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Publications (9)
The computing continuum can enable new, novel big
data use cases across the edge-cloud-supercomputer spectrum.
Fast and high-volume data movement workflows rely on state--
the-art architectures built on top of stream ingestion and
file transfer open-source tools. Unfortunately, users struggle
when faced with dealing with such diverse architectures:...
Real-time Big Data architectures evolved into specialized layers for handling data streams' ingestion, storage, and processing over the past decade. Layered streaming architectures integrate pull-based read and push-based write RPC mechanisms implemented by stream ingestion/storage systems. In addition, stream processing engines expose source/sink...
Big Data is now the new natural resource. Current state-of-the-art Big Data analytics architectures are built on top of a three layer stack: data streams are first acquired by the ingestion layer (e.g., Kafka) and then they flow through the processing layer (e.g., Flink) which relies on the storage layer (e.g., HDFS) for storing aggregated data or...
In this paper we handle the problem of scheduling tasks in hybrid clouds for small companies which can spend only a fixed budget in order to handle specific situations where the demand is high and cannot be predicted. We describe a model with important characteristics for the resource utilization and we design an algorithm for scheduling tasks whic...
