An adaptive fine-grained performance modeling approach for internetware

2010 ◽  
Author(s):  
Xiang Huang ◽  
Wei Wang ◽  
Wenbo Zhang ◽  
Jun Wei ◽  
Tao Huang
Keyword(s):  
Performance Modeling ◽  
Fine Grained ◽  
Modeling Approach

Automatic Performance Modeling Approach to Performance Profiling of Web Applications

2012 ◽  
Vol 23 (4) ◽  
pp. 786-801 ◽  
Author(s):  
Xiang HUANG ◽  
Wei WANG ◽  
Wen-Bo ZHANG ◽  
Jun WEI ◽  
Tao HUANG
Keyword(s):  
Web Applications ◽  
Performance Modeling ◽  
Performance Profiling ◽  
Modeling Approach

A SDN-based fine-grained measurement and modeling approach to vehicular communication network traffic

2019 ◽  
pp. e4092 ◽  
Author(s):  
Liuwei Huo ◽  
Dingde Jiang ◽  
Xiangnan Zhu ◽  
Yuqing Wang ◽  
Zhihan Lv ◽  
...  
Keyword(s):  
Communication Network ◽  
Network Traffic ◽  
Vehicular Communication ◽  
Fine Grained ◽  
Modeling Approach

An Analytical Performance Modeling Approach for Supply Chain Networks

2012 ◽  
Vol 9 (2) ◽  
pp. 265-275 ◽  
Author(s):  
Sandeep Srivathsan ◽  
Manjunath Kamath
Keyword(s):  
Supply Chain ◽  
Performance Modeling ◽  
Analytical Performance ◽  
Supply Chain Networks ◽  
Modeling Approach

scholarly journals Transfer Learning Based Performance Modeling And Effective Storage Management In Big Data Ecosystems

2021 ◽  
Author(s):  
Fatimah Alsayoud
Keyword(s):  
Big Data ◽  
Transfer Learning ◽  
Performance Modeling ◽  
Storage System ◽  
Cost Effective ◽  
Storage Management ◽  
Management Decisions ◽  
Modeling Approach ◽  
Data Ecosystem ◽  
The Right

Big data ecosystems contain a mix of sophisticated hardware storage components to support heterogeneous workloads. Storage components and the workloads interact and affect each other; therefore, their relationship has to consider when modeling workloads or managing storage. Efficient workload modeling guides optimal storage management decisions, and the right decisions help guarantee the workload’s needs. The first part of this thesis focuses on workload modeling efficiency, and the second part focuses on cost-effective storage management.<div>Workload performance modeling is an essential step in management decisions. The standard modeling approach constructs the model based on a historical dataset collected from one set of setups (scenario). The standard modeling approach requires the model to be reconstructed from scratch with every time the setups changes. To address this issue, we propose a cross-scenario modeling approach that improves the workload’s performance classification accuracy by up to 78% through adopting the Transfer Learning (TL).<br></div><div>The storage system is the most crucial component of the big data ecosystem, where the workload’s execution process starts by fetching data from it and ends by storing data into it. Thus, the workload’s performance is directly affected by storage capability. To provide a high I/O performance in the ecosystems, Solid State Drive (SSD) are utilized as a tier or as a cache on big data distributed ecosystems. SSDs have a short lifespan that is affected by data size and the number of writing operations. Balancing performance requirements and SSD’s lifespan consumption is never easy, and it’s even harder when interacting with a huge amount of data and with heterogeneous I/O patterns. In this thesis, we analysis big data workloads I/O pattern impacts on SSD’s lifespan when SSD is used as a tier or as a cache. Then, we design a Hidden Markov Model (HMM) based I/O pattern controller that manages workload placement and guarantees cost-effective storage that enhances the workload performance by up to 60%, and improves SSD’s lifespan by up to 40%. </div><div>The designed transfer learning modeling approach and the storage management solutions improve workload modeling accuracy, and the quality of the storage management policies while the testing setup changes.<br></div>

A Performance Modeling Approach for Mobile Cloud System

2019 ◽  
Author(s):  
Han Xu ◽  
Liang Luo ◽  
Xiwei Qiu ◽  
Yanping Xiang
Keyword(s):  
Performance Modeling ◽  
Cloud System ◽  
Mobile Cloud ◽  
Modeling Approach ◽  
A Performance

Performance modeling of mobile phone providers: a DEA-ANN combined approach

2014 ◽  
Vol 21 (6) ◽  
pp. 1120-1144 ◽  
Author(s):  
He-Boong Kwon
Keyword(s):  
Neural Networks ◽  
Decision Making ◽  
Performance Measurement ◽  
Mobile Phone ◽  
Performance Modeling ◽  
Back Propagation ◽  
Content Type ◽  
Support Tool ◽  
Performance Benchmarking ◽  
Modeling Approach

Purpose – The purpose of this paper is to investigate the feasibility of using artificial neural networks (ANNs) in conjunction with data envelopment analysis (DEA) for the performance measurement of major mobile phone providers, and for subsequent predictions related to best performance benchmarking and decision making. Design/methodology/approach – DEA and ANN are combined, providing an integrated modeling approach via a two-stage process. DEA is used for front end measurement, while ANN provides learning and prediction capabilities. DEA analysis of industry characteristics is based on the measurement of each decision-making unit's (DMU) performance. Back propagation neural networks (BPNN) can then predict each DMU's efficiency score, based on the results of the DEA models. Additional BPNN models provide best performance predictions. Findings – The DEA module successfully evaluates the competitive status of firms in the mobile phone industry in terms of efficiency. Efficiency trends over the observation period reveal the dynamic nature of competition in this industry. The predictive power of the BPNN module has been demonstrated as well. The proposed system is an effective benchmarking and decision support tool, via its capability to simulate performance scenarios, thereby facilitating insightful, prudent decision making. Originality/value – This paper proposes the use of two different but complementary methods, DEA and ANN, in a combined performance modeling approach, and examines mobile phone providers. This methodology can improve users’ performance benchmarking and decision-making processes. Additionally, adaptive prediction capability is provided through approximating efficient frontiers, in addition to performance measurement.

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