Sökning: "Node Scheduling"
Visar resultat 1 - 5 av 28 uppsatser innehållade orden Node Scheduling.
1. The Applicability and Scalability of Graph Neural Networks on Combinatorial Optimization
Master-uppsats, KTH/Matematik (Avd.)Sammanfattning : 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. LÄS MER
2. Highly Available Task Scheduling in Distinctly Branched Directed Acyclic Graphs
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : 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. LÄS MER
3. An I/O-aware scheduler for containerized data-intensive HPC tasks in Kubernetes-based heterogeneous clusters
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : 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. LÄS MER
4. Randomized heuristic scheduling of electrical distribution network maintenance in spatially clustered balanced zones
Master-uppsats, KTH/GeoinformatikSammanfattning : 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. LÄS MER
5. Efficient serverless resource scheduling for distributed deep learning.
Master-uppsats, Umeå universitet/Institutionen för datavetenskapSammanfattning : 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. LÄS MER