Cloud Computing: Virtualization, Service Models, and Deployment Options
Advanced Framework for Multi-Objective Optimization of Computation Offloading in Heterogeneous MEC Environments
Intelligent Adaptive Optimization for Workflow Scheduling in Cloud Environment
A Review Paper on Cloud Computing
Leveraging Mobile Cloud Computing for Real-Time Data Processing: Novel approach
A Comprehensive Review of Security Issues in Cloud Computing
Data Quality Evaluation Framework for Big Data
An Extended Min-Min Scheduling Algorithm in Cloud Computing
Be Mindful of the Move: A Swot Analysis of Cloud Computing Towards the Democratization of Technology
An Architectural Framework for Ant Lion Optimization-based Feature Selection Technique for Cloud Intrusion Detection System using Bayesian Classifier
GridSim Installation and Implementation Process
A Survey on Energy Aware Job Scheduling Algorithms in Cloud Environment
Genetic Algorithm Using MapReduce - A Critical Review
Clustering based Cost Optimized Resource Scheduling Technique in Cloud Computing
Encroachment of Cloud Education for the Present Educational Institutions
Cloud computing has completely transformed how users access and use applications, services, and data. This study provides a thorough analysis of cloud computing, including its history, key features, the role of virtualization in cloud environments, and various cloud service models. From the introduction of time-sharing in the 1960s to its widespread adoption in the 2000s, cloud computing has evolved significantly. The properties of cloud computing, such as resource pooling, on-demand self-service, measured service, resilience, and rapid flexibility, are examined in this research. Virtualization plays a crucial role in cloud computing by enabling efficient resource utilization, scalability, and workload separation. A detailed discussion of several virtualization techniques, including multitenancy, containerization, and hypervisors, is provided. The advantages and drawbacks of each method are also compared in the paper to help readers select the most suitable approach for specific use cases. The functions, deployment models, customization options, scalability, and service examples of cloud service models—Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS)—are described. Additionally, the study explores cloud deployment options, such as community, multi-cloud, hybrid, public, and private models, each with its own unique features. This article offers a comprehensive overview of cloud computing, making it an invaluable resource for both beginners and experts. It enables informed decision-making and the successful deployment of cloud technologies to meet various business needs.
The proliferation of data-intensive mobile applications has necessitated efficient computation offloading techniques to mitigate resource constraints in mobile devices (MDs). Existing approaches fail to address multi-objective optimization challenges effectively. This study proposes an Enhanced Adaptive Cat Hunt Optimization (EACHO) algorithm, designed to optimize energy consumption (EC), delay, and resource utilization in heterogeneous Mobile Edge Computing (MEC) environments. The model leverages Directed Acyclic Graphs (DAGs) for task representation and adaptive parameters for real-time decision-making. Experimental results demonstrate that EACHO achieves significant reductions in delay (0.0172 seconds), EC (0.251 × 10⁻³ J), and cost (0.387) compared to state-of-the-art methods. These findings highlight the robustness and scalability of EACHO for diverse MEC scenarios.
The workflow scheduling in cloud computing systems has gained significance in both commercial and artificial operations. However, generating effective and affordable scheduling methods under deadline constraints remains extremely challenging, particularly for large-scale workflow operations. To address this issue, this study analyzes the workflow scheduling problem in the cloud with the goal of reducing the overall execution cost while keeping the processing time within a set deadline. An Intelligent Adaptive Optimization (IAO) algorithm is developed based on the problem-specific knowledge of workflow scheduling in the cloud. In the proposed IAO, an operator for discrete propagation is created using knowledge of idle time in an hourly cost model to dynamically explore the extensive search space. The adaptive refraction operator is designed to reduce stagnation and increase available resources. The dynamic grouping-based breaking operator is created to leverage the excellent block structure knowledge of the task allocation scheme and speed up convergence. The IAO approach has been shown to outperform several state-of-the-art algorithms in extensive simulation experiments conducted on a well-known scientific workflow.
Cloud computing has evolved significantly since the launch of Amazon's cloud services in 2006. In Hong Kong, its importance is evident in the vast amount of data processing occurring daily across various sectors. While local companies were slow to adopt cloud services, their subscriptions have steadily increased. Cloud computing remains a key topic in computer science studies, particularly in big data. Alibaba’s entry into the cloud market in 2014 and the establishment of its data center in Hong Kong marked a new phase of growth. With a strong background in networking and cloud studies, Hong Kong is well-positioned to contribute to advancements in cloud computing.The establishment of Lenovo's cloud research and development center in 2015 further underscores this potential. Studies in Hong Kong are addressing key challenges with the goal of positioning the region at the forefront of global cloud computing study. Trends focus on AI-driven cloud scalability, edge computing, multi-cloud adoption, serverless computing, and DevOps integration, all contributing to improved operational efficiency. Advancing cloud technology to achieve full maturity remains a priority. This study provides a brief assessment of cloud computing by reviewing over 15 related articles, highlighting its evolution and ongoing developments.
Cell phones have become an essential part of daily life. Studies indicate that most corporate and telecommunication sectors plan to integrate Mobile Cloud Computing (MCC) to meet their business needs efficiently. MCC combines cloud computing with mobile devices, enhancing computational power and storage capacity. This study provides a comprehensive analysis of real-time data processing in MCC environments, exploring challenges, opportunities, and future directions. It discusses computational offloading, network latency, security concerns, and resource management strategies to optimize performance. Since 2009, the term 'mobile cloud computing' (MCC), which blends cloud computing with mobile computing, has gained popularity and become a hot topic in the IT community. As MCC is still in its infancy, identifying future research directions requires a solid understanding of the technology. To achieve this goal, this study provides an overview of MCC's history, fundamental principles, features, ongoing studies, and potential future directions. After reviewing MCC’s evolution from mobile computing to cloud computing, its key features and ongoing research are examined. The infrastructure and characteristics of mobile cloud computing are also analyzed. The remainder of the study explores the challenges associated with mobile cloud computing, provides an overview of various relevant research efforts, and identifies possible avenues for further investigation.
