1,812 research outputs found
Auto-tuning Distributed Stream Processing Systems using Reinforcement Learning
Fine tuning distributed systems is considered to be a craftsmanship, relying
on intuition and experience. This becomes even more challenging when the
systems need to react in near real time, as streaming engines have to do to
maintain pre-agreed service quality metrics. In this article, we present an
automated approach that builds on a combination of supervised and reinforcement
learning methods to recommend the most appropriate lever configurations based
on previous load. With this, streaming engines can be automatically tuned
without requiring a human to determine the right way and proper time to deploy
them. This opens the door to new configurations that are not being applied
today since the complexity of managing these systems has surpassed the
abilities of human experts. We show how reinforcement learning systems can find
substantially better configurations in less time than their human counterparts
and adapt to changing workloads
Deep Learning Data and Indexes in a Database
A database is used to store and retrieve data, which is a critical component for any software application. Databases requires configuration for efficiency, however, there are tens of configuration parameters. It is a challenging task to manually configure a database. Furthermore, a database must be reconfigured on a regular basis to keep up with newer data and workload. The goal of this thesis is to use the query workload history to autonomously configure the database and improve its performance. We achieve proposed work in four stages: (i) we develop an index recommender using deep reinforcement learning for a standalone database. We evaluated the effectiveness of our algorithm by comparing with several state-of-the-art approaches, (ii) we build a real-time index recommender that can, in real-time, dynamically create and remove indexes for better performance in response to sudden changes in the query workload, (iii) we develop a database advisor. Our advisor framework will be able to learn latent patterns from a workload. It is able to enhance a query, recommend interesting queries, and summarize a workload, (iv) we developed LinkSocial, a fast, scalable, and accurate framework to gain deeper insights from heterogeneous data
An Efficient Online Prediction of Host Workloads Using Pruned GRU Neural Nets
Host load prediction is essential for dynamic resource scaling and job
scheduling in a cloud computing environment. In this context, workload
prediction is challenging because of several issues. First, it must be accurate
to enable precise scheduling decisions. Second, it must be fast to schedule at
the right time. Third, a model must be able to account for new patterns of
workloads so it can perform well on the latest and old patterns. Not being able
to make an accurate and fast prediction or the inability to predict new usage
patterns can result in severe outcomes such as service level agreement (SLA)
misses. Our research trains a fast model with the ability of online adaptation
based on the gated recurrent unit (GRU) to mitigate the mentioned issues. We
use a multivariate approach using several features, such as memory usage, CPU
usage, disk I/O usage, and disk space, to perform the predictions accurately.
Moreover, we predict multiple steps ahead, which is essential for making
scheduling decisions in advance. Furthermore, we use two pruning methods: L1
norm and random, to produce a sparse model for faster forecasts. Finally,
online learning is used to create a model that can adapt over time to new
workload patterns
AdaChain: A Learned Adaptive Blockchain
This paper presents AdaChain, a learning-based blockchain framework that
adaptively chooses the best permissioned blockchain architecture in order to
optimize effective throughput for dynamic transaction workloads. AdaChain
addresses the challenge in the Blockchain-as-a-Service (BaaS) environments,
where a large variety of possible smart contracts are deployed with different
workload characteristics. AdaChain supports automatically adapting to an
underlying, dynamically changing workload through the use of reinforcement
learning. When a promising architecture is identified, AdaChain switches from
the current architecture to the promising one at runtime in a way that respects
correctness and security concerns. Experimentally, we show that AdaChain can
converge quickly to optimal architectures under changing workloads,
significantly outperform fixed architectures in terms of the number of
successfully committed transactions, all while incurring low additional
overhead
Machine learning regression to boost scheduling performance in hyper-scale cloud-computing data centres
Data centres increase their size and complexity due to the increasing amount of heterogeneous work loads and patterns to be served. Such a mix of various purpose workloads makes the optimisation of
resource management systems according to temporal or application-level patterns difficult. Data centre operators have developed multiple resource-management models to improve scheduling perfor mance in controlled scenarios. However, the constant evolution of the workloads makes the utilisation
of only one resource-management model sub-optimal in some scenarios.
In this work, we propose: (a) a machine learning regression model based on gradient boosting to pre dict the time a resource manager needs to schedule incoming jobs for a given period; and (b) a resource
management model, Boost, that takes advantage of this regression model to predict the scheduling time
of a catalogue of resource managers so that the most performant can be used for a time span.
The benefits of the proposed resource-management model are analysed by comparing its scheduling
performance KPIs to those provided by the two most popular resource-management models: two level, used by Apache Mesos, and shared-state, employed by Google Borg. Such gains are empirically eval uated by simulating a hyper-scale data centre that executes a realistic synthetically generated workload
that follows real-world trace patternsMinisterio de Ciencia e Innovación RTI2018-098062-A-I0
Addendum to Informatics for Health 2017: Advancing both science and practice
This article presents presentation and poster abstracts that were mistakenly omitted from the original publication
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