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oai:dspace.mit.edu:1721.1/1625462025-08-28T03:08:00Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023

Szekely, Ariel Kaashoek, M. Frans Chang, Ryan Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 2025-08-27T14:32:28Z 2025-08-27T14:32:28Z 2025-05 2025-06-23T14:01:18.866Z https://hdl.handle.net/1721.1/162546 SigmaOS is a multi-tenant cloud operating system designed for efficient orchestration of fault-tolerant, burst-parallel workloads. It provides users with isolated cloud environments called realms, where resources are accessed through a Unix-like filesystem interface, and supports applications built from procs—lightweight, rapidly-spawnable programs that can be both short-lived for bursty tasks or long-running and stateful for persistent services. However, the current prototype exhibits performance bottlenecks that hinder its scalability for larger, more demanding applications. This thesis addresses these limitations by introducing two key optimizations: (1) a rearchitected watch API, enhancing its efficiency and scalability for monitoring directory changes crucial for inter-proc coordination and event notification, and (2) a new ft/task server, providing a robust and high-performance mechanism for managing fault-tolerant bags of tasks, essential for applications like MapReduce. Through these enhancements, this work demonstrates significant improvements in SigmaOS’s performance on the MapReduce benchmark, showcasing improved scaling capabilities for larger cluster deployments, larger inputs, and more granular tasks. These optimizations are crucial steps towards enabling SigmaOS to effectively realize its vision as a scalable and performant platform for complex cloud workloads. M.Eng. Massachusetts Institute of Technology In Copyright - Educational Use Permitted Copyright retained by author(s) https://rightsstatements.org/page/InC-EDU/1.0/ Optimizing SigmaOS for Efficient Orchestration of Fault-Tolerant, Burst-Parallel Workloads Thesis application/pdf Master Master of Engineering in Electrical Engineering and Computer Science Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="956fbcb1-de5a-4f4c-957b-57e3e1b2f867"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Title>Optimizing SigmaOS for Efficient Orchestration of Fault-Tolerant, Burst-Parallel Workloads</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2025-05</PublicationDate> <Authors> <Author> <DisplayName>Chang, Ryan</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>https://rightsstatements.org/page/InC-EDU/1.0/</License> <Abstract>SigmaOS is a multi-tenant cloud operating system designed for efficient orchestration of fault-tolerant, burst-parallel workloads. It provides users with isolated cloud environments called realms, where resources are accessed through a Unix-like filesystem interface, and supports applications built from procs—lightweight, rapidly-spawnable programs that can be both short-lived for bursty tasks or long-running and stateful for persistent services. However, the current prototype exhibits performance bottlenecks that hinder its scalability for larger, more demanding applications. This thesis addresses these limitations by introducing two key optimizations: (1) a rearchitected watch API, enhancing its efficiency and scalability for monitoring directory changes crucial for inter-proc coordination and event notification, and (2) a new ft/task server, providing a robust and high-performance mechanism for managing fault-tolerant bags of tasks, essential for applications like MapReduce. Through these enhancements, this work demonstrates significant improvements in SigmaOS’s performance on the MapReduce benchmark, showcasing improved scaling capabilities for larger cluster deployments, larger inputs, and more granular tasks. These optimizations are crucial steps towards enabling SigmaOS to effectively realize its vision as a scalable and performant platform for complex cloud workloads.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>