Sökning: "data-intensive computing"
Visar resultat 1 - 5 av 15 avhandlingar innehållade orden data-intensive computing.
1. Methods and Algorithms for Data-Intensive Computing : Streams, Graphs, and Geo-Distribution
Sammanfattning : Struggling with the volume and velocity of Big Data has attracted lots of interest towards stream processing paradigm, a paradigm in the area of data-intensive computing that provides methods and solutions to process data in motion. Today's Big Data includes geo-distributed data sources. LÄS MER
2. Performance Optimization Techniques and Tools for Data-Intensive Computation Platforms : An Overview of Performance Limitations in Big Data Systems and Proposed Optimizations
Sammanfattning : Big data processing has recently gained a lot of attention both from academia and industry. The term refers to tools, methods, techniques and frameworks built to collect, store, process and analyze massive amounts of data. Big data can be structured, unstructured or semi-structured. LÄS MER
3. Resource Allocation for Data-Intensive Services in the Cloud
Sammanfattning : Cloud computing has become ubiquitous due to its resource flexibility and cost efficiency. Resource flexibility allows Cloud users to elastically scale their Cloud resources, for instance, by horizontally scaling the number of virtual machines allocated to each application as the application demands change. LÄS MER
4. Intelligence Partitioning for IoT : Design Space Exploration for a Data Intensive IoT Node
Sammanfattning : The technological shift towards the Internet of Everything has resulted in an ever-increasing interest in smart sensor nodes. The required deployment of these nodes in a variety of environments, powered by constrained energy sources such as energy harvester or conventional batteries, is reflected in the significant constraints in terms of energy consumption for the smart sensor node. LÄS MER
5. Application-aware resource management for datacenters
Sammanfattning : High Performance Computing (HPC) and Cloud Computing datacenters are extensively used to steer and solve complex problems in science, engineering, and business, such as calculating correlations and making predictions. Already in a single datacenter server, there are thousands of hardware and software metrics – Key Performance Indicators (KPIs) – that individually and aggregated can give insight in the performance, robustness, and efficiency of the datacenter and the provisioned applications. LÄS MER