Title
Aten: A Dispatcher for Big Data Applications in Heterogeneous Systems
Abstract
Stream Processing Engines (SPEs) have to support high data ingestion to ensure the quality and efficiency for the end-user or a system administrator. The data flow processed by SPE fluctuates over time, and requires real-time or near real-time resource pool adjustments (network, memory, CPU and other). This scenario leads to the problem known as skewed data production caused by the non-uniform incoming flow at specific points on the environment, resulting in slow down of applications caused by network bottlenecks and inefficient load balance. This work proposes Aten as a solution to overcome unbalanced data flows processed by Big Data Stream applications in heterogeneous systems. Aten manages data aggregation and data streams within message queues, assuming different algorithms as strategies to partition data flow over all the available computational resources. The paper presents preliminary results indicating that is possible to maximize the throughput and also provide low latency levels for SPEs.
Year
DOI
Venue
2018
10.1109/HPCS.2018.00098
2018 International Conference on High Performance Computing & Simulation (HPCS)
Keywords
Field
DocType
Aten,Big Data applications,heterogeneous systems,SPEs,high data ingestion,system administrator,real-time resource pool adjustments,skewed data production,nonuniform incoming flow,inefficient load balance,data aggregation,data streams,partition data flow,stream processing engines,computational resources,unbalanced data flows,Big Data stream applications,message queues
Data modeling,Data stream mining,Computer science,Load balancing (computing),Throughput,Stream processing,Big data,Data aggregator,Data flow diagram,Distributed computing
Conference
ISBN
Citations 
PageRank 
978-1-5386-7880-0
0
0.34
References 
Authors
12
6