017 Just based on the paper title alone, if you had to guess what the situation is … | Continue reading
Master of web puppets: abusing web browsers for persistent and stealthy computation Papadopoulus et al., NDSS’19 You’ve probably heard about crypto-currency mining and the like in hijacked br… | Continue reading
Don’t trust the locals: investigating the prevalence of persistent client-side cross-site scripting in the wild Steffens et al., NDSS’19 Does your web application make use of local storage? I… | Continue reading
How bad can it git? Characterizing secret leakage in public GitHub repositories Meli et al., NDSS’19 On the one hand you might say there’s no new news here. We know that developers shouldn’t … | Continue reading
How bad can it git? Characterizing secret leakage in public GitHub repositories Meli et al., NDSS’19 On the one hand you might say there’s no new news here. We know that developers shouldn’t … | Continue reading
How bad can it git? Characterizing secret leakage in public GitHub repositories Meli et al., NDSS’19 On the one hand you might say there’s no new news here. We know that developers shouldn’t … | Continue reading
Ginseng: keeping secrets in registers when you distrust the operating system Yun & Zhong et al., NDSS’19 Suppose you did go to the extreme length of establishing an unconditional root of … | Continue reading
Ginseng: keeping secrets in registers when you distrust the operating system Yun & Zhong et al., NDSS’19 Suppose you did go to the extreme length of establishing an unconditional root of … | Continue reading
Establishing software root of trust unconditionally Gligor & Woo, NDSS’19 The authors won a best paper award for this work at NDSS this year. The main result is quite something, but as yo… | Continue reading
Amazon Aurora: on avoiding distributed consensus for I/Os, commits, and membership changes, Verbitski et al., SIGMOD’18 This is a follow-up to the paper we looked at earlier this week on the design… | Continue reading
Amazon Aurora: design considerations for high throughput cloud-native relational databases Verbitski et al., SIGMOD’17 Werner Vogels recently published a blog post describing Amazon Aurora as… | Continue reading
How to Quantify Scalability: The Universal Scalability Law (USL) – Gunther Update: corrected sign in USL equation – many thanks to Rob Fielding for pointing out the error. TL;DR: The Un… | Continue reading
Slim: OS kernel support for a low-overhead container overlay network Zhuo et al., NSDI’19 Container overlay networks rely on packet transformations, with each packet traversing the networking… | Continue reading
Understanding lifecycle management complexity of datacenter topologies Zhang et al., NSDI’19 There has been plenty of interesting research on network topologies for datacenters, with Clos-lik… | Continue reading
Graph neural networks: a review of methods and applications Zhou et al., arXiv 2019 It’s another graph neural networks survey paper today! Cue the obligatory bus joke. Clearly, this covers much of … | Continue reading
A comprehensive survey on graph neural networks Wu et al., arXiv’19 Last year we looked at ‘Relational inductive biases, deep learning, and graph networks,’ where the authors made the case fo… | Continue reading
Veritas: shared verifiable databases and tables in the cloud Allen et al., CIDR’19 Two (or more) parties want to transact based on the sharing of information (e.g. current offers). In order t… | Continue reading
The case for network-accelerated query processing Lerner et al., CIDR’19 Datastores continue to advance on a number of fronts. Some of those that come to mind are adapting to faster networks … | Continue reading
Programming paradigms for dummies: what every programmer should know Peter Van Roy, 2009 We’ll get back to CIDR’19 next week, but chasing the thread starting with the Data Continuum paper led me to… | Continue reading
The Data Calculator: data structure design and cost synthesis from first principles and learned cost models Idreos et al., SIGMOD’18 This paper preceded the work on data continuums that we lo… | Continue reading
Design continuums and the path toward self-designing key-value stores that know and learn Idreos et al., CIDR’19 We’ve seen systems that help to select the best data structure from a pre-defi… | Continue reading
Towards a hands-free query optimizer through deep learning Marcus & Papaemmanouil, CIDR’19 Where the SageDB paper stopped— at the exploration of learned models to assist in query optimisa… | Continue reading
SageDB: a learned database system Kraska et al., CIDR’19 About this time last year, a paper entitled ‘The case for learned index structures’ (part I, part II) generated a lot of excitement an… | Continue reading
Serverless computing: one step forward, two steps back Hellerstein et al., CIDR’19 The biennial Conference on Innovative Data Systems Research has come round again. Today’s paper choice is su… | Continue reading
Unsupervised learning of artistic styles with archetypal style analysis Wynen et al., NeurIPS’18 I’ve always enjoyed following work on artistic style transfer. The visual nature makes it easy… | Continue reading
Neural ordinary differential equations Chen et al., NeurIPS’18 ‘Neural Ordinary Differential Equations’ won a best paper award at NeurIPS last month. It’s not an easy piece (at least not for … | Continue reading
Building machines that learn and think like people Lake et al., arXiv 2016 Pro-tip: if you’re going to try and read and write up a paper every weekday, it’s best not to pick papers that… | Continue reading
The tradeoffs of large scale learning Bottou & Bousquet, NIPS’07 Welcome to another year of The Morning Paper. As usual we’ll be looking at a broad cross-section of computer science resea… | Continue reading
Towards a theory of software development expertise Baltes et al., ESEC/FSE’18 This is the last paper we’ll be looking at this year, so I’ve chosen something a little more reflective to leave … | Continue reading
Identifying impactful service system problems via log analysis He et al., ESEC/FSE’18 If something is going wrong in your system, chances are you’ve got two main sources to help you detect an… | Continue reading
Applied machine learning at Facebook: a datacenter infrastructure perspective Hazelwood et al., _HPCA’18 _ This is a wonderful glimpse into what it’s like when machine learning comes to pervade nea… | Continue reading
Darwinian data structure selection Basios et al., FSE’18 GraphIt may have caught your attention for the success of its approach, but I suspect for many readers it’s not something you’ll be im… | Continue reading
GraphIt: a high-performance graph DSL Zhang et al., OOPSLA’18 See also: The problem with finding the optimal algorithm and data structures for a given problem is that so often it depends. Thi… | Continue reading
MadMax: surviving out-of-gas conditions in ethereum smart contracts Grech et al., OOPSLA’18 We’re transitioning to look at a selection of papers from the recent OOPSLA conference this week. M… | Continue reading
RapidChain: scaling blockchain via full sharding Zamani et al., CCS’18 RapidChain is a sharding-based public blockchain protocol along the lines of OmniLedger that we looked at earlier in the… | Continue reading
FairSwap: how to fairly exchange digital goods Dziembowski et al., CCS’18 (Preprint) This is a transactions paper with a twist. The transactions we’re talking about are purchases of digital a… | Continue reading
Securify: practical security analysis of smart contracts Tsankov et al., CCS’18 Sometimes the perfect is the enemy of the good. When we’re talking about securing smart contracts, we need all … | Continue reading
LEMNA: explaining deep learning based security applications Guo et al., CCS’18 Understanding why a deep learning model produces the outputs it does is an important part of gaining trust in th… | Continue reading
BEAT: asynchronous BFT made practical Duan et al., CCS’18 Reaching agreement (consensus) is hard enough, doing it in the presence of active adversaries who can tamper with or destroy your com… | Continue reading
Uncertainty propagation in data processing systems Manousakis et al., SoCC’18 When I’m writing an edition of The Morning Paper, I often imagine a conversation with a hypothetical reader sat i… | Continue reading
ScootR: scaling R dataframes on dataflow systems Kunft et al., SoCC’18 The language of big data is Java ( / Scala). The languages of data science are Python and R. So what do you do when you … | Continue reading
Overload control for scaling WeChat microservices Zhou et al., SoCC’18 There are two reasons to love this paper. First off, we get some insights into the backend that powers WeChat; and secon… | Continue reading
Unikernels as processes Williams et al., SoCC’18 Ah, unikernels. Small size, fast booting, tiny attack surface, resource efficient, hard to deploy on existing cloud platforms, and undebuggabl… | Continue reading
Debugging distributed systems with why-across-time provenance Whittaker et al., SoCC’18 This value is 17 here, and it shouldn’t be. Why did the get request return 17? Sometimes the simplest q… | Continue reading
ApproxJoin: approximate distributed joins Le Quoc et al., SoCC’18 GitHub: The join is a fundamental data processing operation and has been heavily optimised in relational databases. When you’… | Continue reading
ASAP: fast, approximate graph pattern mining at scale Iyer et al., OSDI’18 I have a real soft spot for approximate computations. In general, we waste a lot of resources on overly accurate ana… | Continue reading