An OptiAssign-PSO Based Optimisation for Multi-Objective Multi-Level Multi-Task Scheduling in Cloud Computing Environment
Advanced Analytics for Disease Tracking & Remote Intravenous Injection Monitoring
DevKraft - Fueling Collaboration in Coding Challenges
Comparative Security and Compliance Analysis of Serverless Computing Platforms: AWS Lambda, Azure Functions, and Google Cloud Functions
Blockchain Healthcare Management using Patients
A Comprehensive Review of Security Issues in Cloud Computing
An Extended Min-Min Scheduling Algorithm in Cloud Computing
Data Quality Evaluation Framework for Big Data
An Architectural Framework for Ant Lion Optimization-based Feature Selection Technique for Cloud Intrusion Detection System using Bayesian Classifier
Be Mindful of the Move: A Swot Analysis of Cloud Computing Towards the Democratization of Technology
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
The collection of interconnected computers that constitutes more than one united computing resource is known as the Cloud. In recent years, the advancement of cloud computing has facilitated the rapid arrangement of interconnected data centers that are geographically dispersed, offering high-quality and dependable services. Scalable traffic management has recently been developed in cloud data centers for traffic load balancing and quality of service provisioning. However, reducing latency during multidimensional resource allocation still remains a challenge. Hence, there is a need for efficient resource scheduling to ensure load optimization in the cloud. The objective of this work is to introduce an integrated resource scheduling and load balancing algorithm for efficient cloud service provisioning. The goal of the proposed system is to select the required load balancing algorithm to enhance resource utilization. Simulations were conducted to evaluate effectiveness using the Cloudsim simulator in cloud data centers, and the results show that the proposed method achieves better performance in terms of the average success rate, resource scheduling efficiency, and response time. The dynamic nature of cloud environments requires constant adaptation in resource allocation strategies. This necessitates the development of algorithms capable of handling diverse workloads efficiently. Additionally, the increasing complexity of applications and services hosted on the cloud demands a comprehensive approach that considers not only load balancing but also the intricacies of resource utilization. Furthermore, the proposed algorithm focuses on predictive analytics to anticipate fluctuations in demand and adjust resource allocation preemptively. By incorporating machine learning techniques, the system can adapt to changing patterns, ensuring optimal performance even in unpredictable scenarios. This holistic approach addresses the evolving challenges in cloud computing, providing a robust foundation for reliable and efficient service provisioning.
Energy consumption in cloud computing plays a vital role in operating costs for both the service provider and the cloud user. The cloud is scalable and can provide access as per demand. Due to this, resource access requests are increasing and submitted to the server. To manage all requests, scheduling is the solution to assign requests with the quality of service. To avoid high operating costs, resource scheduling needs to be energy-aware. In this paper, energy-aware resource scheduling in the cloud is proposed. The total resource utilization of each resource has been calculated, and energy is optimized through the antlion optimization algorithm to avoid high power consumption. The resource is identified with its best utilization value and assigned to submitted workloads on a priority basis. The experimental results of the proposed work are analyzed with existing autonomic frameworks, and it is found that the proposed work performs exceptionally well. In addition to addressing the increasing resource access requests, the proposed energy-aware resource scheduling in cloud computing also focuses on optimizing overall system performance. The antlion optimization algorithm is employed to efficiently manage energy consumption, ensuring that power usage is minimized without compromising the quality of service. The algorithm calculates the total resource utilization for each component in the cloud infrastructure, allowing for a thorough understanding of system dynamics. The paper optimizes cloud computing by prioritizing efficient and capacity-based resource assignment, improving responsiveness, user experience, and reducing costs. The approach also promotes environmental sustainability. Experimental results validate its effectiveness in energy efficiency and system responsiveness, contributing to advanced resource management in cloud computing for sustainable and cost-effective services.
Generally, enterprises store data in internal storage and establish firewalls to protect against intruders attempting to access the data. In cloud computing, data is stored in storage provided by service providers. Information privacy protection involves storing data in encrypted form. The cloud system is responsible for both storage and the encryption or decryption of data, allowing system administrators to access encrypted data and decryption keys. This capability poses a risk to information privacy as it enables unauthorized access to information. This survey discusses Data Storage in Cloud Computing, focusing on the concept of storing encryption and decryption services alongside the storage service. Additionally, the survey examines storage system architecture, security challenges, services, and user application systems to provide a comparison of various cloud models. In addition to the fundamental aspects of data storage and encryption, this survey delves into the evolving landscape of cloud computing, addressing the dynamic nature of storage system architectures. Enterprises increasingly rely on cloud service providers for scalable and flexible storage solutions, prompting a critical examination of the security challenges associated with this paradigm shift. The survey examines how storage services in different cloud models affect user applications and privacy. It stresses the importance of understanding cloud providers' services amidst evolving technology and growing threats. The study delves into trends like edge computing and distributed storage, evaluating their impact on cloud storage security. Overall, it aims to provide a comprehensive overview for enterprises and researchers to make informed decisions on data security in Cloud Computing.
The computing capability of mobile systems is enhanced by cloud computing. Mobile devices can rely on cloud computing to perform computationally intensive operations such as data mining and searching. Mobile cloud computing refers to the availability of cloud computing services in a mobile environment. It is a combination of mobile networks and cloud computing, providing optimal services for mobile users. The use of mobile cloud computing overcomes performance-related obstacles, such as bandwidth, storage capacity, and battery life, as well as environment-related issues like availability, scalability, and heterogeneity. Security threats have become obstacles in the rapid adaptability of the mobile cloud computing paradigm, and there is a significant risk associated with migrating storage and data processing from mobiles to clouds. User privacy and the integrity of data and applications are key concerns that most cloud providers prioritize. This paper reviews the concept of mobile cloud computing and the security issues inherent within the context of mobile applications and cloud computing. It analyzes and compares various possible approaches proposed by researchers to address security and privacy issues in MCC. Despite widespread adoption, challenges like interoperability and standardization persist. Ongoing efforts focus on establishing common protocols, while research explores integrating technologies like edge computing and AI to enhance mobile cloud capabilities. This paper examines the dynamic landscape of MCC, addressing security concerns and exploring advancements and challenges, contributing to ongoing discussions on optimization, security, and the future of mobile cloud computing. As the field continues to evolve, researchers are actively working on developing comprehensive frameworks that not only address current security challenges but also anticipate and mitigate emerging threats in the ever-changing landscape of mobile cloud computing.
Cloud computing is revolutionizing Customer Internet computing and the IT industry by integrating with mobile environments through remote access technologies. This growth involves using smartphones and sensors as data collection nodes interacting with the cloud. However, widespread adoption faces a hurdle in data security concerns, as users are hesitant about entrusting sensitive data to public clouds operated by external providers. To tackle barriers in cloud computing, we urgently require innovative and secure management architectures. This paper introduces a comprehensive framework addressing user concerns, focusing on secure data sharing in the cloud. The framework covers aspects like transport, aggregation, usage, and destruction of sensitive data, emphasizing the semi-trusted nature of the cloud to boost user confidence and address security concerns. One key aspect of the proposed framework is the integration of the Kerberos protocol over the network. This protocol plays a pivotal role in establishing secure and authenticated communication channels, ensuring the confidentiality and integrity of the data being transferred within the cloud infrastructure. Additionally, the paper introduces a user process protection method based on a virtual machine monitor. This method contributes to enhancing overall system security by providing a layer of isolation and protection for user processes operating in the cloud environment. The combined integration of the Kerberos protocol and the virtual machine monitor-based user process protection method forms a cohesive approach towards realizing robust system functionalities. By offering a secure foundation for data management in the cloud, this framework addresses the current limitations and instills a sense of trust among users, fostering a more widespread and confident adoption of cloud computing techniques.
