Consistency in the Cloud: When Money Does Matter!

Chihoub, Houssem-Eddine; Ibrahim, Shadi; Antoniu, Gabriel; Pérez, Marı́a S.

doi:10.1109/ccgrid.2013.40

Cited by 23 publications

(13 citation statements)

References 21 publications

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“…Recently few studies have been concentrated on improving the monetary cost of consistency in the cloud [8,16]. The goal of these studies is to minimize the monetary cost of leased resources in the cloud through selecting the most appropriate consistency level that copes with both the dynamic state of the system and the change of service load.…”

Section: B Related Workmentioning

confidence: 99%

“…It is being used by many data-center applications (e.g., Facebook (Instagram) [3] and AppScale [2]). Many research efforts have been dedicated to exploring and improving consistencyperformance, consistency-latency and consistency-monetary cost trade-offs in Cassandra storage system [8,16]. In contrast, little work is focusing on consistency-energy trade-off.…”

Section: Introductionmentioning

confidence: 99%

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Exploring Energy-Consistency Trade-Offs in Cassandra Cloud Storage System

Chihoub

Ibrahim

et al. 2015

2015 27th International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD)

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Apache Cassandra is an open-source cloud storage system that offers multiple types of operation-level consistency including eventual consistency with multiple levels of guarantees and strong consistency. It is being used by many datacenter applications (e.g., Facebook and AppScale). Most existing research efforts have been dedicated to exploring trade-offs such as: consistency vs. performance, consistency vs. latency and consistency vs. monetary cost. In contrast, a little work is focusing on the consistency vs. energy trade-off. As power bills have become a substantial part of the monetary cost for operating a data-center, this paper aims to provide a clearer understanding of the interplay between consistency and energy consumption. Accordingly, a series of experiments have been conducted to explore the implication of different factors on the energy consumption in Cassandra. Our experiments have revealed a noticeable variation in the energy consumption depending on the consistency level. Furthermore, for a given consistency level, the energy consumption of Cassandra varies with the access pattern and the load exhibited by the application. This further analysis indicates that the uneven distribution of the load amongst different nodes also impacts the energy consumption in Cassandra. Finally, we experimentally compare the impact of four storage configuration and data partitioning policies on the energy consumption in Cassandra: interestingly, we achieve 23% energy saving when assigning 50% of the nodes to the hot pool for the applications with moderate ratio of reads and writes, while applying eventual (quorum) consistency. This study points to opportunities for future research on consistencyenergy trade-offs and offers useful insight into designing energyefficient techniques for cloud storage systems.

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Section: B Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Exploring Energy-Consistency Trade-Offs in Cassandra Cloud Storage System

Chihoub

Ibrahim

et al. 2015

2015 27th International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD)

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“…In this work [10], we first investigate the bill details of running storage service in the cloud considering various consistency levels. We provide an in-depth understanding of the monetary cost of cloud services with respect to their adopted consistency models.…”

Section: B Cost-efficient Consistencymentioning

confidence: 99%

Self-Adaptive Cost-Efficient Consistency Management in the Cloud

Chihoub

2013

2013 IEEE International Symposium on Parallel &Amp; Distributed Processing, Workshops and PHD Forum

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Many data-intensive applications and services in the cloud are geo-distributed and rely on geo-replication. Traditional synchronous replication that ensures strong consistency exposes these systems to the bottleneck of wide areas network latencies that affect their performance, availability and the monetary cost of running in the cloud. In this context, several weaker consistency models were introduced to hide such effects. However, these solutions may tolerate far too much stale data to be read. In this PhD research, we focus on the investigation of better and efficient ways to manage consistency. We propose self-adaptive methods that tune consistency levels at runtime in order to achieve better performance, availability and reduce the monetary cost without violating the consistency requirements of the application. Furthermore, we introduce a behavior modeling method that automatically analyzes the application and learns its consistency requirements. The set of experimental evaluations on Grid'5000 and Amazon EC2 cloud platforms show the effectiveness of the proposed approaches.

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“…In this experiment, we adjust I into a suitable value based on this scenario, making it reach an optimal point from both a read access latency perspective and cross datacenter bandwidth consumption. In our experiment, we modeled the arrival of the requests as a Poisson Process, which is a common way [18,19].…”

Section: Methodsmentioning

confidence: 99%

A Two-layer Geo-cloud based Dynamic Replica Creation Strategy

Ye¹,

Li²,

Zhou³

2014

Appl. Math. Inf. Sci.

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Abstract:To improve data availability and reduce user access latency, geo-cloud based data replication is widely used in large global Web sites, such as Facebook. However, as the popularity of data is different and will change as time goes by, simple static replica creation strategies that assign the same number of replicas to all data, never changing thereafter, are not suitable. To this issue, we propose a two-layer geo-cloud based dynamic replica creation strategy called TGstag. TGstag addresses the issue with twofold: policy constraint heuristic inter-datacenter replication and load aware adaptive intra-datacenter replication. TGstag aims to minimize both cross-datacenter bandwidth consumption and average access time with constraints of policy and commodity node capacity. To evaluate the effectiveness of our strategy, we've conducted comprehensive experiments to compare TGstag with other approaches. The results show TGstag significantly reduces cross-datacenter bandwidth and average read access time.

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Consistency in the Cloud: When Money Does Matter!

Cited by 23 publications

References 21 publications

Exploring Energy-Consistency Trade-Offs in Cassandra Cloud Storage System

Exploring Energy-Consistency Trade-Offs in Cassandra Cloud Storage System

Self-Adaptive Cost-Efficient Consistency Management in the Cloud

A Two-layer Geo-cloud based Dynamic Replica Creation Strategy

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