Fast, elastic storage for the cloud
Abstract/Contents
- Abstract
- Cloud computing promises high performance, cost-efficiency, and elasticity --- three essential goals when processing exponentially growing datasets. To meet these goals, cloud platforms must provide each application with the right amount and balance of compute and fast storage resources (e.g., NVMe Flash storage). However, providing the right resources to applications is challenging today because server machines have a fixed ratio of compute and storage resources, remote access to fast storage leads to significant performance and cost overheads, and storage requirements vary significantly over time and across applications. This dissertation focuses on how to build high performance, cost-effective, and easy-to-use cloud storage systems. First, we discuss how to provide fast access to remote Flash storage so that the balance of compute and storage allocation is not limited by the physical characteristics of server hardware. We present ReFlex, a custom network-storage operating system that provides fast access to modern Flash storage over commodity cloud networks. ReFlex enables storage devices to be shared among multiple tenants with predictable performance. Second, we discuss how to implement intelligent allocation of storage resources. We present Pocket, a distributed storage service that combines the fast remote data access in ReFlex with automatic resource allocation for serverless analytics workloads. Finally, we discuss the potential of using machine learning to automate resource allocation decisions with a system called Selecta that recommends a near-optimal cloud resource configuration for a job based on sparse training data collected across multiple jobs and configurations.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2019; ©2019 |
| Publication date | 2019; 2019 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Klimovic, Ana |
|---|---|
| Degree supervisor | Kozyrakis, Christoforos, 1974- |
| Thesis advisor | Kozyrakis, Christoforos, 1974- |
| Thesis advisor | Horowitz, Mark (Mark Alan) |
| Thesis advisor | Zaharia, Matei |
| Degree committee member | Horowitz, Mark (Mark Alan) |
| Degree committee member | Zaharia, Matei |
| Associated with | Stanford University, Department of Electrical Engineering. |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Ana Klimovic. |
|---|---|
| Note | Submitted to the Department of Electrical Engineering. |
| Thesis | Thesis Ph.D. Stanford University 2019. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2019 by Ana Klimovic
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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