Robust data placement in urgent computing environments

Cope, Jason M.; Trebon, Nick; Tufo, Henry M.; Beckman, Pete

doi:10.1109/ipdps.2009.5160914

Cited by 27 publications

(18 citation statements)

References 21 publications

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“…Data replication can influence consistency, while it also enhances the scalability and higher availability of data. In general, the existing data placement strategies can be grouped based on user-imposed constraints, such as data access latency [75], fault tolerance [76], energy-cost awareness [77], data dependency [78,79] and robustness or reliability [80,81].…”

Section: Data Placement Techniquesmentioning

confidence: 99%

A survey on data storage and placement methodologies for Cloud-Big Data ecosystem

et al. 2019

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Over the time, the type of applications has evolved from batch, compute or memory intensive applications to streaming or even interactive applications. As a result, applications are getting more complex and become long-running. Such applications might require frequent-access to multiple distributed data sources. During application deployment and provisioning, the user can face various issues such as (i) where to effectively place both the data and the computation; (ii) how to achieve required objectives while reducing the overall application running cost. Data could be generated from various sources, including a multitude of devices over IoT environments that can generate a huge amount of data, while the applications are running. An application can further produce a large amount of data. In general, data of such size is usually referred to as Big Data. In general, Big Data is characterised by five properties [1, 2]. These are volume, velocity (means rapid update and propagation of data), variety

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Section: Data Placement Techniquesmentioning

confidence: 99%

A survey on data storage and placement methodologies for Cloud-Big Data ecosystem

et al. 2019

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“…in the LEAD project [5], the Urgent Data Management Framework (UDMF) [11] and SPRUCE, which proposes robust data placement mechanisms [16]. However, these solutions take advantage of existing data, whereas real-time monitoring calls for delivery of up-to-date measurements collected on the fly by a distributed sensor infrastructureespecially wireless sensors [10], which have become a common solution in recent years.…”

Section: Related Workmentioning

confidence: 99%

Dedicated IT infrastructure for Smart Levee Monitoring and Flood Decision Support

et al. 2016

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Abstract. Smart levees are being increasingly investigated as a flood protection technology. However, in large-scale emergency situations, a flood decision support system may need to collect and process data from hundreds of kilometers of smart levees; such a scenario requires a resilient and scalable IT infrastructure, capable of providing urgent computing services in order to perform frequent data analyses required in decision making, and deliver their results in a timely fashion. We present the ISMOP IT infrastructure for smart levee monitoring, designed to support decision making in large-scale emergency situations. Most existing approaches to urgent computing services in decision support systems dealing with natural disasters focus on delivering quality of service for individual, isolated subsystems of the IT infrastructure (such as computing, storage, or data transmission). We propose a holistic approach to dynamic system management during both urgent (emergency) and normal (non-emergency) operation. In this approach, we introduce a Holistic Computing Controller which calculates and deploys a globally optimal configuration for the entire IT infrastructure, based on cost-of-operation and quality-of-service (QoS) requirements of individual IT subsystems, expressed in the form of Service Level Agreements (SLAs). Our approach leads to improved configuration settings and, consequently, better fulfilment of the system's cost and QoS requirements than would have otherwise been possible had the configuration of all subsystems been managed in isolation.

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“…Xie [15] proposed an energy-aware strategy for data placement in RAID systems. Cope [16] proposed a replication strategy to guarantee data robustness for urgent computing. However, none of them is proposed for higher data reliability or skew balancing ability in cluster systems.…”

Section: Other Related Workmentioning

confidence: 99%

A fragmented data-declustering strategy for high skew tolerance and efficient failure recovery

Luo

Dang

Yokota

2014

Proceedings of the 18th International Database Engineering &Amp; Applications Symposium on - IDEAS '14

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Data declustering is a common technique to improve data I/O performance by retrieving data in parallel from multiple storage nodes. Data-declustering methods with replicated data also increase system availability, reliability and skew tolerance. Current replicated declustering schemes fall into two types. In the first, each storage node has its data fully replicated on one other storage node. In the second, each storage node has its replica data fragmented and distributed over several storage nodes. The different schemes have different trade-offs between skew tolerance and data reliability. In this paper, we introduce Fragmented Chained Declustering (FCD) to provide better load balancing and efficient failed-data restoration, with the same or lower data loss rates than previous solutions. Experimental results show the load-balancing and failure recovery efficiency of FCD.

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Robust data placement in urgent computing environments

Cited by 27 publications

References 21 publications

A survey on data storage and placement methodologies for Cloud-Big Data ecosystem

A survey on data storage and placement methodologies for Cloud-Big Data ecosystem

Dedicated IT infrastructure for Smart Levee Monitoring and Flood Decision Support

A fragmented data-declustering strategy for high skew tolerance and efficient failure recovery

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