Sökning: "Computing resource"

Visar resultat 21 - 25 av 179 uppsatser innehållade orden Computing resource.

1. 21. Predicting resource usage on a Kubernetes platform using Machine Learning Methods

   Master-uppsats, Karlstads universitet/Institutionen för matematik och datavetenskap (from 2013)

   Författare :Arvid Gördén; [2023]
   Nyckelord :Master thesis; Kubernetes; scaling; resource management; horizontal pod autoscaler; vertical scaling; machine learning; Masterarbete; Kubernetes; scaling; resurshantering; horisontell pod autoscaler; vertikal scaling; maskininlärning;

   Sammanfattning : Cloud computing and containerization has been on the rise in recent years and have become important areas of research and development in the field of computer science. One of the challenges in distributed and cloud computing is to predict the resource utilization of the nodes that run the applications and services. LÄS MER

3. 22. Auto-Tuning Apache Spark Parameters for Processing Large Datasets

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Shidi Zhou; [2023]
   Nyckelord :Apache Spark; Cloud Environment; Spark Configuration Parameter; Resource Utilization; Ridge Regression; Elastic Net; Random Forest; Deep Neural Network; Bayesian Optimization; Particle Swarm Optimization.; Apache Spark; Molnmiljö; Apache Spark konfigurationsparameter; Resursutnyttjande; Ridge-regression; Elastisk nät; Slumpskog; Djupt neuralt nätverk; Bayesiansk optimering; Partikelsvärmsoptimering.;

   Sammanfattning : Apache Spark is a popular open-source distributed processing framework that enables efficient processing of large amounts of data. Apache Spark has a large number of configuration parameters that are strongly related to performance. Selecting an optimal configuration for Apache Spark application deployed in a cloud environment is a complex task. LÄS MER

4. 23. A Meta-narrative Review of Recent Advancements in Material Footprint Methodologies

   Master-uppsats, KTH/Hållbar utveckling, miljövetenskap och teknik

   Författare :John Henry Nylén; [2023]
   Nyckelord :;

   Sammanfattning : The circular economy is a strategy that aims at closing material loops and lower resource consumption. Resource use indicators, like the material footprint, can play an important role in this transition. LÄS MER

5. 24. Probabilistic Multi-Modal Data Fusion and Precision Coordination for Autonomous Mobile Systems Navigation : A Predictive and Collaborative Approach to Visual-Inertial Odometry in Distributed Sensor Networks using Edge Nodes

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Isabella Luppi; [2023]
   Nyckelord :Distributed Sensor Networks; Point Cloud Processing; Bounding Box Fitting; Trajectory Tracking; Distributed Estimation; Predictive Estimation; Edge-Computing; Reti di Sensori Distribuiti; Elaborazione di Nuvole di Punti; Riquadri di Delimitazione; Tracciamento della Traiettoria; Stima Distribuita; Stima Predittiva; Calcolo Distribuito.; Distribuerade Sensornätverk; Bearbetning av Punktmoln; Anpassning av Begränsningsruta; Trajektorieuppföljning; Distribuerad Uppskattning; Prediktiv Uppskattning; Edge-datorbehandling;

   Sammanfattning : This research proposes a novel approach for improving autonomous mobile system navigation in dynamic and potentially occluded environments. The research introduces a tracking framework that combines data from stationary sensing units and on-board sensors, addressing challenges of computational efficiency, reliability, and scalability. LÄS MER

7. 25. Intelligent autoscaling in Kubernetes : the impact of container performance indicators in model-free DRL methods

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Tommaso Praturlon; [2023]
   Nyckelord :Cloud computing; container autoscaling; resource optimisation; Deep Reinforcement Learning; Actor-Critic; Kubernetes; service mesh; Cloud computing; container autoscaling; Optimering av resurser; Deep Reinforcement Learning; Actor-Critic; Kubernetes; service mesh;

   Sammanfattning : A key challenge in the field of cloud computing is to automatically scale software containers in a way that accurately matches the demand for the services they run. To manage such components, container orchestrator tools such as Kubernetes are employed, and in the past few years, researchers have attempted to optimise its autoscaling mechanism with different approaches. LÄS MER