G
Gabriele Mencagli
Researcher at University of Pisa
Publications - 78
Citations - 850
Gabriele Mencagli is an academic researcher from University of Pisa. The author has contributed to research in topics: Stream processing & Programming paradigm. The author has an hindex of 15, co-authored 74 publications receiving 702 citations. Previous affiliations of Gabriele Mencagli include Pontifícia Universidade Católica do Rio Grande do Sul.
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Journal ArticleDOI
Proactive elasticity and energy awareness in data stream processing
TL;DR: A set of energy-aware proactive strategies, optimized for throughput and latency QoS requirements, which regulate the number of used cores and the CPU frequency through the Dynamic Voltage and Frequency Scaling (DVFS) support offered by modern multicore CPUs are designed.
Proceedings ArticleDOI
Keep calm and react with foresight: strategies for low-latency and energy-efficient elastic data stream processing
TL;DR: The results demonstrate the high-degree of flexibility and configurability of the approach, and show the effectiveness of the elastic scaling strategies compared with existing state-of-the-art techniques used in similar scenarios.
Journal IssueDOI
Next generation grids and wireless communication networks: towards a novel integrated approach
TL;DR: It will be shown here how a wireless heterogeneous network can be exploited for implementing a pervasive and dynamic grid (mobile grid) and, on the other hand, a mobile grid allows the optimization of the communication infrastructure.
Journal ArticleDOI
Parallel Patterns for Window-Based Stateful Operators on Data Streams: An Algorithmic Skeleton Approach
TL;DR: This is the first time that a similar effort to merge the Data Stream Processing domain and the field of Structured Parallelism has been made, and parallel patterns for window-based stateful operators are presented “à la” Algorithmic Skeleton.
Proceedings ArticleDOI
Elastic Scaling for Distributed Latency-Sensitive Data Stream Operators
TL;DR: A control-theoretic strategy to drive the elastic behavior of latency-sensitive streaming operators in distributed environments that allows the operator to meet desired average latency requirements with a significant reduction in the experienced latency jitter.