Sökning: "key-value data store"

Visar resultat 1 - 5 av 25 uppsatser innehållade orden key-value data store.

1. 1. High-Performing Cloud Native SW Using Key-Value Storage or Database for Externalized States

   Kandidat-uppsats, Linköpings universitet/Institutionen för datavetenskap

   Författare :Ahmed Sikh; Joel Axén; [2023]
   Nyckelord :cloud-native; externalized states; latency; simulator; Redis; PostgreSQL; moln-nativ; externaliserade tillstånd; latens; simulator; Redis; PostgreSQL;

   Sammanfattning : To meet the demands of 5G and what comes after, telecommunications companies will need to replace their old embedded systems with new technology. One such solution could be to develop cloud-native applications that offer many benefits but are less reliable than embedded systems. LÄS MER

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

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

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

   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

4. 3. Online Sample Selection for Resource Constrained Networked Systems

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

   Författare :Philip Sjösvärd; Samuel Miksits; [2022]
   Nyckelord :Online learning; Sample Selection; Real-time Learning; Random Forest; Reservoir Sampling; Relevance and Redundancy; Binned Distribution; Autoregressive Model; Change Detection; Model Re-computation;

   Sammanfattning : As more devices with different service requirements become connected to networked systems, such as Internet of Things (IoT) devices, maintaining quality of service becomes increasingly difficult. Large data sets can be obtained ahead of time in networks to train prediction models offline, however, resulting in high computational costs. LÄS MER

5. 4. Scaling Apache Hudi by boosting query performance with RonDB as a Global Index : Adopting a LATS data store for indexing

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

   Författare :Ralfs Zangis; [2022]
   Nyckelord :Apache Hudi; Lakehouse; RonDB; Performance; Index; Key-value store; Apache Hudi; Lakehouse; RonDB; Prestanda; Index; Nyckel-värde butik;

   Sammanfattning : The storage and use of voluminous data are perplexing issues, the resolution of which has become more pressing with the exponential growth of information. Lakehouses are relatively new approaches that try to accomplish this while hiding the complexity from the user. LÄS MER

7. 5. Machine Learning Modeling using Heterogeneous Transfer Learning in the Edge Cloud

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

   Författare :Fernando Garcia Sanz; [2021]
   Nyckelord :Deep learning; Edge cloud; Heterogeneous transfer learning; Machine Learning; Service performance; ; Deep learning; Edge cloud; heterogen överföringsinlärning; maskininlärning; serviceprestanda; ;

   Sammanfattning : The dynamic nature of the edge cloud and future network infrastructures is another challenge to be added when modeling end-to-end service performance using machine learning. That is, a model that has been trained for one specific environment may see reductions in prediction accuracy over time due to e.g., routing, migration, or scaling decisions. LÄS MER