Invoking Web Services Based on Energy Consumption Models

2012 ◽  
Author(s):  
Apostolos Papageorgiou ◽  
Ulrich Lampe ◽  
Dieter Schuller ◽  
Ralf Steinmetz ◽  
Athanasios Bamis
Keyword(s):  
Energy Consumption ◽  
Web Services

A Recommendation Model for Energy-Efficient Data Mining Services Based on Data Properties

2021 ◽  
Author(s):  
Zainab Al-Zanbouri
Keyword(s):  
Data Mining ◽  
Energy Consumption ◽  
Web Services ◽  
Recommender System ◽  
Energy Efficient ◽  
Contextual Information ◽  
Three Dimensions ◽  
Context Aware ◽  
Rating Data ◽  
Recommendation Accuracy

Currently, there is a big increase in the usage of data analytics applications and services because of the growth in the data produced from different sources. The QoS properties such as response time and latency of these services are important factors to decide which services to select. As a result of IT expansion, energy consumption has become a big issue. Therefore, establishing a QoS-based web service recommender system that considers energy consumption as one of the essential QoS properties represents a significant step towards selecting the energy efficient web services. This dissertation presents an experimental study on energy consumption levels and latency behavior collected from a set of data mining web services running on different datasets. Our study shows that there is a strong relation between the dataset properties and the QoS properties. Based on the findings from this study, a recommender system is built which considers three dimensions (user, service, dataset). The energy consumption values of candidate services invoked by specific users can be predicted for a given dataset. Afterwards, these services can be ranked according to their predicted energy values and presented to users. We propose three approaches to build our recommender system and we treat it as a context-aware recommendation problem. The dataset is considered as contextual information and we use a context-aware matrix factorization model to predict energy values. In the first approach, we adopt the pre-filtering model where the contextual information serves as a query for filtering relevant rating data. In the second approach, we propose a new method for the pre-filtering implementation. Finally, in the last approach, we adopt the contextual modeling method and we explore different ways of representing dataset information as contextual factors to investigate their impacts on the recommendation accuracy. We compare the proposed approaches with the baseline approaches and the results show the effectiveness of the proposed ones. Also, we compare the performance of the three approaches to discover the best-fit approach when being measured using different metrics. Both prediction and recommendation accuracy of the proposed approaches are significantly better than the baseline models.

Improving Energy Efficiency in Web Services

2013 ◽  
Vol 10 (1) ◽  
pp. 29-52 ◽  
Author(s):  
Jiwei Huang ◽  
Chuang Lin
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Web Services ◽  
Web Service ◽  
Service Selection ◽  
Service Systems ◽  
Markov Decision Problem ◽  
Speed Scaling ◽  
Dynamic Speed Scaling ◽  
The Web

With the rapid increase of the energy consumption associated with IT systems and services, energy efficiency is becoming a critical issue in the design, development and management of web service systems. One of the main mechanisms that can be used to reduce the energy consumption is dynamic speed scaling which scales the frequencies of the processors of web servers at hardware level. Another approach is service selection to facilitate the use of energy through effective distribution and management of the web services. In this paper, both the web service selection and server dynamic speed scaling are optimized by maximizing the quality of service (QoS) revenue and minimizing energy costs. Stochastic models of web service systems are proposed, and techniques for quantitative analysis of the performance and energy consumption are investigated. The authors formulate the service selection and speed scaling as a Markov Decision problem, and introduce related algorithms to solve it. Furthermore, the authors build up an optimization framework using multi-agent techniques, and design efficient algorithms to solve the problem in large-scale web service systems. Finally, the effectiveness of their approach is validated by simulation experiments.

scholarly journals Evaluating the Energy Consumption of Web Services Protocols in Ad Hoc Networks

2014 ◽  
Vol 7 ◽  
pp. 8-13
Author(s):  
Soheyb Ayad ◽  
Okba Kazar ◽  
Nabila Benharkat
Keyword(s):  
Energy Consumption ◽  
Web Services ◽  
Ad Hoc Networks ◽  
Ad Hoc ◽  
Hoc Networks

scholarly journals Reducing Energy Consumption by Using Smart Metering Intelligent Systems

2016 ◽  
Vol 16 (2) ◽  
pp. 113-124
Author(s):  
Ivaylo Atanasov ◽  
Anastas Nikolov ◽  
Evelina Pencheva
Keyword(s):  
Energy Consumption ◽  
Web Services ◽  
Power Control ◽  
Energy Management ◽  
Intelligent Systems ◽  
Smart Metering ◽  
Efficient Energy ◽  
Machine Type ◽  
Machine Type Communications ◽  
Reducing Energy

Abstract Smart metering is aimed at efficient energy management. Its potential may be revealed using recent advances in machine type communications. This paper presents an approach to design web services for residential power control with prepaid functionality. The reduction in energy consumption is estimated for typical households applying heating control.

scholarly journals An Analysis of the Energy Consumption Behavior of Scaled, Containerized Web Apps

2018 ◽  
Vol 10 (8) ◽  
pp. 2710 ◽  
Author(s):  
Sandro Kreten ◽  
Achim Guldner ◽  
Stefan Naumann
Keyword(s):  
Energy Consumption ◽  
Web Services ◽  
Web Service ◽  
Data Centers ◽  
Consumption Behavior ◽  
Save Energy ◽  
Web Developers ◽  
The Web

Containerization is one of the most important topics for modern data centers and web developers. Since the number of containers on one- and multi-node systems is growing, knowledge about the energy consumption behavior of single web-service containers is essential in order to save energy and, of course, money. In this article, we are going to show how the energy consumption behavior of single containerized web services/web apps changes while creating replicas of the service in order to scale and balance the web service.

scholarly journals A Recommendation Model for Energy-Efficient Data Mining Services Based on Data Properties

2021 ◽  
Author(s):  
Zainab Al-Zanbouri
Keyword(s):  
Data Mining ◽  
Energy Consumption ◽  
Web Services ◽  
Recommender System ◽  
Energy Efficient ◽  
Contextual Information ◽  
Three Dimensions ◽  
Context Aware ◽  
Rating Data ◽  
Recommendation Accuracy

Currently, there is a big increase in the usage of data analytics applications and services because of the growth in the data produced from different sources. The QoS properties such as response time and latency of these services are important factors to decide which services to select. As a result of IT expansion, energy consumption has become a big issue. Therefore, establishing a QoS-based web service recommender system that considers energy consumption as one of the essential QoS properties represents a significant step towards selecting the energy efficient web services. This dissertation presents an experimental study on energy consumption levels and latency behavior collected from a set of data mining web services running on different datasets. Our study shows that there is a strong relation between the dataset properties and the QoS properties. Based on the findings from this study, a recommender system is built which considers three dimensions (user, service, dataset). The energy consumption values of candidate services invoked by specific users can be predicted for a given dataset. Afterwards, these services can be ranked according to their predicted energy values and presented to users. We propose three approaches to build our recommender system and we treat it as a context-aware recommendation problem. The dataset is considered as contextual information and we use a context-aware matrix factorization model to predict energy values. In the first approach, we adopt the pre-filtering model where the contextual information serves as a query for filtering relevant rating data. In the second approach, we propose a new method for the pre-filtering implementation. Finally, in the last approach, we adopt the contextual modeling method and we explore different ways of representing dataset information as contextual factors to investigate their impacts on the recommendation accuracy. We compare the proposed approaches with the baseline approaches and the results show the effectiveness of the proposed ones. Also, we compare the performance of the three approaches to discover the best-fit approach when being measured using different metrics. Both prediction and recommendation accuracy of the proposed approaches are significantly better than the baseline models.

scholarly journals Energy Consumption Control of an Air Conditioner Using Web Services

2011 ◽  
Vol 02 (03) ◽  
pp. 255-260 ◽  
Author(s):  
Asaad Elmoudi ◽  
Omar Asad ◽  
Melike Erol-Kantarci ◽  
Hussein Talaat Mouftah
Keyword(s):  
Energy Consumption ◽  
Web Services ◽  
Air Conditioner ◽  
Consumption Control

Lay Perceptions of Energy Consumption

2009 ◽  
Author(s):  
Shahzeen Z. Attari ◽  
Michael L. DeKay ◽  
Cliff I. Davidson ◽  
Wandi Bruine de Bruin
Keyword(s):  
Energy Consumption ◽  
Lay Perceptions
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