Single window stream aggregation using reconfigurable hardware

Geethakumari, Prajith Ramakrishnan; Gulisano, Vincenzo; Svensson, Bo Joel; Trancoso, Pedro; Sourdis, Ioannis

doi:10.1109/fpt.2017.8280128

Cited by 12 publications

(4 citation statements)

References 17 publications

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“…The events which the successor operators have already acknowledged are removed from the output buffer B O . Although the state model is applicable to many modern CEP systems, such as Apache Flink 7 , which assumes the presence of buffers, with a few adaptations in the internal structures, it can be applied to other CEP system, e.g., those that do not assume buffers [41].…”

Section: Moving Fine-grained State (Mfgs) Sequential Transitionmentioning

confidence: 99%

TCEP: Transitions in operator placement to adapt to dynamic network environments

Luthra

Koldehofe

Danger

et al. 2021

Journal of Computer and System Sciences

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Distributed Complex Event Processing (DCEP) is a commonly used paradigm to detect and act on situational changes of many applications, including the Internet of Things (IoT). DCEP achieves this using a simple specification of analytical tasks on data streams called operators and their distributed execution on a set of infrastructure. The adaptivity of DCEP to the dynamics of IoT applications is essential and very challenging in the face of changing demands concerning Quality of Service. In our previous work, we addressed this issue by enabling transitions, which allow for the adaptive use of operator placement mechanisms. In this article, we extend the transition methodology by optimizing the costs of transition and analyzing the behavior using multiple operator placement mechanisms. Furthermore, we provide an extensive evaluation on the costs of transition imposed by operator migrations and learning, as it can inflict overhead on the performance if operated uncoordinatedly.

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Section: Moving Fine-grained State (Mfgs) Sequential Transitionmentioning

confidence: 99%

TCEP: Transitions in operator placement to adapt to dynamic network environments

Luthra

Koldehofe

Danger

et al. 2021

Journal of Computer and System Sciences

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“…Challenge 2: Hardware-, data-and system-awareness. Large distributed systems such as smart grids or vehicular networks are composed of heterogeneous devices and sensors [5,6,14,15,17,21], ranging from the small embedded ones found in smart meters to GPU-based platforms for AI-based self-driving cars [9]. Such heterogeneity must be taken into account for streaming applications to scale while sharing resources with the existing ecosystem of applications running in each device.…”

Section: Talk Overviewmentioning

confidence: 99%

Motivations and Challenges for Stream Processing in Edge Computing

Gulisano

2021

Companion of the ACM/SPEC International Conference on Performance Engineering

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The 2030 Agenda for Sustainable Development of the United Nations General Assembly defines 17 development goals to be met for a sustainable future. Goals such as Industry, Innovation and Infrastructure and Sustainable Cities and Communities depend on digital systems. As a matter of fact, billions of Euros are invested into digital transformation within the European Union, and many researchers are actively working to push state-of-the-art boundaries for techniques/tools able to extract value and insights from the large amounts of raw data sensed in digital systems.Edge computing aims at supporting such data-to-value transformation. In digital systems that traditionally rely on central data gathering, edge computing proposes to push the analysis towards the devices and data sources, thus leveraging the large cumulative computational power found in modern distributed systems. Some of the ideas promoted in edge computing are not new, though. Continuous and distributed data analysis paradigms such as stream processing have argued about the need for smart distributed analysis for basically 20 years. Starting from this observation, this talk covers a set of standing challenges for smart, distributed, and continuous stream processing in edge computing, with real-world examples and use-cases from smart grids and vehicular networks.

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Section: Transition Enginementioning

confidence: 99%