Sökning: "Vlassov Vlassov"
Visar resultat 1 - 5 av 21 avhandlingar innehållade orden Vlassov Vlassov.
1. Self-Management for Large-Scale Distributed Systems
Sammanfattning : Autonomic computing aims at making computing systems self-managing by using autonomic managers in order to reduce obstacles caused by management complexity. This thesis presents results of research on self-management for large-scale distributed systems. LÄS MER
2. Scalable Streaming Graph and Time Series Analysis Using Partitioning and Machine Learning
Sammanfattning : Recent years have witnessed a massive increase in the amount of data generated by the Internet of Things (IoT) and social media. Processing huge amounts of this data poses non-trivial challenges in terms of the hardware and performance requirements of modern-day applications. LÄS MER
3. Enabling and Achieving Self-Management for Large Scale Distributed Systems : Platform and Design Methodology for Self-Management
Sammanfattning : Autonomic computing is a paradigm that aims at reducing administrative overhead by using autonomic managers to make applications self-managing. To better deal with large-scale dynamic environments; and to improve scalability, robustness, and performance; we advocate for distribution of management functions among several cooperative autonomic managers that coordinate their activities in order to achieve management objectives. LÄS MER
4. On Service Optimization in Community Network Micro-Clouds
Sammanfattning : Internet coverage in the world is still weak and local communities are required to come together and build their own network infrastructures. People collaborate for the common goal of accessing the Internet and cloud services by building Community networks (CNs).The use of Internet cloud services has grown over the last decade. LÄS MER
5. Performance Characterization and Optimization of In-Memory Data Analytics on a Scale-up Server
Sammanfattning : The sheer increase in the volume of data over the last decade has triggered research in cluster computing frameworks that enable web enterprises to extract big insights from big data. While Apache Spark defines the state of the art in big data analytics platforms for (i) exploiting data-flow and in-memory computing and (ii) for exhibiting superior scale-out performance on the commodity machines, little effort has been devoted to understanding the performance of in-memory data analytics with Spark on modern scale-up servers. LÄS MER