Search for dissertations about: "cloud computer"

Showing result 1 - 5 of 166 swedish dissertations containing the words cloud computer.

2. 1. Outsourcing Computations to a Cloud That You Don't Trust

   Author : Georgia Tsaloli; Chalmers tekniska högskola; []
   Keywords : NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; differential privacy; function secret sharing; homomorphic secret sharing; verifiable computation; privacy-preservation; public verifiability;

   Abstract : In many application scenarios, data need to be collected, stored and processed. Often sensitive data are collected from IoT devices, which are constrained regarding their resources, and, thus, remote, untrusted cloud servers are required to perform the computations. READ MORE

4. 2. Performance problem diagnosis in cloud infrastructures

   Author : Olumuyiwa Ibidunmoye; Erik Elmroth; Guilliano Casale; Viktoria Fodor; Henrik Björklund; Umeå universitet; []
   Keywords : TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Systems Performance; Performance anomalies; Performance bottlenecks; Cloud infrastructures; Cloud Computing; Cloud Services; Cloud Computing Performance; Performance problems; Performance anomaly detection; Performance bottleneck identification; Performance Root-cause Analysis; Computer Systems; datorteknik; Computer Science; datalogi;

   Abstract : Cloud datacenters comprise hundreds or thousands of disparate application services, each having stringent performance and availability requirements, sharing a finite set of heterogeneous hardware and software resources. The implication of such complex environment is that the occurrence of performance problems, such as slow application response and unplanned downtimes, has become a norm rather than exception resulting in decreased revenue, damaged reputation, and huge human-effort in diagnosis. READ MORE

5. 3. Explainable and Resource-Efficient Stream Processing Through Provenance and Scheduling

   Author : Dimitrios Palyvos-Giannas; Chalmers tekniska högskola; []
   Keywords : NATURVETENSKAP; NATURAL SCIENCES; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Data Streaming; Scheduling; Provenance; Stream Processing;

   Abstract : In our era of big data, information is captured at unprecedented volumes and velocities, with technologies such as Cyber-Physical Systems making quick decisions based on the processing of streaming, unbounded datasets. In such scenarios, it can be beneficial to process the data in an online manner, using the stream processing paradigm implemented by Stream Processing Engines (SPEs). READ MORE

6. 4. Secure and Privacy-Preserving Cloud-Assisted Computing

   Author : Georgia Tsaloli; Chalmers tekniska högskola; []
   Keywords : NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; differential privacy; decentralization; verifiability; privacy-preservation; secure aggregation; secret sharing; cloud computing; privacy;

   Abstract : Smart devices such as smartphones, wearables, and smart appliances collect significant amounts of data and transmit them over the network forming the Internet of Things (IoT). Many applications in our daily lives (e.g., health, smart grid, traffic monitoring) involve IoT devices that often have low computational capabilities. READ MORE

7. 5. Clustering in the Big Data Era: methods for efficient approximation, distribution, and parallelization

   Author : Amir Keramatian; Chalmers tekniska högskola; []
   Keywords : NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; NATURVETENSKAP; NATURAL SCIENCES; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Clustering; Approximation-based synopsis; Distributed and Parallel Processing; Applied ML;

   Abstract : Data clustering is an unsupervised machine learning task whose objective is to group together similar items. As a versatile data mining tool, data clustering has numerous applications, such as object detection and localization using data from 3D laser-based sensors, finding popular routes using geolocation data, and finding similar patterns of electricity consumption using smart meters. READ MORE