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Orchestrating scheduling, grou** and parallelism to enhance the performance of distributed stream computing system
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 …
fluctuates over time, posing a great challenge to the elasticity of system. The performance of …
Vdtuner: Automated performance tuning for vector data management systems
Vector data management systems (VDMSs) have become an indispensable cornerstone in
large-scale information retrieval and machine learning systems like large language models …
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 …
artan müşteri profili heterojenliği satış tahminleme süreçlerinin karmaşıklığını artırmaktadır …
SimCost: cost-effective resource provision prediction and recommendation for spark workloads
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 …
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
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 …
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
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 …
optimize the large software infrastructures the modern data economy relies on. Among them …
Some new observations on slo-aware edge stream processing
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 …
data from the Internet of Things (IoT), which comprises a plethora of geographically …
Elastic Scaling of Stateful Operators Over Fluctuating Data Streams
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 …
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 …
Applications' performance can fluctuate depending on the input stream. This unpredictability …
A Cost-aware Operator Migration Approach for Distributed Stream Processing System
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 …
Nevertheless, variability in user group sizes and disparate computing capabilities of edge …