{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/95402"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/95402","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Adaptive batching of streams to enhance throughput and to support dynamic load balancing","abstract":"As data permeates all disciplines, the role of big data becomes increasingly important. Sensors, IoT devices, social networks, and online transactions are all generating data that can be monitored constantly to enable a business to identify opportunity to enhance customer service and increase revenue. This need for real-time processing of big data has led to the development of frameworks for distributed stream processing in clusters. It is important for such frameworks to be resilient against variable operating conditions such as server load variation, changes in data ingestion rates, and workload characteristics. In this thesis, we explore the effects of the batch size on the performance of streaming workloads by developing an adaptive batching framework and building load-balancing algorithms on top of this framework. We explore the idea of using a combination of adaptive batching of tuples and dynamic tuple dispatching to improve the throughput and load-distribution of the workload. We show through experiments that the system is able to be resilient and robust under varying operating conditions.","abstract_html":"As data permeates all disciplines, the role of big data becomes increasingly important. Sensors, IoT devices, social networks, and online transactions are all generating data that can be monitored constantly to enable a business to identify opportunity to enhance customer service and increase revenue. This need for real-time processing of big data has led to the development of frameworks for distributed stream processing in clusters. It is important for such frameworks to be resilient against variable operating conditions such as server load variation, changes in data ingestion rates, and workload characteristics. In this thesis, we explore the effects of the batch size on the performance of streaming workloads by developing an adaptive batching framework and building load-balancing algorithms on top of this framework. We explore the idea of using a combination of adaptive batching of tuples and dynamic tuple dispatching to improve the throughput and load-distribution of the workload. We show through experiments that the system is able to be resilient and robust under varying operating conditions.","abstract_has_math":false,"creators":["Jayakumar, Anirudh"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Abdelzaher, Tarek F."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2017,"date_issued":"2017-03-01T15:49:29Z","date_published":"2017-03-01T15:49:29Z","updated_at":"2026-07-22T22:26:37Z","subjects":["Load-balancing","Apache storm","Stream processing"],"languages":["en"],"rights":["Copyright 2016 Anirudh Jayakumar"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/95402","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Abdelzaher, Tarek F."]},{"key":"dc:creator","label":"Author","values":["Jayakumar, Anirudh"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2017-03-01T15:49:29Z","2016-12-06","2016-12"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Load-balancing","Apache storm","Stream processing"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2016 Anirudh Jayakumar"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/95402"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["As data permeates all disciplines, the role of big data becomes increasingly important. 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This need for real-time processing of big data has led to the development of frameworks for distributed stream processing in clusters. It is important for such frameworks to be resilient against variable operating conditions such as server load variation, changes in data ingestion rates, and workload characteristics. In this thesis, we explore the effects of the batch size on the performance of streaming workloads by developing an adaptive batching framework and building load-balancing algorithms on top of this framework. We explore the idea of using a combination of adaptive batching of tuples and dynamic tuple dispatching to improve the throughput and load-distribution of the workload. We show through experiments that the system is able to be resilient and robust under varying operating conditions.","Submission original under an indefinite embargo labeled 'Open Access'. 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