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Quality-aware Scheduling for Key-value Data Stores

Part of the SpringerBriefs in Computer Science Book Series

DOI: 10.1007/978-3-662-47306-1

ISBN: 978-3-662-47305-4

Publisher: Springer Berlin Heidelberg - Springer Journals

Jun 6, 2015

LitStream

Chapter 1Introduction

Xu, Chen; Zhou, Aoying

Pages: 1 –9

[Key-value stores provide a distributed solution to cloud computing and big data management. Generally, key-value stores employ a weak consistency model, which relaxes the data consistency to improve system performance on query response. However, a drawback is that data accessed by users might be...
LitStream

Chapter 2Literature and Research Review

Xu, Chen; Zhou, Aoying

Pages: 11 –23

[Quality-aware scheduling is a novel term employed in our study. However, the related work on this topic appeared in the 1990s. In this chapter, we briefly summarize the literature and research work on quality-aware scheduling. On the one hand, we overview the typical metrics for quality-aware...
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Chapter 3Problem Overview

Xu, Chen; Zhou, Aoying

Pages: 25 –35

[How to define the quality-aware scheduling problem in key-value stores is the foundation of studying the scheduling strategies. In this chapter (Part of this chapter are reprinted from Xu et al., Distrib Parallel Databases 32(4):535–581, 2014 [1], with kind permission from Springer...
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Chapter 4Scheduling for State-Transfer Updates

Xu, Chen; Zhou, Aoying

Pages: 37 –63

[Under the state-transfer update model, the propagated updates involve an entirely new value. The arrival of a new update to a certain record makes any pending update to that same record worthless. That is, a replica can converge simply by applying the newest update but skipping any intermediate...
LitStream

Chapter 5Scheduling for Operation-Transfer Updates

Xu, Chen; Zhou, Aoying

Pages: 65 –81

[Under the operation-transfer update model, the propagated updates involve partial content rather than the entire value. Hence, each replica basically reconstructs the current value of a data object from a history of propagated updates. This manner is suited for key-value stores such as PNUTS,...
LitStream

Chapter 6AQUAS: A Quality-Aware Scheduler

Xu, Chen; Zhou, Aoying

Pages: 83 –94

[In order to study the role of quality-aware scheduling in practice, we introduce a quality-aware scheduler named AQUAS (Xu et al., AQUAS: a quality-aware scheduler for NoSQL data stores 1210–1213, 2014 [1]) which has been integrated into Cassandra. This prototype employs the strategies proposed...
LitStream

Chapter 7Conclusion and Future Work

Xu, Chen; Zhou, Aoying

Pages: 95 –97

[The chapters above clearly present the problem of quality-aware scheduling in key-value data stores as well as the related literature and researches, the scheduling strategies for both state-transfer and operation-transfer updates, and a prototype of quality-aware scheduler as well as a typical...

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