منابع مشابه
Low Latency Geo-distributed Data Analytics – Public Review
Large cloud service providers ingest massive amounts of data in geographically distributed sites spread across the globe. Analytics for such planetary-scale datasets is an important emerging challenge. The current practice is to copy all data to a central location, where it can be dealt with locally by standard data analytics stacks such as Hadoop and Spark. However, transferring large volumes ...
متن کاملBohr: Similarity Aware Geo-distributed Data Analytics
We propose Bohr, a similarity aware geo-distributed data analytics system that minimizes query completion time. The key idea is to exploit similarity between data in different data centers (DCs), and transfer similar data from the bottleneck DC to other sites with more WAN bandwidth. Though these sites have more input data to process, these data are more similar and can be more efficiently aggr...
متن کاملCost-Effective Geo-Distributed Storage for Low-Latency Web Services
We present two systems that we have developed to aid geo-distributed web services deployed in the cloud to serve their users with low latency. First, CosTLO [23] helps mitigate the high latency variance observed when accessing data stored in multi-tenant cloud storage services. To cost-effectively satisfy a web service’s tail latency goals, CosTLO uses measurement-driven inferences about replic...
متن کاملWANalytics: Analytics for a Geo-Distributed Data-Intensive World
Large organizations today operate data centers around the globe where massive amounts of data are produced and consumed by local users. Despite their geographically diverse origin, such data must be analyzed/mined as a whole. We call the problem of supporting rich DAGs of computation across geographically distributed data Wide-Area Big-Data (WABD). To the best of our knowledge, WABD is not supp...
متن کاملDynamic Low-Latency Distributed Event Processing of Sensor Data Streams
Event-based systems (EBS) are used to detect meaningful events with low latency in surveillance, sports, finances, etc. However, with rising data and event rates and with correlations among these events, processing can no longer be sequential but it needs to be distributed. However, naively distributing existing approaches not only cause failures as their order-less processing of events cannot ...
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ژورنال
عنوان ژورنال: ACM SIGCOMM Computer Communication Review
سال: 2015
ISSN: 0146-4833
DOI: 10.1145/2829988.2787505