IEEE Transactions on Sustainable Computing
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From the January-March 2017 issue
Customer-Satisfaction-Aware Optimal Multiserver Configuration for Profit Maximization in Cloud Computing
By Jing Mei, Kenli Li, and Keqin Li
Along with the development of cloud computing, an increasing number of enterprises start to adopt cloud service, which promotes the emergence of many cloud service providers. For cloud service providers, how to configure their cloud service platforms to obtain the maximum profit becomes increasingly the focus that they pay attention to. In this paper, we take customer satisfaction into consideration to address this problem. Customer satisfaction affects the profit of cloud service providers in two ways. On one hand, the cloud configuration affects the quality of service which is an important factor affecting customer satisfaction. On the other hand, the customer satisfaction affects the request arrival rate of a cloud service provider. However, few existing works take customer satisfaction into consideration in solving profit maximization problem, or the existing works considering customer satisfaction do not give a proper formalized definition for it. Hence, we first refer to the definition of customer satisfaction in economics and develop a formula for measuring customer satisfaction in cloud computing. And then, an analysis is given in detail on how the customer satisfaction affects the profit. Lastly, taking into consideration customer satisfaction, service-level agreement, renting price, energy consumption, and so forth, a profit maximization problem is formulated and solved to get the optimal configuration such that the profit is maximized.
Editorials and Announcements
- Congratulations to Albert Zomaya on his appointment as 2016-2018 Editor-in-Chief of IEEE Transactions on Sustainable Computing. Dr. Zomaya is currently the Chair Professor of High Performance Computing & Networking and Director of the Centre for Distributed and High Performance Computing in the School of Information Technologies, The University of Sydney.
Call for Papers
Special Issue on Cryptography and Data Security in Sustainable Computing
Submission deadline: August 1, 2017. View PDF.
With the proliferation of several kinds of attacks towards ICT infrastructures and the relative effects caused by a successful compromise of them, data security is of pivotal importance in our current society. As a practical example, health-related data are rapidly being digitalised passing from paper-based communications among patients and physicians to computer-based ones. However, the occurrence of data leakage is increasing, with the consequence of stealing sensitive personal information from the leaked health-related data. To protect the ICT infrastructures from these attacks, several solutions have been proposed, where cryptography plays a key role. Despite being able to provide a suitable degree of security and privacy, such solutions have not been designed by taking care of their energy consumption and resource usage. Therefore, they are not optimal in the case of resource-constrained systems, such as sensor networks, and are under radical rethinking in order to be effectively adopted in such context. Moreover, the recent increasing attention to climate changes and environmental issues are leading a considerable debate on how changing the current computing technologies so as to have less severe effects on the global warming and resource usage. Such a debate involves also the current cryptosystems and the other widely-accepted solutions to provide data security, so as to modify them by considering their sustainability.
The aim of the special issue is to solicit novel contributions to the current debate of realizing sustainable solutions to support data security and to realize cryptosystems to protect the data at rest and in motion within the current ICT infrastructures, by also seeking practical experiences in using these solutions in concrete use cases of Green Computing and Resource-Constrained Systems.
Special Issue on Smart Data and Deep Learning in Sustainable Computing
Submission deadline: September 1, 2017. View PDF.
We are living in a data-driven era in which numerous infrastructure can be connected and the interconnected systems can perform “smart” when the large pool of the data are well utilized. Finding the way of well utilizing the large volume of data has an urgent demand in multiple realms, including academics, industries, and education. The force behind the data can be pushed out from a variety of data-driven techniques, such as machine learning and deep learning, which is a great potential for generating successful model, framework, and method for achieving sustainable computing. Therefore, gathering recent achievements in smart data and deep learning in sustainable computing is meaningful and valuable for powering the capability of data-driven domain and the various applications, implementations, and innovations in different disciplines and fields.
This special issue focuses on two aspects considering the perspective of sustainable computing, which include smart data and deep learning. The smart data covers all dimensions of data usage lifecycles, such as data selections and collections, data preprocessing, data mining, and data analytics, in various application scenarios. The other aspect, deep learning, emphasizes the intelligent performance of applying data-driven techniques in practices and research explorations. Thus, this special issue aims at collecting updated outstanding papers that illustrate the latest achievements and development updates concerning the smart data and deep learning solutions, issues, applications, trends, and implementations in sustainable computing.
Special Issue on Sustainable Cyber Forensics and Threat Intelligence
Submission deadline: September 1, 2017. View PDF.
Increasing societal reliance on interconnected digital systems, including smart grids and Internet of Things (IoT), made sustainable detection and investigation of threat actors among highest priorities of any society. Scale and attack surface of modern networks mandate optimized deployment of limited cyber forensics and threat intelligence resources to detect and remove malicious actors in a timely manner. However, timely dealing with such a huge number of attacks is not possible without employment of artificial intelligence and machine learning techniques. When a significant amount of data is collected from or generated by different security monitoring solutions; intelligent big-data analytical techniques are necessary to mine, interpret and extract knowledge out of those data. The emerging field of cyber threat intelligence is investigating applications of artificial intelligence and machine learning techniques to perceive, reason, learn and act intelligently against advanced cyber attacks.
General Call for Papers
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