Browsing by Author "Buchaca Prats, David"
Now showing items 1-11 of 11
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A highly parameterizable framework for Conditional Restricted Boltzmann Machine based workloads accelerated with FPGAs and OpenCL
(Elsevier, 2020-03-01)
Article
Open AccessConditional Restricted Boltzmann Machine (CRBM) is a promising candidate for a multidimensional system modeling that can learn a probability distribution over a set of data. It is a specific type of an artificial neural ... -
A multilayer extension of the similarity neural network
(Universitat Politècnica de Catalunya, 2014-11-24)
Master thesis
Open AccessAquest projecte ajunta idees de les radial basis functions, i el multilayer perceptron per a desenvolupar una altra arquitectura de xarxa neuronal artificial i un mètode per a poder-la entrenar. És una extensió de la ... -
Automatic generation of workload profiles using unsupervised learning pipelines
(2017-12-27)
Article
Open AccessThe complexity of resource usage and power consumption on cloud-based applications makes the understanding of application behavior through expert examination difficult. The difficulty increases when applications are seen ... -
Improving maritime traffic emission estimations on missing data with CRBMs
(Elsevier, 2020-07-07)
Article
Open AccessMaritime traffic emissions are a major concern to governments as they heavily impact the Air Quality in coastal cities. Ships use the Automatic Identification System (AIS) to continuously report position and speed among ... -
Learning workload behaviour models from monitored time-series for resource estimation towards data center optimization
(Universitat Politècnica de Catalunya, 2021-01-14)
Doctoral thesis
Open AccessIn recent years there has been an extraordinary growth of the demand of Cloud Computing resources executed in Data Centers. Modern Data Centers are complex systems that need management. As distributed computing systems ... -
Proactive container auto-scaling for cloud native machine learning services
(Institute of Electrical and Electronics Engineers (IEEE), 2020)
Conference lecture
Open AccessUnderstanding the resource usage behaviors of the ever-increasing machine learning workloads are critical to cloud providers offering Machine Learning (ML) services. Capable of auto-scaling resources for customer workloads ... -
Sequence-to-sequence models for workload interference prediction on batch processing datacenters
(Elsevier, 2020-09)
Article
Open AccessCo-scheduling of jobs in data centers is a challenging scenario where jobs can compete for resources, leading to severe slowdowns or failed executions. Efficient job placement on environments where resources are shared ... -
Stopping criteria in contrastive divergence: Alternatives to the reconstruction error
(2014)
Conference report
Open AccessRestricted Boltzmann Machines (RBMs) are general unsupervised learning devices to ascertain generative models of data distributions. RBMs are often trained using the Contrastive Divergence learning algorithm (CD), an ... -
Theta-Scan: Leveraging behavior-driven forecasting for vertical auto-scaling in container cloud
(Institute of Electrical and Electronics Engineers (IEEE), 2021)
Conference report
Open AccessDetection of behavior patterns on resource usage in containerized Cloud applications is necessary for proper resource provisioning. Applications can use CPU/Memory with repetitive patterns, following a trend over time ... -
Weighted contrastive divergence
(2019-06)
Article
Open AccessLearning algorithms for energy based Boltzmann architectures that rely on gradient descent are in general computationally prohibitive, typically due to the exponential number of terms involved in computing the partition ... -
You only run once: Spark auto-tuning from a single run
(2020-12)
Article
Open AccessTuning configurations of Spark jobs is not a trivial task. State-of-the-art auto-tuning systems are based on iteratively running workloads with different configurations. During the optimization process, the relevant features ...
