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Harmony: Towards automated self-adaptive consistency in cloud storage (2012)
| Venue: | in: 2012 IEEE International Conference on Cluster Computing (CLUSTER’12 |
| Citations: | 8 - 5 self |
BibTeX
@INPROCEEDINGS{Chihoub12harmony:towards,
author = {Houssem-eddine Chihoub and Shadi Ibrahim and Gabriel Antoniu},
title = {Harmony: Towards automated self-adaptive consistency in cloud storage},
booktitle = {in: 2012 IEEE International Conference on Cluster Computing (CLUSTER’12},
year = {2012},
pages = {293--301}
}
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Abstract
Abstract—In just a few years cloud computing has become a very popular paradigm and a business success story, with storage being one of the key features. To achieve high data availability, cloud storage services rely on replication. In this context, one major challenge is data consistency. In contrast to traditional approaches that are mostly based on strong consis-tency, many cloud storage services opt for weaker consistency models in order to achieve better availability and performance. This comes at the cost of a high probability of stale data being read, as the replicas involved in the reads may not always have the most recent write. In this paper, we propose a novel approach, named Harmony, which adaptively tunes the consistency level at run-time according to the application requirements. The key idea behind Harmony is an intelligent estimation model of stale reads, allowing to elastically scale up or down the number of replicas involved in read operations to maintain a low (possibly zero) tolerable fraction of stale reads. As a result, Harmony can meet the desired consistency of the applications while achieving good performance. We have implemented Harmony and performed extensive evaluations with the Cassandra cloud storage on Grid’5000 testbed and on Amazon EC2. The results show that Harmony can achieve good performance without exceeding the tolerated number of stale reads. For instance, in contrast to the static eventual consistency used in Cassandra, Harmony reduces the stale data being read by almost 80 % while adding only minimal latency. Meanwhile, it improves the throughput of the system by 45% while maintaining the desired consistency requirements of the applications when compared to the strong consistency model in Cassandra. Keywords-consistency; replications; data stale; Cassandra; cloud; self-adaptive I.
Keyphrases
stale read self-adaptive consistency cloud storage good performance stale data popular paradigm consistency level tolerable fraction novel approach high probability key feature read operation extensive evaluation cassandra cloud storage application requirement strong consis-tency business success story data stale key idea minimal latency strong consistency model intelligent estimation model major challenge consistency model cloud storage service desired consistency amazon ec2 static eventual consistency desired consistency requirement storage service tolerated number data consistency recent write high data availability traditional approach
