Runtime adaptation of data stream processing systems: The state of the art
Data stream processing (DSP) has emerged over the years as the reference paradigm for
the analysis of continuous and fast information flows, which often have to be processed with …
the analysis of continuous and fast information flows, which often have to be processed with …
A comprehensive survey on parallelization and elasticity in stream processing
Stream Processing (SP) has evolved as the leading paradigm to process and gain value
from the high volume of streaming data produced, eg, in the domain of the Internet of Things …
from the high volume of streaming data produced, eg, in the domain of the Internet of Things …
Resource management and scheduling in distributed stream processing systems: a taxonomy, review, and future directions
Stream processing is an emerging paradigm to handle data streams upon arrival, powering
latency-critical application such as fraud detection, algorithmic trading, and health …
latency-critical application such as fraud detection, algorithmic trading, and health …
More on pipelined dynamic scheduling of big data streams
An important as well as challenging task in modern applications is the management and
processing with very short delays of large data volumes. It is quite often, that the capabilities …
processing with very short delays of large data volumes. It is quite often, that the capabilities …
Self‐adaptation on parallel stream processing: A systematic review
A recurrent challenge in real‐world applications is autonomous management of the
executions at run‐time. In this vein, stream processing is a class of applications that compute …
executions at run‐time. In this vein, stream processing is a class of applications that compute …
Reinforcement learning based policies for elastic stream processing on heterogeneous resources
Data Stream Processing (DSP) has emerged as a key enabler to develop pervasive services
that require to process data in a near real-time fashion. DSP applications keep up with the …
that require to process data in a near real-time fashion. DSP applications keep up with the …
Elastic scaling for distributed latency-sensitive data stream operators
T De Matteis, G Mencagli - 2017 25th Euromicro International …, 2017 - ieeexplore.ieee.org
High-volume data streams are straining the limits of stream processing frameworks which
need advanced parallel processing capabilities to withstand the actual incoming bandwidth …
need advanced parallel processing capabilities to withstand the actual incoming bandwidth …
SP-ant: An ant colony optimization based operator scheduler for high performance distributed stream processing on heterogeneous clusters
A key feature of distributed stream processing (DSP) systems is the scheduling of operators
on clustered computers. In scheduling, the assignment plan of operators to nodes of the …
on clustered computers. In scheduling, the assignment plan of operators to nodes of the …
TCEP: Adapting to dynamic user environments by enabling transitions between operator placement mechanisms
Operator placement has a profound impact on the performance of a distributed complex
event processing system (DCEP). Since the behavior of a placement mechanism strongly …
event processing system (DCEP). Since the behavior of a placement mechanism strongly …
Hierarchical Auto-scaling Policies for Data Stream Processing on Heterogeneous Resources
Data Stream Processing (DSP) applications analyze data flows in near real-time by means
of operators, which process and transform incoming data. Operators handle high data rates …
of operators, which process and transform incoming data. Operators handle high data rates …