Sökning: "Vladimir Vlassov"

Visar resultat 1 - 5 av 19 avhandlingar innehållade orden Vladimir Vlassov.

1. 1. Scalable Streaming Graph and Time Series Analysis Using Partitioning and Machine Learning

   Författare :Zainab Abbas; Vladimir Vlassov; Peter Van Roy; Paris Carbone; Vasiliki Kalavri; Vincenzo Massimiliano Gulisano; KTH; []
   Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Stream processing; graph processing; time series; big data; machine learning; Informations- och kommunikationsteknik; Information and Communication Technology;

   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. 2. Enabling and Achieving Self-Management for Large Scale Distributed Systems : Platform and Design Methodology for Self-Management

   Författare :Ahmad Al-Shishtawy; Vladimir Vlassov; Seif Haridi; Peter Van Roy; KTH; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; Autonomic Computing; Self-Management; Distributed Systems; Computer science; Datavetenskap;

   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. 3. On Service Optimization in Community Network Micro-Clouds

   Författare :Nuno Apolonia; Leandro Navarro; Sarunas Girdzijauskas; Felix Freitag; Vladimir Vlassov; Dino Lopez; KTH; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; micro-clouds; Community Networks; cloud services; social networks; overlay networks; distributed systems; Datalogi; Computer Science;

   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. 4. Performance Characterization and Optimization of In-Memory Data Analytics on a Scale-up Server

   Författare :Ahsan Javed Awan; Eduard Ayguade; Mats Brorsson; Vladimir Vlassov; Lieven Eeckhout; KTH; []
   Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Workload Characterization; Big Data Analytics; Multicore Performance; Apache Spark; Near Data Processing; NUMA; Hyperthreading; Prefetchers; Coherently attached accelerators; Informations- och kommunikationsteknik; Information and Communication Technology;

   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

7. 5. Performance Characterization of In-Memory Data Analytics on a Scale-up Server

   Författare :Ahsan Javed Awan; Mats Brorsson; Vladimir Vlassov; Eduard Ayguade; Boris Grot; KTH; []
   Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Informations- och kommunikationsteknik; Information and Communication Technology;

   Sammanfattning : The sheer increase in 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 at understanding the performance of in-memory data analytics with Spark on modern scale-up servers. LÄS MER