Locality-aware distributed state partitioning for stream processing systems
| buir.advisor | Gedik, Buğra | |
| dc.contributor.author | Şahin, Muhammed Yağmur | |
| dc.date.accessioned | 2016-12-05T11:00:45Z | |
| dc.date.available | 2016-12-05T11:00:45Z | |
| dc.date.copyright | 2016-10 | |
| dc.date.issued | 2016-10 | |
| dc.date.submitted | 2016-12-01 | |
| dc.department | Department of Computer Engineering | en_US |
| dc.description | Cataloged from PDF version of article. | en_US |
| dc.description | Thesis (M.S.): Bilkent University, Department of Computer Engineering, İhsan Doğramacı Bilkent University, 2016. | en_US |
| dc.description | Includes bibliographical references (leaves 46-48). | en_US |
| dc.description.abstract | Today, there are many applications that deal with high-volume data streams. These distributed stream processing applications process data on-the-fly and provide real-time distributed computing for big data. Due to the volume of data they process, some of these applications make use of data parallel nodes. The state management for distributed nodes in these applications is an important task to handle, because of different use cases such as: dealing with node failures, checkpointing, data enrichment, and re-partitioning. Therefore, distributed stream processing applications need a state management mechanism. In this thesis, we present a locality-aware state management mechanism for distributed stream processing applications. The proposed mechanism provides a transparent locality-aware data partitioning and state management system for distributed stream processing applications. The mechanism partitions data while preserving locality and handles state transfer among nodes transparently, in order to adapt to potential changes in the partitioning. In addition to this, it provides operators with a high-performance state management facility that can tackle check-pointing scenarios. The idea is implemented as a pluggable library for the open-source, distributed stream-processing engine, Apache Storm. | en_US |
| dc.description.degree | M.S. | en_US |
| dc.description.statementofresponsibility | by Muhammed Yağmur Şahin. | en_US |
| dc.format.extent | x, 48 leaves : charts (some color) | en_US |
| dc.identifier.itemid | B154854 | |
| dc.identifier.uri | http://hdl.handle.net/11693/32564 | |
| dc.language.iso | English | en_US |
| dc.publisher | Bilkent University | en_US |
| dc.rights | info:eu-repo/semantics/openAccess | en_US |
| dc.subject | Locality-aware state partitioning | en_US |
| dc.subject | Consistent Hash | en_US |
| dc.subject | Apache Storm | en_US |
| dc.title | Locality-aware distributed state partitioning for stream processing systems | en_US |
| dc.title.alternative | Veri katarı işleme sistemleri için veri yerelliği farkındalığı olan dağıtık durum bölümlendirmesi | en_US |
| dc.type | Thesis | en_US |