World-models for bitrate streaming

Adaptive bitrate (ABR) algorithms optimize the quality of streaming experiences for users in client-side video players, especially in unreliable or slow mobile networks. Several rule-based heuristic algorithms can achieve stable performance, but they sometimes fail to properly adapt to changing network conditions. Fluctuating bandwidth may cause algorithms to default to behavior that creates a negative experience for the user. ABR algorithms can be generated with reinforcement learning, a decision-making paradigm in which an agent learns to make optimal choices through interactions with an environment. Training reinforcement learning algorithms for bitrate streaming requires building a simulator for an agent to experience interactions quickly; training an agent in the real environment is infeasible due to the long step times in real environments. This project explores using supervised learning to construct a world-model, or a learned simulator, from recorded interactions. A reinforcement learning agent that is trained inside of the learned model, rather than a simulator, can outperform rule-based heuristics. Furthermore, agents that are trained inside the learned world-model can outperform model-free agents in low sample regimes. This work highlights the potential for world-models to quickly learn simulators, and to be used for generating optimal policies.</jats:p

BOAT: Building auto-tuners with structured Bayesian optimization

Due to their complexity, modern systems expose many con-figuration parameters which users must tune to maximizeperformance. Auto-tuning has emerged as an alternative inwhich a black-box optimizer iteratively evaluates configura-tions to find efficient ones. Unfortunately, for many systems,such as distributed systems, evaluating performance takestoo long and the space of configurations is too large for theoptimizer to converge within a reasonable tim

Collaborative facilitation and collaborative inhibition in virtual environments

Worldwide, organizations and small and medium-sized enterprises have already disruptively changed in many ways their physiological inner mechanisms, because of information and communication technologies (ICT) revolution. Nevertheless, the still ongoing COVID-19 worldwide emergency definitely promoted a wide adoption of teleworking modalities for many people around the world, making it more relevant than before to understand the real impact of virtual environments (VEs) on teamwork dynamics. From a psychological point of view, a critical question about teleworking modalities is how the social and cognitive dynamics of collaborative facilitation and collaborative inhibition would affect teamwork within VEs. This study analyzed the impact of a virtual environment (VE) on the recall of individuals and members of nominal and collaborative groups. The research assessed costs and benefits for collaborative retrieval by testing the effect of experimental conditions, stimulus materials, group size, experimental conditions order, anxiety state, personality traits, gender group composition and social interactions. A total of 144 participants were engaged in a virtual Deese-Roediger-McDermott (DRM) classical paradigm, which involved remembering word lists across two successive sessions, in one of four protocols: I-individual/nominal, I I -nominal/individual, I I I -nominal/collaborative, I V -collaborative/nominal. Results suggested, in general, a reduced collaborative inhibition effect in the collaborative condition than the nominal and individual condition. A combined effect between experimental condition and difficulty of the task appears to explain the presence of collaborative inhibition or facilitation. Nominal groups appeared to enhance the collaborative groups’ performance when virtual nominal groups come before collaborative groups. Variables such as personality traits, gender and social interactions may have a contribution to collaborative retrieval. In conclusion, this study indicated how VEs could maintain those peculiar social dynamics characterizing the participants’ engagement in a task, both working together and individually, and could affect their intrinsic motivation as well as performances. These results could be exploited in order to design brand new and evidenced-based practices, to improve teleworking procedures and workers well-being, as well as teleworking teamwork effectiveness.</jats:p

Quaestor: Query web caching for database-as-a-service providers

Today, web performance is primarily governed by round-trip latencies between end devices and cloud services. To improve performance, services need to minimize the delay of accessing data. In this paper, we propose a novel approach to low latency that relies on existing content delivery and web caching infrastructure. The main idea is to enable application-independent caching of query results and records with tunable consistency guarantees, in particular bounded staleness. Q uaestor (Query Store) employs two key concepts to incorporate both expiration-based and invalidation-based web caches: (1) an Expiring Bloom Filter data structure to indicate potentially stale data, and (2) statistically derived cache expiration times to maximize cache hit rates. Through a distributed query invalidation pipeline, changes to cached query results are detected in real-time. The proposed caching algorithms offer a new means for data-centric cloud services to trade latency against staleness bounds, e.g. in a database-as-a-service. Q uaestor is the core technology of the backend-as-a-service platform Baqend, a cloud service for low-latency websites. We provide empirical evidence for Q uaestor 's scalability and performance through both simulation and experiments. The results indicate that for read-heavy workloads, up to tenfold speed-ups can be achieved through Q uaestor 's caching. </jats:p

A First Step Towards User Assisted Online Social Networks

This work is at: 3rd ACM Workshop on Social Network Systems (SNS'10), co-located with EuroSys 2010 Conference, took place April, 13-16, 2010 in Paris, France.The current Online Social Networks' infrastructure is com- posed by thousands of servers distributed across data-centers spread over several geographical locations. These servers store all the users' information (pro le, contacts, contents, etc). Such an infrastructure incurs high operational and maintenance costs. Furthermore, this may threaten the scal- ability, the reliability, the availability and the privacy of the o ered service. On the other hand this centralized approach gives to the OSN provider full control over a huge amount of valuable information. This information constitutes the basis of the OSN provider's business. Most of the storage capacity is dedicated to store the user's content (e.g. photos, videos, etc). We believe that OSN provider does not have strong incentive to dedicate a large part of its infrastructure to store majority part of this content. In this position paper we introduce the concept of user assisted Online Social Network (uaOSN). This novel archi- tecture seeks to distribute the storage load associated to the content (e.g. photos, videos, etc) among the OSN's users. Thus the OSN provider keeps the control on the relevant in- formation while reducing the operational and maintenance costs. We discuss the bene ts that this proposal may pro- duce for both, the OSN provider and the users. We also discuss the technical aspects to be considered and compare this solution to other distributed approaches.This research is funded in part by the EU grant for the SO- CIALNETS project, 217141, by the Spanish Ministery of Science and Innovation through the CONPARTE project, TEC2007-67966-C03-03/TCM, and by the Regional Gover- ment ofMadrid through theMEDIANET project, S2009/TIC- 1468.Publicad

PrefEdge: SSD prefetcher for large-scale graph traversal

Mining large graphs has now become an important aspect of mul-tiple diverse applications and a number of computer systems have been proposed to provide runtime support. Recent interest in this area has led to the construction of single machine graph computa-tion systems that use solid state drives (SSDs) to store the graph. This approach reduces the cost and simplifies the implementation of graph algorithms, making computations on large graphs avail-able to the average user. However, SSDs are slower than main memory, and making full use of their bandwidth is crucial for ex-ecuting graph algorithms in a reasonable amount of time. In this paper, we present PrefEdge, a prefetcher for graph algorithms that parallelises requests to derive maximum throughput from SSDs. PrefEdge combines a judicious distribution of graph state between main memory and SSDs with an innovative read-ahead algorithm to prefetch needed data in parallel. This is in contrast to existing approaches that depend on multi-threading the graph algorithms to saturate available bandwidth. Our experiments on graph algo-rithms using random access show that PrefEdge not only is capa-ble of maximising the throughput from SSDs but is also able to almost hide the effect of I/O latency. The improvements in run-time for graph algorithms is up to 14 × when compared to a single threaded baseline. When compared to multi-threaded implemen-tations, PrefEdge performs up to 80 % faster without the program complexity and the programmer effort needed for multi-threaded graph algorithms

Performance analysis of single board computer clusters

The past few years have seen significant developments in Single Board Computer (SBC) hardware capabilities. These advances in SBCs translate directly into improvements in SBC clusters. In 2018 an individual SBC has more than four times the performance of a 64-node SBC cluster from 2013. This increase in performance has been accompanied by increases in energy efficiency (GFLOPS/W) and value for money (GFLOPS/$). We present systematic analysis of these metrics for three different SBC clusters composed of Raspberry Pi 3 Model B, Raspberry Pi 3 Model B+ and Odroid C2 nodes respectively. A 16-node SBC cluster can achieve up to 60GFLOPS, running at 80W. We believe that these improvements open new computational opportunities, whether this derives from a decrease in the physical volume required to provide a fixed amount of computation power for a portable cluster; or the amount of compute power that can be installed given a fixed budget in expendable compute scenarios. We also present a new SBC cluster construction form factor named Pi Stack; this has been designed to support edge compute applications rather than the educational use-cases favoured by previous methods. The improvements in SBC cluster performance and construction techniques mean that these SBC clusters are realising their potential as valuable developmental edge compute devices rather than just educational curiosities

GDDR: GNN-based data-driven routing

We explore the feasibility of combining Graph Neural Network-based policy architectures with Deep Reinforcement Learning as an approach to problems in systems. This fits particularly well with operations on networks, which naturally take the form of graphs. As a case study, we take the idea of data-driven routing in intradomain traffic engineering, whereby the routing of data in a network can be managed taking into account the data itself. The particular subproblem which we examine is minimising link congestion in networks using knowledge of historic traffic flows. We show through experiments that an approach using Graph Neural Networks (GNNs) performs at least as well as previous work using Multilayer Perceptron architectures. GNNs have the added benefit that they allow for the generalisation of trained agents to different network topologies with no extra work. Furthermore, we believe that this technique is applicable to a far wider selection of problems in systems research.The authors would like to thank Kai Fricke for his input on GDDR project. This research was partly funded by the Alan Turing Institute