Dynamo

DeCandia, Giuseppe; Hastorun, Deniz; Jampani, Madan; Kakulapati, Gunavardhan; Lakshman, Avinash; Pilchin, Alex; Sivasubramanian, S.; Vosshall, Peter; Vogels, Werner

doi:10.1145/1323293.1294281

Cited by 1,058 publications

(77 citation statements)

References 19 publications

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“…Consul 8 , Zookeeper 9 , Etcd 10 , Amazon Dynamo [DeCandia et al (2007)], Pahoehoe [Anderson et al (2010)]) with strong or eventual consistency guarantees. They are used both to store the "state" of each management functionality and to facilitate the internal consensus algorithm for leader election and assignment of management functionalities to cluster nodes.…”

Section: Self-managing (Micro) Servicesmentioning

confidence: 99%

Self-managing cloud-native applications: Design, implementation, and experience

Toffetti

Brunner

Blöchlinger

et al. 2017

Future Generation Computer Systems

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Running applications in the cloud efficiently requires much more than deploying software in virtual machines. Cloud applications have to be continuously managed : 1) to adjust their resources to the incoming load and 2) to face transient failures replicating and restarting components to provide resiliency on unreliable infrastructure. Continuous management monitors application and infrastructural metrics to provide automated and responsive reactions to failures (health management) and changing environmental conditions (auto-scaling) minimizing human intervention.In the current practice, management functionalities are provided as infrastructural or third party services. In both cases they are external to the application deployment. We claim that this approach has intrinsic limits, namely that separating management functionalities from the application prevents them from naturally scaling with the application and requires additional management code and human intervention. Moreover, using infrastructure provider services for management functionalities results in vendor lock-in effectively preventing cloud applications to adapt and run on the most effective cloud for the job.In this paper we discuss the main characteristics of cloud native applications, propose a novel architecture that enables scalable and resilient selfmanaging applications in the cloud, and relate on our experience in porting a legacy application to the cloud applying cloud-native principles.

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Section: Self-managing (Micro) Servicesmentioning

confidence: 99%

Self-managing cloud-native applications: Design, implementation, and experience

Toffetti

Brunner

Blöchlinger

et al. 2017

Future Generation Computer Systems

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“…Most often they have only two operations: inserting and retrieving data [20]. The examples of such data stores are Dynamo data store by Amazon which uses single integer key [20] or Apache Hbase which identifies the data by a timestamp, column name and row name [21]. The Document data stores use textual data interchange formats, like JSON or xml to store data.…”

Section: The Nosql Data Storesmentioning

confidence: 99%

The Scalable Distributed Two-layer Content Based Image Retrieval Data Store

Deniziak¹,

Michno²,

Krechowicz³

2015

Annals of Computer Science and Information Systems

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Abstract-The multimedia databases are becoming more and more popular nowadays. One of their main problem is a huge data amount storage. Another problem with multimedia databases is querying. Traditional approaches, based on textual keywords are not sufficient. More advanced techniques, incorporating image content features, should be used. In this paper we propose new multimedia database structure with ability of Content Based Image Retrieval which is based on our previous work: Query by Shape method (QS). Query by Shape is a method which is based on decomposing an object into features. Each feature may consists of shape primitive, a color or a texture. In this paper we only use shape primitives. In order to achieve high scalability and workload control, we propose a modified Scalable Distributed Two-layer Data Structure, as a storage. The modification incorporates adding tree structure, comparing algorithm and returning a set of results to the client.

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“…Among distinct noSQL databases, like HBase [38], Dynamo [26], Bigtable [17], Cassandra [41], PNUTS [22], monogDB [19] and cloudDB [5], we adopted HBase since its basic data storage is Apache's Hadoop Distributed File System (HDFS) [13] and it is a column-oriented database [24,50,51], which allows joining data (biological data file) and sets of information (e.g. data provenance).…”

Section: Storage Servicementioning

confidence: 99%