Where in the world is my data?

Kadambi, Sudarshan; Chen, Jianjun; Cooper, Brian F.; Lomax, David; Ramakrishnan, Raghu; Silberstein, Adam; Tam, Erwin; García-Molina, Héctor

doi:10.14778/3402707.3402740

Cited by 20 publications

(8 citation statements)

References 13 publications

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“…But version is not an easy parameter to specify, especially in a large-scale sharing scenario where multiple applications access the same data set. Another related work that studies the replication and latency problem controls latency by way of replica locality [26]. Although some NoSQL store like Cassandra [19] provides multiple consistency choices, it actually guarantees strong consistency on the ALL mode and eventual consistency on the other modes through the readrepair mechanism.…”

Section: F Discussionmentioning

confidence: 99%

“…There has been abundant research [26], [27] on asynchronous replication exploring the trade-offs between replication frequency, application RPO (recovery point objective) demands, financial outlay by application owners, and possibly even multi-site replication. We are the first to explore the replica consistency versus latency trade-off for operation latency bounding.…”

Section: F Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Latency Bounding by Trading off Consistency in NoSQL Store: A Staging and Stepwise Approach

Zhu,

Yu,

Wang

2012

Preprint

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Latency is a key service factor for user satisfaction. Consistency is in a trade-off relation with operation latency in the distributed and replicated scenario. Existing NoSQL stores guarantee either strong or weak consistencies but none provides the best consistency based on the response latency. In this paper, we introduce dConssandra, a NoSQL store enabling users to specify latency bounds for data access operations. dConssandra dynamically bounds data access latency by trading off replica consistency. dConssandra is based on Cassandra. In comparison to Cassandra's implementation, dConssandra has a staged replication strategy enabling synchronous or asynchronous replication on demand. The main idea to bound latency by trading off consistency is to decompose the replication process into minute steps and bound latency by executing only a subset of these steps. dConssandra also implements a different in-memory storage architecture to support the above features. Experimental results for dConssandra over an actual cluster demonstrate that (1) the actual response latency is bounded by the given latency constraint; (2) greater write latency bounds lead to a lower latency in reading the latest value; and, (3) greater read latency bounds lead to the return of more recently written values.

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Section: F Discussionmentioning

confidence: 99%

Section: F Discussionmentioning

confidence: 99%

Latency Bounding by Trading off Consistency in NoSQL Store: A Staging and Stepwise Approach

Zhu,

Yu,

Wang

2012

Preprint

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“…An interesting discussion arises about the solutions on how to replicate data across globally distributed data centres. For instance the idea of replicating all data to all locations requires using huge amounts of resources since users from different locations may have different data consumption needs [ 28 ]. In order to satisfy exceptional requirements of data storage in IoT, the distributed storage systems are crucial.…”

Section: Background On Iot Surveysmentioning

confidence: 99%

Internet of Things: A Review of Surveys Based on Context Aware Intelligent Services

Gil

Ferrández

Mora

et al. 2016

Sensors

185

106

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The Internet of Things (IoT) has made it possible for devices around the world to acquire information and store it, in order to be able to use it at a later stage. However, this potential opportunity is often not exploited because of the excessively big interval between the data collection and the capability to process and analyse it. In this paper, we review the current IoT technologies, approaches and models in order to discover what challenges need to be met to make more sense of data. The main goal of this paper is to review the surveys related to IoT in order to provide well integrated and context aware intelligent services for IoT. Moreover, we present a state-of-the-art of IoT from the context aware perspective that allows the integration of IoT and social networks in the emerging Social Internet of Things (SIoT) term.

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“…Metric 3, Number of servers processing a query (query span): Several studies already indicate that minimizing query span also minimizes query latency [2], [3], [4], [5], [6], [7]. In Fig.…”

Section: Motivating Insightsmentioning

confidence: 99%

“…In these systems, the general approach is to use hash-based partitioning and random replication of data. This approach ignores the relations among the data and often leads to redundant replications and significant communication overheads during query processing, which in turn leads to performance degradation [2], [3], [4], [5].…”

Section: Introductionmentioning

confidence: 99%

Temporal Workload-Aware Replicated Partitioning for Social Networks

Türk

Selvitopi

Ferhatosmanoğlu

et al. 2014

IEEE Trans. Knowl. Data Eng.

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Most frequent and expensive queries in social networks involve multi-user operations such as requesting the latest tweets or news-feeds of friends. The performance of such queries are heavily dependent on the data partitioning and replication methodologies adopted by the underlying systems. Existing solutions for data distribution in these systems involve hash-or graph-based approaches that ignore the multi-way relations among data. In this work, we propose a novel data partitioning and selective replication method that utilizes the temporal information in prior workloads to predict future query patterns. Our method utilizes the social network structure and the temporality of the interactions among its users to construct a hypergraph that correctly models multi-user operations. It then performs simultaneous partitioning and replication of this hypergraph to reduce the query span while respecting load balance and I/O load constraints under replication. To test our model, we enhance the Cassandra NoSQL system to support selective replication and we implement a social network application (a Twitter clone) utilizing our enhanced Cassandra. We conduct experiments on a cloud computing environment (Amazon EC2) to test the developed systems. Comparison of the proposed method with hash-and enhanced graph-based schemes indicate that it significantly improves latency and throughput.

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Where in the world is my data?

Cited by 20 publications

References 13 publications

Latency Bounding by Trading off Consistency in NoSQL Store: A Staging and Stepwise Approach

Latency Bounding by Trading off Consistency in NoSQL Store: A Staging and Stepwise Approach

Internet of Things: A Review of Surveys Based on Context Aware Intelligent Services

Temporal Workload-Aware Replicated Partitioning for Social Networks

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