- Email: [email protected]
Introduction to the special issue on Cloud Computing: Recent Developments and Challenging Issues
Computers and Electrical Engineering 42 (2015) 31–32
Contents lists available at ScienceDirect
Computers and Electrical Engineering journal homepage: www.elsevier.com/locate/compeleceng
Guest editorial
Introduction to the special issue on Cloud Computing: Recent Developments and Challenging Issues Cloud computing is now an established research area. Its foundations lay in areas such as virtualization, service-oriented architectures (SOA), grid computing, or autonomic computing. The consolidation of this new research area encourages new research to focus in challenging issues that particularly affect Cloud computing, such as issues related to the scale in which cloud platforms operate, both in terms of resources (i.e. storage, communication networks, or computational machines) and in terms of services and applications elasticity when there is a need to serve potentially millions of users. The aim of this special issue is to bring together novel research in the area of Cloud computing addressing such issues. From a total of twenty-seven papers submitted to this special issue, six high-quality articles were selected. Each selected paper received input during at least two rounds of review with three substantial reviews per round. The accepted papers in this special issue are devoted to some of the most recent developments and research addressing both theoretical, practical, and application aspects on Cloud computing. The contributions of these papers are briefly described below. Thresholding is a popular image segmentation method that converts a grayscale image into a binary one. To address the problem of detecting low-level features in images, Wu et al. [1] propose a cloud model-based framework for rangeconstrained thresholding with uncertainty. Compared to the traditional state-of-art algorithms on a variety of synthetic and real images, with or without noisy, as well as laser cladding images, the experimental results suggest that the presented method is efficient and effective. Hsieh and Chen [2] propose a Distributed Multi-Agent Scheme (DMAS), which is based on a distributed artificial intelligence technique and a group of knowledge modules that cooperate together through an in-cloud database. Compared with conventional IP Multimedia Subsystem (IMS) handoff management, the simulation results indicate the proposed DMAS scheme achieves shorter handoff delay and better QoS for real-time service applications. In the next paper, Duan et al. [3] present CSTORE, a desktop-oriented distributed public cloud storage system that allows users to efficiently store desktop files. From this study, the authors highlight an independent namespace based on the threelevel mapping hash method, the namespace and file data consistency mechanism and block-level deduplication strategy. They also have shown CSTORE achieves better performance than Riak CS in terms of processing metadata, small files and large files. Since cloud implementations have increased due to the rapid growth and proliferation of cloud computing services around the world, reducing energy consumption is a challenging issue. Esfandiarpoor et al. [4] propose and evaluate a number of Virtual Machines (VMs) consolidation algorithms for cloud datacenter energy reduction. They have taken into account the cooling and network structure of the datacenter hosting the physical machines when consolidating the VMs so that fewer racks and routers are employed without SLA violations. Huang and Zeadally [5] propose FACE, a Flexible Architecture for Cluster Evolution to give application developers flexibility in customizing the ways in which data is partitioned, localized, and processed based on specific application requirements. FACE supports system primitives that allow application developers to develop various applications in clouds. Experimental results suggest both FACE’s flexibility and its potential in cluster design architectures. Service composition is an evolving approach that increases the number of applications of cloud computing by reusing existing services. In the last paper of this special issue, Kurdi et al. [6] propose COM2, a COMbinatorial optimization algorithm for cloud service COMposition which aim to reduce communication costs and financial charges. Results suggest the COM2 algorithm retains a low number of combined clouds without compromising the number of examined services. In conclusion, we consider the outcome of this special issue reinforces how important Cloud computing research has become over last few years. At the same time, it seems clear how many questions remain still open and unaddressed. We hope these papers can stimulate further and yet deeper research in this exciting field.
http://dx.doi.org/10.1016/j.compeleceng.2015.03.008 0045-7906/Ó 2015 Published by Elsevier Ltd.
32
Guest editorial / Computers and Electrical Engineering 42 (2015) 31–32
Acknowledgements We would like to express our deep thanks to the Editor-in-Chief, Dr. Manu Malek, for providing us the opportunity to handle this special issue and his editorial staff for their support and help. We also thank all the authors who submitted their papers, as well the thoughtful work of the many reviewers who have provided invaluable evaluations and recommendations in a timely manner. References [1] Tao Wu, Jin Xiao, Kun Qin, Yixiang Chen. Cloud model-based method for range-constrained thresholding. Comput Electr Eng 2015;42:63–78. [2] Han-Chuan Hsieh, Jiann-Liang Chen. Distributed multi-agent scheme support for service continuity in IMS-4G-Cloud networks. Comput Electr Eng 2015;42:79–89. [3] Hancong Duan, Shiyao Yu, Mei Mei, Wenhan Zhan, Lin Li. CSTORE: a desktop-oriented distributed public cloud storage system. Comput Electr Eng 2015;42:90–103. [4] Sina Esfandiarpoor, Ali Pahlavan, Maziar Goudarzi. Structure-aware online virtual machine consolidation for datacenter energy improvement in cloud computing. Comput Electr Eng 2015;42:104–119. [5] Tzu-Chi Huang, Sherali Zeadally. Flexible architecture for cluster evolution in cloud computing. Comput Electr Eng 2015;42:120–136. [6] Heba Kurdi, Abeer Al-Anazi, Carlene Campbell, Auhood Al Faries. A combinatorial optimization algorithm for multiple cloud service composition. Comput Electr Eng 2015;42:137–143.
Guest Editors Danielo G. Gomes Universidade Federal do Ceará, Brazil E-mail address: [email protected] Rodrigo N. Calheiros The University of Melbourne, Australia E-mail address: [email protected] Rafael Tolosana-Calasanz Universidad de Zaragoza, Spain E-mail address: [email protected] Danielo G. Gomes is an assistant professor at the Department of Teleinformatics Engineering of the Universidade Federal do Ceará, Brazil. He received his Ph.D. in Réseaux et Télécoms from the University of Evry, France (2004). His research interests include wireless sensor networks, performance evaluation of ICT systems, mobile cloud, integration Cloud-IoT, green/energy-efficient computing. Danielo is an editorial board member of Computers & Electrical Engineering, Computer Communications and Sustainable Computing.
Dr. Rodrigo N. Calheiros is a Research Fellow in the Department of Computing and Information Systems, The University of Melbourne, Australia. He works in this field of Cloud computing since 2008, when he designed and developed CloudSim, an Open Source tool for simulation of cloud platforms used by academic institutions and companies all around the world. His research interests also include virtualization, grid computing, and simulation and emulation of distributed systems.
Rafael Tolosana-Calasanz is currently Associate Professor at the Computer Science and Systems Engineering Department of the Universidad de Zaragoza, Spain. He holds a PhD from University of Zaragoza and his research interests lie in the intersection of Grid, Cloud and Green computing and Concurrent Systems, Scientific Workflows and Petri nets.
Contents lists available at ScienceDirect
Computers and Electrical Engineering journal homepage: www.elsevier.com/locate/compeleceng
Guest editorial
Introduction to the special issue on Cloud Computing: Recent Developments and Challenging Issues Cloud computing is now an established research area. Its foundations lay in areas such as virtualization, service-oriented architectures (SOA), grid computing, or autonomic computing. The consolidation of this new research area encourages new research to focus in challenging issues that particularly affect Cloud computing, such as issues related to the scale in which cloud platforms operate, both in terms of resources (i.e. storage, communication networks, or computational machines) and in terms of services and applications elasticity when there is a need to serve potentially millions of users. The aim of this special issue is to bring together novel research in the area of Cloud computing addressing such issues. From a total of twenty-seven papers submitted to this special issue, six high-quality articles were selected. Each selected paper received input during at least two rounds of review with three substantial reviews per round. The accepted papers in this special issue are devoted to some of the most recent developments and research addressing both theoretical, practical, and application aspects on Cloud computing. The contributions of these papers are briefly described below. Thresholding is a popular image segmentation method that converts a grayscale image into a binary one. To address the problem of detecting low-level features in images, Wu et al. [1] propose a cloud model-based framework for rangeconstrained thresholding with uncertainty. Compared to the traditional state-of-art algorithms on a variety of synthetic and real images, with or without noisy, as well as laser cladding images, the experimental results suggest that the presented method is efficient and effective. Hsieh and Chen [2] propose a Distributed Multi-Agent Scheme (DMAS), which is based on a distributed artificial intelligence technique and a group of knowledge modules that cooperate together through an in-cloud database. Compared with conventional IP Multimedia Subsystem (IMS) handoff management, the simulation results indicate the proposed DMAS scheme achieves shorter handoff delay and better QoS for real-time service applications. In the next paper, Duan et al. [3] present CSTORE, a desktop-oriented distributed public cloud storage system that allows users to efficiently store desktop files. From this study, the authors highlight an independent namespace based on the threelevel mapping hash method, the namespace and file data consistency mechanism and block-level deduplication strategy. They also have shown CSTORE achieves better performance than Riak CS in terms of processing metadata, small files and large files. Since cloud implementations have increased due to the rapid growth and proliferation of cloud computing services around the world, reducing energy consumption is a challenging issue. Esfandiarpoor et al. [4] propose and evaluate a number of Virtual Machines (VMs) consolidation algorithms for cloud datacenter energy reduction. They have taken into account the cooling and network structure of the datacenter hosting the physical machines when consolidating the VMs so that fewer racks and routers are employed without SLA violations. Huang and Zeadally [5] propose FACE, a Flexible Architecture for Cluster Evolution to give application developers flexibility in customizing the ways in which data is partitioned, localized, and processed based on specific application requirements. FACE supports system primitives that allow application developers to develop various applications in clouds. Experimental results suggest both FACE’s flexibility and its potential in cluster design architectures. Service composition is an evolving approach that increases the number of applications of cloud computing by reusing existing services. In the last paper of this special issue, Kurdi et al. [6] propose COM2, a COMbinatorial optimization algorithm for cloud service COMposition which aim to reduce communication costs and financial charges. Results suggest the COM2 algorithm retains a low number of combined clouds without compromising the number of examined services. In conclusion, we consider the outcome of this special issue reinforces how important Cloud computing research has become over last few years. At the same time, it seems clear how many questions remain still open and unaddressed. We hope these papers can stimulate further and yet deeper research in this exciting field.
http://dx.doi.org/10.1016/j.compeleceng.2015.03.008 0045-7906/Ó 2015 Published by Elsevier Ltd.
32
Guest editorial / Computers and Electrical Engineering 42 (2015) 31–32
Acknowledgements We would like to express our deep thanks to the Editor-in-Chief, Dr. Manu Malek, for providing us the opportunity to handle this special issue and his editorial staff for their support and help. We also thank all the authors who submitted their papers, as well the thoughtful work of the many reviewers who have provided invaluable evaluations and recommendations in a timely manner. References [1] Tao Wu, Jin Xiao, Kun Qin, Yixiang Chen. Cloud model-based method for range-constrained thresholding. Comput Electr Eng 2015;42:63–78. [2] Han-Chuan Hsieh, Jiann-Liang Chen. Distributed multi-agent scheme support for service continuity in IMS-4G-Cloud networks. Comput Electr Eng 2015;42:79–89. [3] Hancong Duan, Shiyao Yu, Mei Mei, Wenhan Zhan, Lin Li. CSTORE: a desktop-oriented distributed public cloud storage system. Comput Electr Eng 2015;42:90–103. [4] Sina Esfandiarpoor, Ali Pahlavan, Maziar Goudarzi. Structure-aware online virtual machine consolidation for datacenter energy improvement in cloud computing. Comput Electr Eng 2015;42:104–119. [5] Tzu-Chi Huang, Sherali Zeadally. Flexible architecture for cluster evolution in cloud computing. Comput Electr Eng 2015;42:120–136. [6] Heba Kurdi, Abeer Al-Anazi, Carlene Campbell, Auhood Al Faries. A combinatorial optimization algorithm for multiple cloud service composition. Comput Electr Eng 2015;42:137–143.
Guest Editors Danielo G. Gomes Universidade Federal do Ceará, Brazil E-mail address: [email protected] Rodrigo N. Calheiros The University of Melbourne, Australia E-mail address: [email protected] Rafael Tolosana-Calasanz Universidad de Zaragoza, Spain E-mail address: [email protected] Danielo G. Gomes is an assistant professor at the Department of Teleinformatics Engineering of the Universidade Federal do Ceará, Brazil. He received his Ph.D. in Réseaux et Télécoms from the University of Evry, France (2004). His research interests include wireless sensor networks, performance evaluation of ICT systems, mobile cloud, integration Cloud-IoT, green/energy-efficient computing. Danielo is an editorial board member of Computers & Electrical Engineering, Computer Communications and Sustainable Computing.
Dr. Rodrigo N. Calheiros is a Research Fellow in the Department of Computing and Information Systems, The University of Melbourne, Australia. He works in this field of Cloud computing since 2008, when he designed and developed CloudSim, an Open Source tool for simulation of cloud platforms used by academic institutions and companies all around the world. His research interests also include virtualization, grid computing, and simulation and emulation of distributed systems.
Rafael Tolosana-Calasanz is currently Associate Professor at the Computer Science and Systems Engineering Department of the Universidad de Zaragoza, Spain. He holds a PhD from University of Zaragoza and his research interests lie in the intersection of Grid, Cloud and Green computing and Concurrent Systems, Scientific Workflows and Petri nets.