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
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
The cloud computing environment allows for the sharing of highly scalable hardware and software resources over the internet. Virtual Machines (VM) allow the cloud provider to share hardware resources with cloud clients. Co-resident VMs are Virtual Machines (VMs) that run on the same physical server. The Virtual Machines in Co-Residence are logically separated from one another. The harmful users' side channels compromise the logical isolation. Co-resident attacks are described as unauthorized users accessing sensitive information from Co-resident VMs. Malicious users gain access to critical information such as cryptographic keys, workloads, and web traffic rates. Co-location, co-residence, and coresidency threats are all terms used to describe a Co-resident attack. The Virtual Machine allocation policy is used to determine where the Virtual Machines should be placed on the physical server. The malicious user co-locates their Virtual Machine with the target Virtual Machine. The Virtual Machine deployment procedure takes into account security, workload balancing, and power consumption criteria. Secure metrics are defined to assess the VM allocation policy's security. The Balanced VM Allocation Policy is designed to distribute virtual machines among physical servers. With security metrics, the Previous Selected Server First (PSSF) policy is applied. With the workload balance parameter, the least VM allocation policy, most VM allocation policy, and random allocation policy are applied. Within the same environment, the data centres are connected to the Virtual Machines. With centralised and distributed scheduling algorithms, the attack-resistant Virtual Machine Management framework is built. Side channel attacks are prevented during live VM transfer. Multiple data centre management mechanisms have been added to the system. To allocate virtual machines on the physical server, the Distributed VM Placement (DVMP) policy is created.
The main aim of this paper is to make sure that the message which is sent from the sender to the receiver side is not accessed by any intruder. To ensure that the message is reached safely, we are first using cryptography followed by steganography, that is first the message will be encrypted with the help of the proposed encryption algorithm. Later, the encrypted message is hidden in an image (QR code) and this message is sent to the receiver side for decryption. To the best of our knowledge, the techniques discussed in this paper have not been tried out anywhere, at least in the combination we as a team have implemented. The idea is simple: firstly, the message is encrypted using the algorithm we have developed. The encrypted text is of double the length of the original message, and is jumbled. This helps in fooling the intruder further. The encryption is based on a key generated from a password. Two levels of authentication exist: password, and the key generated based on the password, which will enhance the security of the message. Secondly, we hide the message in the QR code. QR code is generated using Python language. We have generated QR code in three different extensions, they are SVG, EPS and PNG. On the receiver's side, the text is extracted from the QR code and is decrypted. Usage of the QR code is comparatively more reliable, and the limit of the encrypted message hidden in the QR Code has been validated. The whole idea was implemented and tested in Linux environment.
The fact that customers of remote cloud storage services do not have complete physical control of their data makes privacy data search a difficult task. Delegating access to the stored data and completing a search operation to a trusted party is a basic method. However, this does not scale well in practice, as full data access might easily compromise user privacy. We need to ensure the privacy of search contents, i.e., what a user wishes to search and what the server sends back to the user, to securely implement an effective solution. Furthermore, we must ensure that the outsourced data is kept private and that the user is not burdened with an additional local search burden. We present an efficient file modified Key Policy Attribute-Based Encryption technique (KP-ABE) in this paper. A modified Key-policy attribute-based encryption Encryption method is used by data owners to encrypt their confidential data for data recipients. The files could share the ciphertext components linked to characteristics. As a result, both ciphertext storage and encryption time are reduced.
Cloud computing has gained momentum and people are now migrating their data to the cloud as data is growing in size and requires access from a variety of devices. Therefore, Cloud-based data storage has become the standard. However, there are several concerns about data storage in the cloud, starting with virtual machines, which are used to share resources in the cloud, resulting in cloud storage issues. This study identifies cloud data storage concerns such as data storage, data backup, data privacy and integrity, data recoverability, and cloud data recycling. Finally, we provide possible cloud-based solutions to the above-mentioned issues.
In recent years, Big Data applications have grown increasingly significant. Businesses are now aware of the massive amounts of data they collect daily. They also believe that when Big Data is analysed, it can yield more useful information. It is tough to analyse Big Data because of its vast volume and unstructured format. Much work has been done to address the complicated Big Data concerns. As a result, a variety of distribution systems and technologies have emerged. This paper offers a review of recent Big Data technologies that have been developed in recent times. Its goal is to assist users in selecting and implementing the optimum combination of Big Data technologies based on their technology demands and specific application requirements. It not only gives a broad overview of major Big Data technologies, but it also compares them across several system layers, such as the Data Storage Layer, Data Processing Layer, Data Querying Layer, Data Access Layer, and Management Layer. It categorises and examines the essential features, benefits, limitations, and applications of various technologies.