Orchestrating scheduling, grou** and parallelism to enhance the performance of distributed stream computing system

D Sun, H Chen, S Gao, R Buyya - Expert Systems with Applications, 2024‏ - Elsevier
In a big data stream computing environment, the arrival rate of data streams usually
fluctuates over time, posing a great challenge to the elasticity of system. The performance of …

Vdtuner: Automated performance tuning for vector data management systems

T Yang, W Hu, W Peng, Y Li, J Li… - 2024 IEEE 40th …, 2024‏ - ieeexplore.ieee.org
Vector data management systems (VDMSs) have become an indispensable cornerstone in
large-scale information retrieval and machine learning systems like large language models …

Perakende Sektöründe Makine Öğrenmesi Algoritmalarının Karşılaştırmalı Performans Analizi: Black Friday Satış Tahminlemesi

V Sinap - Selçuk Üniversitesi Sosyal Bilimler Meslek …, 2024‏ - dergipark.org.tr
Büyük perakende zincirlerinin şube ağlarının genişlemesi, müşteri tabanlarının büyümesi ve
artan müşteri profili heterojenliği satış tahminleme süreçlerinin karmaşıklığını artırmaktadır …

SimCost: cost-effective resource provision prediction and recommendation for spark workloads

Y Chen, MA Hoque, P Xu, J Lu, S Tarkoma - Distributed and Parallel …, 2024‏ - Springer
Spark is one of the most popular big data analytical platforms. To save time, achieve high
resource utilization, and remain cost-effective for Spark jobs, it is challenging but imperative …

PTSSBench: a performance evaluation platform in support of automated parameter tuning of software systems

R Cao, L Bao, P Zhangsun, C Wu, S Wei, R Sun… - Automated Software …, 2024‏ - Springer
As software systems become increasingly large and complex, automated parameter tuning
of software systems (PTSS) has been the focus of research and many tuning algorithms …

Studying the energy consumption of stream processing engines in the cloud

G Kp, G Pierre, R Rouvoy - 2023 IEEE International …, 2023‏ - ieeexplore.ieee.org
Reducing the energy consumption of the global IT industry requires one to understand and
optimize the large software infrastructures the modern data economy relies on. Among them …

Some new observations on slo-aware edge stream processing

A Shahid, P Kang, P Lama… - 2023 IEEE Cloud Summit, 2023‏ - ieeexplore.ieee.org
The emergence of edge stream processing has created a new way of processing real-time
data from the Internet of Things (IoT), which comprises a plethora of geographically …

Elastic Scaling of Stateful Operators Over Fluctuating Data Streams

M Wu, D Sun, S Gao, K Li… - IEEE Transactions on …, 2024‏ - ieeexplore.ieee.org
Elastic scaling of parallel operators has emerged as a powerful approach to reduce
response time in stream applications with fluctuating inputs. Many state-of-the-art works …

Micro-batch and data frequency for stream processing on multi-cores

AM Garcia, D Griebler, C Schepke… - The Journal of …, 2023‏ - Springer
Latency or throughput is often critical performance metrics in stream processing.
Applications' performance can fluctuate depending on the input stream. This unpredictability …

A Cost-aware Operator Migration Approach for Distributed Stream Processing System

J Tan, Z Tang, W Cai, WJ Tan, X **ao… - … on Cloud Computing, 2025‏ - ieeexplore.ieee.org
Stream processing is integral to edge computing due to its low-latency attributes.
Nevertheless, variability in user group sizes and disparate computing capabilities of edge …