Atta-ur-Rahman¹, Kiran Sultan³, Iftikhar Naseer⁴, Rizwan Majeed⁵, Dhiaa Musleh¹, Mohammed Abdul Salam Gollapalli², Sghaier Chabani², Nehad Ibrahim¹, Shahan Yamin Siddiqui^6,7, Muhammad Adnan Khan^8,*

CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 21-34, 2021, DOI:10.32604/cmc.2021.013453

Abstract COVID-19 turned out to be an infectious and life-threatening viral disease, and its swift and overwhelming spread has become one of the greatest challenges for the world. As yet, no satisfactory vaccine or medication has been developed that could guarantee its mitigation, though several efforts and trials are underway. Countries around the globe are striving to overcome the COVID-19 spread and while they are finding out ways for early detection and timely treatment. In this regard, healthcare experts, researchers and scientists have delved into the investigation of existing as well as new technologies. The situation demands development of a clinical… More >

Jaber Almutairi¹, Mohammad Aldossary^2,*,*

CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 4143-4160, 2021, DOI:10.32604/cmc.2021.018145

Abstract Recently, the number of Internet of Things (IoT) devices connected to the Internet has increased dramatically as well as the data produced by these devices. This would require offloading IoT tasks to release heavy computation and storage to the resource-rich nodes such as Edge Computing and Cloud Computing. However, different service architecture and offloading strategies have a different impact on the service time performance of IoT applications. Therefore, this paper presents an Edge-Cloud system architecture that supports scheduling offloading tasks of IoT applications in order to minimize the enormous amount of transmitting data in the network. Also, it introduces the… More >

Mohammad Aldossary^*

CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3531-3562, 2021, DOI:10.32604/cmc.2021.017477

Abstract With the striking rise in penetration of Cloud Computing, energy consumption is considered as one of the key cost factors that need to be managed within cloud providers’ infrastructures. Subsequently, recent approaches and strategies based on reactive and proactive methods have been developed for managing cloud computing resources, where the energy consumption and the operational costs are minimized. However, to make better cost decisions in these strategies, the performance and energy awareness should be supported at both Physical Machine (PM) and Virtual Machine (VM) levels. Therefore, in this paper, a novel hybrid approach is proposed, which jointly considered the prediction… More >

Shiqi Wang¹, Mingfang Jiang^2,*, Jiaohua Qin¹, Hengfu Yang², Zhichen Gao³

CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 2979-2993, 2021, DOI:10.32604/cmc.2021.017094

Abstract In the era of big data, outsourcing massive data to a remote cloud server is a promising approach. Outsourcing storage and computation services can reduce storage costs and computational burdens. However, public cloud storage brings about new privacy and security concerns since the cloud servers can be shared by multiple users. Privacy-preserving feature extraction techniques are an effective solution to this issue. Because the Rotation Invariant Local Binary Pattern (RILBP) has been widely used in various image processing fields, we propose a new privacy-preserving outsourcing computation of RILBP over encrypted images in this paper (called PPRILBP). To protect image content,… More >

Nandhini Jembu Mohanram^1,2,*, Gnanasekaran Thangavel³, N. M. Jothi Swaroopan⁴

Computer Systems Science and Engineering, Vol.38, No.2, pp. 215-228, 2021, DOI:10.32604/csse.2021.015027

Abstract Fault tolerance (FT) schemes are intended to work on a minimized and static amount of physical resources. When a host failure occurs, the conventional FT frequently proceeds with the execution on the accessible working hosts. This methodology saves the execution state and applications to complete without disruption. However, the dynamicity of open cloud assets is not seen when taking scheduling choices. Existing optimization techniques are intended in dealing with resource scheduling. This method will be utilized for distributing the approaching tasks to the VMs. However, the dynamic scheduling for this procedure doesn’t accomplish the objective of adaptation of internal failure.… More >

Samira Kanwal¹, Zeshan Iqbal¹, Aun Irtaza¹, Rashid Ali², Kamran Siddique^3,*

CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 2469-2486, 2021, DOI:10.32604/cmc.2021.014709

Abstract In IoT networks, nodes communicate with each other for computational services, data processing, and resource sharing. Most of the time huge data is generated at the network edge due to extensive communication between IoT devices. So, this tidal data is transferred to the cloud data center (CDC) for efficient processing and effective data storage. In CDC, leader nodes are responsible for higher performance, reliability, deadlock handling, reduced latency, and to provide cost-effective computational services to the users. However, the optimal leader selection is a computationally hard problem as several factors like memory, CPU MIPS, and bandwidth, etc., are needed to… More >

Ming Li¹, Zhong Fang², Wanwan Cao¹, Yong Ma^1,*, Shang Wu¹, Yang Guo¹, Yu Xue³, Romany F. Mansour⁴

Computer Systems Science and Engineering, Vol.38, No.1, pp. 39-46, 2021, DOI:10.32604/csse.2021.016189

Abstract With the rapid development and popularization of new-generation technologies such as cloud computing, big data, and artificial intelligence, the construction of smart grids has become more diversified. Accurate quick reading and classification of the electricity consumption of residential users can provide a more in-depth perception of the actual power consumption of residents, which is essential to ensure the normal operation of the power system, energy management and planning. Based on the distributed architecture of cloud computing, this paper designs an improved random forest residential electricity classification method. It uses the unique out-of-bag error of random forest and combines the Drosophila… More >

Xian Ma¹, Tingyan Lv^2,*, Yingqiang Jin², Rongmin Chen², Dengxian Dong², Yingtao Jia²

Energy Engineering, Vol.118, No.3, pp. 691-705, 2021, DOI:10.32604/EE.2021.012701

Abstract The large number of gas turbines in large power companies is difficult to manage. A large amount of the data from the generating units is not mined and utilized for fault analysis. This study focuses on F-class (9F.05) gas turbine generators and uses unsupervised learning and cloud computing technologies to analyse the faults for the gas turbines. Remote monitoring of the operational status are conducted. The study proposes a cloud computing service architecture for large gas turbine objects, which uses unsupervised learning models to monitor the operational state of the gas turbine. Faults such as chamber seal failure, load abnormality… More >

Ehab Nabiel Al-Khanak^1,*, Sai Peck Lee², Saif Ur Rehman Khan³, Navid Behboodian⁴, Osamah Ibrahim Khalaf⁵, Alexander Verbraeck⁶, Hans van Lint¹

CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 3265-3282, 2021, DOI:10.32604/cmc.2021.015409

Abstract Scientific Workflow Applications (SWFAs) can deliver collaborative tools useful to researchers in executing large and complex scientific processes. Particularly, Scientific Workflow Scheduling (SWFS) accelerates the computational procedures between the available computational resources and the dependent workflow jobs based on the researchers’ requirements. However, cost optimization is one of the SWFS challenges in handling massive and complicated tasks and requires determining an approximate (near-optimal) solution within polynomial computational time. Motivated by this, current work proposes a novel SWFS cost optimization model effective in solving this challenge. The proposed model contains three main stages: (i) scientific workflow application, (ii) targeted computational environment,… More >

Nadia Tabassum¹, Allah Ditta², Tahir Alyas³, Sagheer Abbas⁴, Hani Alquhayz⁵, Natash Ali Mian⁶, Muhammad Adnan Khan^7,*

CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 3129-3141, 2021, DOI:10.32604/cmc.2021.014729

Abstract Cloud computing is becoming popular technology due to its functional properties and variety of customer-oriented services over the Internet. The design of reliable and high-quality cloud applications requires a strong Quality of Service QoS parameter metric. In a hyperconverged cloud ecosystem environment, building high-reliability cloud applications is a challenging job. The selection of cloud services is based on the QoS parameters that play essential roles in optimizing and improving cloud rankings. The emergence of cloud computing is significantly reshaping the digital ecosystem, and the numerous services offered by cloud service providers are playing a vital role in this transformation. Hyperconverged… More >