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 …
A survey on the evolution of stream processing systems
Stream processing has been an active research field for more than 20 years, but it is now
witnessing its prime time due to recent successful efforts by the research community and …
witnessing its prime time due to recent successful efforts by the research community and …
Decentralized self-adaptation for elastic data stream processing
Abstract Data Stream Processing (DSP) applications are widely used to develop new
pervasive services, which require to seamlessly process huge amounts of data in a near real …
pervasive services, which require to seamlessly process huge amounts of data in a near real …
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 …
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 …
Model-based stream processing auto-scaling in geo-distributed environments
Data stream processing is an attractive paradigm for analyzing IoT data at the edge of the
Internet before transmitting processed results to a cloud. However, the relative scarcity of fog …
Internet before transmitting processed results to a cloud. However, the relative scarcity of fog …
Optimal operator deployment and replication for elastic distributed data stream processing
Processing data in a timely manner, data stream processing (DSP) applications are
receiving an increasing interest for building new pervasive services. Due to the …
receiving an increasing interest for building new pervasive services. Due to the …
Zero-shot cost models for distributed stream processing
R Heinrich, M Luthra, H Kornmayer… - Proceedings of the 16th …, 2022 - dl.acm.org
This paper proposes a learned cost estimation model for Distributed Stream Processing
Systems (DSPS) with an aim to provide accurate cost predictions of executing queries. A …
Systems (DSPS) with an aim to provide accurate cost predictions of executing queries. A …
Meces: Latency-efficient rescaling via prioritized state migration for stateful distributed stream processing systems
R Gu, H Yin, W Zhong, C Yuan, Y Huang - 2022 USENIX Annual …, 2022 - usenix.org
Stateful distributed stream processing engines (SPEs) usually call for dynamic rescaling due
to varying workloads. However, existing state migration approaches suffer from latency …
to varying workloads. However, existing state migration approaches suffer from latency …