Cliffhanger: Scaling Performance Cliffs in Web Memory Caches - Cidon et al. 2016 Cliffhanger continues yesterday's theme of efficient cache allocation policies when sharing cache resources. The paper focuses on a shared memcached service, where memory is divided between a number of slabs (each slab storing items with sizes in a specific range - e.g. … Continue reading Cliffhanger: Scaling Performance Cliffs in Web Memory Caches
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FairRide: Near-Optimal, Fair Cache Sharing
FairRide: Near-Optimal, Fair Cache Sharing - Pu et al. 2016 Yesterday we looked at a near-optimal packet scheduling algorithm (LSTF), today it's the turn of a near-optimal fair cache sharing algorithm. We're concerned with the scenario where a single cache resource is shared by multiple applications / users. Ideally we'd like three properties to hold: … Continue reading FairRide: Near-Optimal, Fair Cache Sharing
Universal Packet Scheduling
Universal Packet Scheduling - Mittal et al. 2015 (presented at NSDI '16) Is there a universal scheduling algorithm, such that simply by changing its configuration parameters, we can produce any desired schedule? In Universal Packet Scheduling, Mittal et al. show us that in theory there can be no Universal Packet Scheduling (UPS) algorithm which achieves … Continue reading Universal Packet Scheduling
Maglev: A Fast and Reliable Software Network Load Balancer
Maglev: A Fast and Reliable Software Network Load Balancer - Eisenbud et al. 2016 Maglev is Google's software load balancer used within all their datacenters. It offers greater scalability and availability than hardware load balancers, enables quick iteration, and is much easier to upgrade. Maglev is a just another distributed system running on the commodity … Continue reading Maglev: A Fast and Reliable Software Network Load Balancer
Distributed TensorFlow with MPI
Distributed TensorFlow with MPI - Vishnu et al. 2016 A short early release paper to close out the week this week, which looks at how to support machine learning and data mining (MLDM) with Google's TensorFlow in a distributed setting. The paper also contains some good background on TensorFlow itself as well as MPI - … Continue reading Distributed TensorFlow with MPI
HyperLogLog in Practice: Algorithmic Engineering of a State of the Art Cardinality Estimation Algorithm
HyperLogLog in Practice: Algorithmic Engineering of a State of the Art Cardinality Estimation Algorithm - Heule et al. 2013 Continuing on the theme of approximations from yesterday, today's paper looks at what must be one of the best known approximate data structures after the Bloom Filter, HyperLogLog. It's HyperLogLog with a twist though - a … Continue reading HyperLogLog in Practice: Algorithmic Engineering of a State of the Art Cardinality Estimation Algorithm
MacroBase: Analytic Monitoring for the Internet of Things
MacroBase: Analytic Monitoring for the Internet of Things - Bailis et al. 2016 It looks like Peter Alvaro is not the only one to be doing some industrial collaboration recently! MacroBase is the result of Peter Bailis' collaboration with Cambridge Mobile Telematics (CMT), an IoT company. The topic at hand is analytic monitoring - detecting … Continue reading MacroBase: Analytic Monitoring for the Internet of Things
A Taxonomy of Attacks and a Survey of Defence Mechanisms for Semantic Social Engineering Attacks
A Taxonomy of Attacks and a Survey of Defence Mechanisms for Semantic Social Engineering Attacks - Heartfield and Loukas 2015 This paper is concerned with semantic social engineering: the manipulation of the user-computer interface to deceive a user and ultimately breach a computer system's information security. Semantic attack exploits include phishing, file masquerading (disguising file … Continue reading A Taxonomy of Attacks and a Survey of Defence Mechanisms for Semantic Social Engineering Attacks
Secrets, Lies, and Account Recovery: Lessons from the Use of Personal Knowledge Questions at Google
Secrets, Lies, and Account Recovery: Lessons from the Use of Personal Knowledge Questions at Google - Bonneau et al. 2015 What was your mother's maiden name? What was your city of birth? What was the name of your first school? I don't know about you, but I always groan inwardly when a website asks such … Continue reading Secrets, Lies, and Account Recovery: Lessons from the Use of Personal Knowledge Questions at Google
Strategic Dialogue Management via Deep Reinforcement Learning
Strategic Dialogue Management via Deep Reinforcement Learning - Cuayahuitl et al. 2015 If computers learning to play Atari arcade games by themselves isn't really your thing, perhaps you're more into board games? How about a Deep Reinforcement Learning system that learns how to trade effectively in Settlers of Catan! Again, we're not talking about a … Continue reading Strategic Dialogue Management via Deep Reinforcement Learning
the morning paper 