Essays about: "Node Scheduling"

Showing result 1 - 5 of 27 essays containing the words Node Scheduling.

  1. 1. The Applicability and Scalability of Graph Neural Networks on Combinatorial Optimization

    University essay from KTH/Matematik (Avd.)

    Author : Peder Hårderup; [2023]
    Keywords : applied mathematics; combinatorial optimization; machine learning; graph neural networks; scalability; tillämpad matematik; kombinatorisk optimering; maskininlärning; grafiska neurala nätverk; skalbarhet;

    Abstract : This master's thesis investigates the application of Graph Neural Networks (GNNs) to address scalability challenges in combinatorial optimization, with a primary focus on the minimum Total Dominating set Problem (TDP) and additionally the related Carrier Scheduling Problem (CSP) in networks of Internet of Things. The research identifies the NP-hard nature of these problems as a fundamental challenge and addresses how to improve predictions on input graphs of sizes much larger than seen during training phase. READ MORE

  3. 2. Highly Available Task Scheduling in Distinctly Branched Directed Acyclic Graphs

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Patrik Zhong; [2023]
    Keywords : Distributed Scheduling; Fault-tolerance; Graph Partitioning; Task Graphs; Dask; Dask Distributed; Data Processing; Distribuerad Schemaläggning; Feltolerans; Grafpartitionering; Uppgiftsgrafer; Dask; Dask Distributed; Dataprocessering;

    Abstract : Big data processing frameworks utilizing distributed frameworks to parallelize the computing of datasets have become a staple part of the data engineering and data science pipelines. One of the more known frameworks is Dask, a widely utilized distributed framework used for parallelizing data processing jobs. READ MORE

  4. 3. An I/O-aware scheduler for containerized data-intensive HPC tasks in Kubernetes-based heterogeneous clusters

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Zheyun Wu; [2022]
    Keywords : Cloud-native; Containers; Kubernetes; High-performance computing HPC ; Data-intensive computing; Task scheduling; Heterogeneous systems; Cloud-native; Containrar; Kubernetes; Högpresterande datoranvändning HPC ; Dataintensiv datoranvändning; Uppgiftsschemaläggning; Heterogena system;

    Abstract : Cloud-native is a new computing paradigm that takes advantage of key characteristics of cloud computing, where applications are packaged as containers. The lifecycle of containerized applications is typically managed by container orchestration tools such as Kubernetes, the most popular container orchestration system that automates the containers’ deployment, maintenance, and scaling. READ MORE

  5. 4. Randomized heuristic scheduling of electrical distribution network maintenance in spatially clustered balanced zones

    University essay from KTH/Geoinformatik

    Author : Carolina Offenbacher; Ellen Thornström; [2022]
    Keywords : Capacitated Vehicle Routing Problem; Electrical distribution network; Heuristic algorithm; Scheduling; Handelsresandeproblemet; Eldistributionsnätverk; Heurustik algortim; Schemaläggning;

    Abstract : Reliable electricity distribution systems are crucial; hence, the maintenance of such systems is highly important, and in Sweden strictly regulated. Poorly planned maintenance scheduling leads unnecessary driving which contributes to increased emissions and costs. READ MORE

  6. 5. Efficient serverless resource scheduling for distributed deep learning.

    University essay from Umeå universitet/Institutionen för datavetenskap

    Author : Johan Sundkvist; [2021]
    Keywords : Serverless; distributed; deep learning; scheduling; regression;

    Abstract : Stemming from the growth and increased complexity of computer vision, natural language processing, and speech recognition algorithms; the need for scalability and fault tolerance of machine learning systems has risen. In order to comply with these demands many have turned their focus towards implementing machine learning on distributed systems. READ MORE