scholarly journals Reducing Monitoring Overhead in Virtualized Environments Through Feature Selection

2018 ◽  
Author(s):  
Pedro F. Popiolek ◽  
Karina S. Machado ◽  
Odorico M. Mendizabal
Keyword(s):  
Cloud Computing ◽  
Feature Selection ◽  
Performance Monitoring ◽  
Feature Space ◽  
Cost Effective ◽  
Shared Resources ◽  
Server Consolidation ◽  
Efficient Management ◽  
Management Of Resources ◽  
Physical Resources

Cloud computing has emerged as a cost-effective paradigm for hosting and delivering services. Cloud providers adopt server consolidation strategies to achieve efficient management of resources. A drawback is that applications running on the same host compete for physical resources. Such interference can affect the performance of applications. Performance monitors are useful tools to detect or even predict performance degradation. However, the monitoring itself can be a source of contention. In this paper, we analyze the influence of performance monitoring overhead in virtualized environments. Furthermore, as a mean to reduce contention for shared resources, we propose an approach to reduce the dimensionality of the performance feature space.

scholarly journals Identifying Potential Clinical Syndromes of Hepatocellular Carcinoma Using PSO-Based Hierarchical Feature Selection Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Zhiwei Ji ◽  
Bing Wang
Keyword(s):  
Hepatocellular Carcinoma ◽  
Feature Selection ◽  
Clinical Symptoms ◽  
Malignant Tumors ◽  
Feature Space ◽  
Middle Layer ◽  
Active Role ◽  
Feature Representation ◽  
Causal Relationships ◽  
Positive Score

Hepatocellular carcinoma (HCC) is one of the most common malignant tumors. Clinical symptoms attributable to HCC are usually absent, thus often miss the best therapeutic opportunities. Traditional Chinese Medicine (TCM) plays an active role in diagnosis and treatment of HCC. In this paper, we proposed a particle swarm optimization-based hierarchical feature selection (PSOHFS) model to infer potential syndromes for diagnosis of HCC. Firstly, the hierarchical feature representation is developed by a three-layer tree. The clinical symptoms and positive score of patient are leaf nodes and root in the tree, respectively, while each syndrome feature on the middle layer is extracted from a group of symptoms. Secondly, an improved PSO-based algorithm is applied in a new reduced feature space to search an optimal syndrome subset. Based on the result of feature selection, the causal relationships of symptoms and syndromes are inferred via Bayesian networks. In our experiment, 147 symptoms were aggregated into 27 groups and 27 syndrome features were extracted. The proposed approach discovered 24 syndromes which obviously improved the diagnosis accuracy. Finally, the Bayesian approach was applied to represent the causal relationships both at symptom and syndrome levels. The results show that our computational model can facilitate the clinical diagnosis of HCC.

Solar Photovoltaic Water Pumping to Alleviate Drought in Remote Locations

2006 ◽  
Author(s):  
Kala Meah ◽  
Steven Fletcher ◽  
Yu Wan ◽  
Sadrul Ula
Keyword(s):  
Performance Monitoring ◽  
Cost Effective ◽  
Small Scale ◽  
Solar Water ◽  
Small Water ◽  
Water Pumping ◽  
Western Us ◽  
Water Right ◽  
And Performance ◽  
The University

Many parts of the western US is rural in nature and consequently do not have electrical distribution lines in many parts of farms and ranches. Distribution line extension costs can run from $15,000 to $25,000 per mile, thereby making availability of electricity to small water pumping projects economically unattractive. Solar photo-voltaic (PV) powered water pumping is more cost effective in these small scale applications. Many western states including Wyoming are passing through fifth year of drought with the consequent shortages of water for many applications. Wyoming State Climatologist is predicting a possible 5–10 years of drought. Drought impacts the surface water right away, while it takes much longer to impact the underground aquifers. To mitigate the effect on the livestock and wildlife, Wyoming Governor Dave Freudenthal initiated a solar water pumping initiative in cooperation with the University of Wyoming, County Conservation Districts, Rural Electric Cooperatives, and ranching organizations. Solar water pumping has several advantages over traditional systems; for example, diesel or propane engines require not only expensive fuels, they also create noise and air pollution in many remote pristine areas. Solar systems are environment friendly, low maintenance and have no fuel cost. In this paper the design, installation and performance monitoring of the solar system for small scale remote water pumping will be presented.

scholarly journals Efficient and Cost-effective Service Broker Policy Based on User Priority in VIKOR for Cloud Computing

2021 ◽  
pp. 1-8
Author(s):  
Mohammed Radi ◽  
Ali Alwan ◽  
Abedallah Abualkishik ◽  
Adam Marks ◽  
Yonis Gulzar
Keyword(s):  
Cloud Computing ◽  
Response Time ◽  
Service Providers ◽  
Cost Effective ◽  
Cloud Service ◽  
User Preferences ◽  
Cloud Environment ◽  
Data Centre ◽  
Wide Range ◽  
Service Broker

Cloud computing has become a practical solution for processing big data. Cloud service providers have heterogeneous resources and offer a wide range of services with various processing capabilities. Typically, cloud users set preferences when working on a cloud platform. Some users tend to prefer the cheapest services for the given tasks, whereas other users prefer solutions that ensure the shortest response time or seek solutions that produce services ensuring an acceptable response time at a reasonable cost. The main responsibility of the cloud service broker is identifying the best data centre to be used for processing user requests. Therefore, to maintain a high level of quality of service, it is necessity to develop a service broker policy that is capable of selecting the best data centre, taking into consideration user preferences (e.g. cost, response time). This paper proposes an efficient and cost-effective plan for a service broker policy in a cloud environment based on the concept of VIKOR. The proposed solution relies on a multi-criteria decision-making technique aimed at generating an optimized solution that incorporates user preferences. The simulation results show that the proposed policy outperforms most recent policies designed for the cloud environment in many aspects, including processing time, response time, and processing cost. KEYWORDS Cloud computing, data centre selection, service broker, VIKOR, user priorities

scholarly journals Scheduling under Open stack – The Current State and Future Enhancements

2019 ◽  
Vol 8 (2) ◽  
pp. 375-382
Keyword(s):  
Cloud Computing ◽  
Open Source ◽  
Task Scheduling ◽  
Computing System ◽  
Important Task ◽  
Shared Resources ◽  
Computing Systems ◽  
Current State ◽  
Cloud Computing System ◽  
Research Findings

Cloud computing is being heavily used for implementing different kinds of applications. Many of the client applications are being migrated to cloud for the reasons of cost and elasticity. Cloud computing is generally implemented on distributing computing wherein the Physical servers are heavily distributed considering both hardware and software, the connectivity among which is established through Internet. The cloud computing systems as such have many physical servers which contain many resources. The resources can be made to be shared among many users who are the tenants to the cloud computing system. The resources can be virtualized so as to provide shared resources to the clients. Scheduling is one of the most important task of a cloud computing system which is concerned with task scheduling, resource scheduling and scheduling Virtual Machin Migration. It is important to understand the issue of scheduling within a cloud computing system more in-depth so that any improvements with reference to scheduling can be investigated and implemented. For carrying in depth research, an OPEN source based cloud computing system is needed. OPEN STACK is one such OPEN source based cloud computing system that can be considered for experimenting the research findings that are related to cloud computing system. In this paper an overview on the way the Scheduling aspect per say has been implemented within OPEN STACK cloud computing system

Minimization of Energy Using Heuristic Resource Allocation and Migration for Cloud Computing

2021 ◽  
Vol 12 (1) ◽  
pp. 74-83
Author(s):  
Manjunatha S. ◽  
Suresh L.
Keyword(s):  
Cloud Computing ◽  
Data Center ◽  
Large Scale ◽  
Data Centers ◽  
Service Providers ◽  
Cost Effective ◽  
Cooling Systems ◽  
Operational Costs ◽  
Cloud Computing Service ◽  
And Migration

Data center is a cost-effective infrastructure for storing large volumes of data and hosting large-scale service applications. Cloud computing service providers are rapidly deploying data centers across the world with a huge number of servers and switches. These data centers consume significant amounts of energy, contributing to high operational costs. Thus, optimizing the energy consumption of servers and networks in data centers can reduce operational costs. In a data center, power consumption is mainly due to servers, networking devices, and cooling systems, and an effective energy-saving strategy is to consolidate the computation and communication into a smaller number of servers and network devices and then power off as many unneeded servers and network devices as possible.

Empirical analysis of time series using feature selection algorithms

2021 ◽  
Author(s):  
Mikhail Kanevski
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Feature Selection ◽  
Dimensional Space ◽  
Multivariate Time Series ◽  
Feature Space ◽  
Real Data ◽  
Theoretical Concepts ◽  
Wide Range ◽  
Linear And Nonlinear

<p>Nowadays a wide range of methods and tools to study and forecast time series is available. An important problem in forecasting concerns embedding of time series, i.e. construction of a high dimensional space where forecasting problem is considered as a regression task. There are several basic linear and nonlinear approaches of constructing such space by defining an optimal delay vector using different theoretical concepts. Another way is to consider this space as an input feature space – IFS, and to apply machine learning feature selection (FS) algorithms to optimize IFS according to the problem under study (analysis, modelling or forecasting). Such approach is an empirical one: it is based on data and depends on the FS algorithms applied. In machine learning features are generally classified as relevant, redundant and irrelevant. It gives a reach possibility to perform advanced multivariate time series exploration and development of interpretable predictive models.</p><p>Therefore, in the present research different FS algorithms are used to analyze fundamental properties of time series from empirical point of view. Linear and nonlinear simulated time series are studied in detail to understand the advantages and drawbacks of the proposed approach. Real data case studies deal with air pollution and wind speed times series. Preliminary results are quite promising and more research is in progress.</p>

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