In the context of cloud computing, preserving the privacy of big data while also allowing for secure access control is a critical concern. With the increasing adoption of cloud technology, it is imperative to address the challenges associated with safeguarding sensitive data while enabling authorized access. This paper develops an efficient privacy-preserving security model that uses cryptographic techniques to protect sensitive data and ensure that only authorized individuals can access it. The research puts together a secure data authentication technique, named secured privacy protection access control (SecPPAccess), allowing secured communication in cloud computing. For the protection of privacy for sensitive data, the protected transferring of data is commenced among the elements, like a user, cloud server, registration authority, key generation center and data owner, by using many phases mainly the key generation phase, setup phase, server registration, user registration, data upload, data encryption, requester authentication, data access, and data download phase. Here, a method is designed newly for securing data privacy using various operations, like secret keys, hashing, encryption, etc. The study proves that the initiated SecPPAccess model achieves the highest rate of detection of 0.85, the lowest usage for memory of 0.505 MB, and less computation time of 51.50 s.

Scheduling tasks in multi-core systems is essential for achieving high performance and energy efficiency in data centers and portable devices. Traditional scheduling methods struggle with challenges such as communication overhead, varying core activity levels, and dynamic environments, leading to inefficiencies in processing and energy consumption. To address these issues, this work introduces the Federated Reinforced Energy-Aware Heterogeneous Task Scheduling (FR-EAHTS) method, that integrates transformer-based models with reinforcement learning in a federated environment. The key innovation lies in leveraging TransformX for enhanced state-action representation and Adaptive Proximal Policy Optimization (APPO) for efficient policy optimization, improving both adaptability and scheduling performance in heterogeneous multi-core systems. Real-time load distribution and task granularity adjustments are achieved through the integration of Dynamic Heuristic Load Distribution (DHLD) and Parameter Dynamic Adjustment (PDA), enabling adaptive scheduling decisions. Additionally, Dynamic Voltage and Frequency Scaling (DVFS) is incorporated to optimize power efficiency, ensuring improved energy-aware scheduling in heterogeneous multi-core systems. To further enhance stability and convergence in reinforcement learning, the proposed approach integrates a Dynamic Learning Rate (DLR) mechanism, which adjusts learning rates based on task execution variability. Unlike fixed learning rates, DLR ensures adaptive learning adjustments, preventing unstable updates and improving scheduling efficiency in rapidly changing environments. This approach effectively coordinates policy changes, adapts to core workload heterogeneity, and dynamically adjusts to system characteristics. Experiments demonstrate the proposed work’s efficiency using three key evaluation metrics: makespan, energy consumption, and overhead, achieving a 25

The effectiveness of Network on Chip (NoC) is highly dependent on how well the real-time tasks are mapped on NoC communication infrastructure. In this paper an efficient dynamic scheduling algorithm is proposed and implemented on standard Mesh topology as well as on similar recently proposed LECΔ topology. The performance of proposed algorithm is evaluated in terms of Load Imbalance Factor (LIF), makespan, communication cost and system throughput. The results are evaluated with different task distribution profile using Java-based simulator. The behavior of load distribution is estimated and compared with different performance metrices on two considered topologies. The simulation results show an improvement of 32.57% in LIF at lesser execution time. When comparing the performance of two networks in terms of makespan, the results show a reduction of 43.7% in case of 4 x 4 LECΔ topology as compared to 4 x 4 Mesh topology. The comparative study in terms of LIF, makespan and system throughput is carried out by implementing other existing techniques on both the considered NoC architectures which shows that the proposed approach is an optimized solution for task mapping at considerable reduced cost on considered NoC architectures. The analysis of simulation results shows that the proposed approach may be considered a better choice and implemented on similar NoC architectures as well as other than mesh-based topologies.

Medical records are transmitted between medical institutions using cloud-based Electronic health record (EHR) systems, which are intended to improve various medical services. Due to the potential of data breaches and the resultant loss of patient data, medical organizations find it challenging to employ cloud-based electronic medical record systems. EHR systems frequently necessitate high transmission costs, energy use, and time loss for physicians and patients. Furthermore, EHR security is a critical concern that jeopardizes patient privacy. Compared to a single system, cloud-based EHR solutions may bring extra security concerns as the system architecture gets more intricate. Access control strategies and the development of efficient security mechanisms for cloud-based EHR data are critical. For privacy reasons, the Dynamic constrained message authentication (DCMA) technique is used in the proposed system to encrypt the outsourced medical data by using symmetric key cryptography, which uses the Seagull optimization algorithm (SOA) to choose the best random keys for encryption and then resultant data is hashed using the SHA-256 technique. The results of the proposed model are evaluated using performance metrics, and the model attained a security of about 98.58%, which is proven to be superior because it adopts advanced random secret key generation, which adds more security to the system.

This study examines real-time Network-on-Chip (NoC) dynamic application mapping and scheduling techniques, architectures, and addresses the need for efficient task allocation in increasingly complex multicore systems. This study focused on heuristic-based, machine learning-based, congestion-aware, energy-aware, deadline-aware, hybrid, and latency-aware algorithms, evaluating their effectiveness in optimizing performance, energy efficiency, and communication latency. The methodology included a systematic analysis of recent research papers with the highest number of citations from the last dec-ade. Future work in this area needs to consider the use of AI for better compatibility, developing hybrid approaches while addressing the issues of scalability and fault tolerance in NoC-based multicore systems. This work serves as the base for further improvements in developing application mapping approaches for advanced NoC architectures.

High-performance interconnection networks are currently being used to design Massively Parallel Computers. Selecting the set of nodes on which parallel tasks execute plays a vital role in the performance of such systems. These networks when deployed to run large parallel applications suffer from communication latencies which ultimately affect the system throughput. Mesh and Torus are primary examples of topologies used in such systems. However, these are being replaced with more efficient and complicated hybrid topologies such as ZMesh and x-Folded TM networks. This paper presents a new topology named as Linearly Extensible Cube-Triangle (LEC Delta) which focuses on low latency, lesser average distance and improved throughput. It is symmetrical in nature and exhibits the desirable properties of similar networks with lesser complexity and cost. For N x N network, the LEC Delta topology has lesser network latency than that of Mesh, ZMesh, Torus and x-Folded networks. The proposed LEC Delta network produces reduced average distance, diameter and cost. It has a high value of bisection width and good scalability. The simulation results show that the performance of LEC Delta network is similar to that of Mesh, ZMesh, Torus and x-Folded networks. The results verify the efficiency of the LEC Delta network as evaluated and compared with similar networks.

The use of multi-core based on Network-on-Chip (NoC) systems has increased in recent years due to its effectiveness in executing real-time applications using the latest technologies. Efficient core utilization demands smart mapping of tasks on processing cores. The communication between different tasks should be done through the best available route to minimize network congestion. However, selecting the best suitable path becomes more complex in the case of dynamic scheduling of tasks. This paper presents an improved dynamic mapping technique for mapping the tasks on cores and minimise communication cost by using the various available links simultaneously for quad-core systems. This technique tries to map the communicating tasks in the vicinity to reduce the communication overhead. The proposed algorithm reduces the average latency by approximately 15

The concept of BYOD has been around in the corporate world for a while now which allows employees to bring and use personal devices for organizational work. However, the onset of such a trend also brought major security issues leading to compromise in the organization’s data and resources. Current solutions to such security issues do exist, nevertheless, most of them are bound by constraints and may also face backlash from the workforce community. Blockchain technology has the potential to address the security threats associated with BYOD by providing decentralized authentication, immutable records and employing signature-based algorithms. In this paper, an Ethereum-based blockchain application has been proposed using smart contracts to authenticate employees’ devices and detect unauthorized access. This paper also explains how blockchain can be adopted to address the issue of ReEntrancy attacks where a malicious user can exploit vulnerabilities in smart contracts and steal funds from decentralized applications.

The growth in the number of cloud users who transfer their health data have enhanced the importance of cloud technology's services and capabilities. However, transferring patient health data to the cloud leaves researchers with several concerns and obstacles in privacy, storage, access, key-formation, and management. The paper presents an efficient methodology for storing and accessing health information to and from the cloud. The symmetric key cryptography with the MD5 hash function is employed to enhance the framework's efficiency. The proposed method also provides secure data sharing and removes the burden of an exhaustive re-encryption computation. In the paper, two different keys are computed: one key for each legitimate user among a group and another key for the crypto-system, which is responsible to do all computations over the data. The method provides security against internal threats since only a single share of the key can be accessed. The efficiency of the model is measured by measuring the execution time for key formation, encryption, and decryption processes.

Speed of Internet is getting faster year after year and the need of high-processing infrastructure becomes essential. In order to satisfy the desired computing power, it is obligatory to improve computing resources particularly. A number of multiprocessor architectures have been proposed to speed up the parallel executing tasks. These topologies classified into cube, star, linear and tree based architectures. This paper presents the properties of cube and linear based networks, and a comparative study is carried out to identify the best choice of topology. Analysis is carried out on the grounds of notable characteristics like degree, diameter, average distance, cost, bisection width, message density and number of links. Results are shown in the form of tables and graphs to explore the conclusion. The article concludes the overall performance variations in both the network families.

The article presents the comparative study of topological properties for various Mesh-based networks. The objective of the study is to find best performing Mesh variant in terms of topological parameters and to find a suitable network which can be more feasible to use in data centers. For our analysis, variants of Mesh networks such as Mesh, D-Mesh, X-Mesh, and Z-Mesh are considered. The various topological parameters like Diameter, Average Distance, Message Density, and Cost are evaluated for considered topologies using MATLAB tools. The performance analysis is carried out on the basis of complexity, cost-effectiveness, fault tolerance, and reliability. The effect of waiting time and total execution time has also been considered to analyze the performance of the considered system. A comparative study is carried out, and graphs are depicted to understand their performances in detail. Best results are obtained for different parameters. The present study concludes with the overall performance track for the family of Mesh networks.

Multicore processor implementation for server, workstations and embedded multicore chips is a challenging task. The design decisions with underlying trends affect the performance of such systems. Conventionally, interconnection networks are designed to connect several nodes, each of them having different cores. However, the on-chip interconnection network between cores becomes a bottleneck as it is being shared by all the cores to cooperate while being solving a given problem. This paper presents a scalable Network-on-Chip architecture which exploits the interconnection network to connect different many core processors. The proposed architecture is designed in analogy to linearly extensible multiprocessor networks which have simple architecture and lesser cost. The introduced architecture is symmetrical in nature and exhibits the desirable properties of similar networks with lesser complexity and cost. A basic (4 x 4), 16-core architecture named as CA network is introduced which is symmetrically extendable at higher level with increasing number of cores. The topological variables such as number of cores, number of links, diameter and bisection width are evaluated for CA network and a comparative study is carried out with Mesh and Torus networks in order to validate the ascendancy of the proposed architecture. The proposed CA network has low diameter, low cost and performs better with lesser number of nodes. It is scalable and expandable easily at higher levels with lesser complexity.

Cloud computing becomes an essential tool for internet users which provide various computing resources over the cloud. Providing on-demand storage is being one of the major services which is effective to meet out the expectations of an organization when utilizes the computational resources while accessing large amount of data. Storing the data on cloud is being a big challenge for the researchers in terms of maintaining the protection and security of data. Sharing of the data among a group can lead to insecurity of data from external and internal threats. This paper introduces a protected data sharing framework in the cloud storage that maintains the privacy and confidentiality of data. The proposed method uses the MD5 hash function that is relatively faster than the SHA256 hash function for checking the data integrity. The framework also provides a check on accessing and sharing of data. Exhaustive re-encryption computations are avoided and a single encryption key is used to encrypt the entire file. There are two distinct key shares for each of the users and user is allowed to share one at a time get access data. In this way having access to a single portion of a key permit the framework to safeguard the data against internal threats. Cryptographic server is used to store the other key share and performs all the expensive computations which are considered as a trusted third party. Proposed work also measure the efficiency of the model based on the time taken to execute the various operations.

Adapting parallel scheduling function in the design of multi-scheduling algorithm results significant impact in the operation of high performance parallel systems. The various methods of parallelizing scheduling functions are widely applied in traditional multiprocessor systems. In this paper a novel algorithm is introduced which works not only for parallel execution of jobs but also focuses the parallelization of scheduling function. It gives attention on reducing the execution time, minimizing the load balance performance by selecting the volume of tasks for migration in terms of packets. Jobs are grouped into packets consisting of 2(n) jobs which are scheduled in parallel. Thus, an enhancement in the scheduling mechanism by packet formation is made to carry out high utilization of underlying architecture with increased throughput. The proposed method is assessed on a desktop computer equipped with multi-core processors in cube based multiprocessor systems. The algorithm is implemented with different configuration of multi-core systems. The simulation results indicate that the proposed technique reduces the overall makespan of execution with an improved performance of the system.

The key challenge of scheduling in multi-core systems is to map highly irregular processes that require the inspection of thread behavior and efficiency of multi-core systems. The motive is to schedule multiple tasks on multiple cores. In this paper, a novel scheduling technique is proposed that works on execution technique for tree type tasks structures that are mapped on different multi-core systems designed using different multiprocessor systems. In particular, the performance is evaluated by applying the proposed technique to a particular class of multiprocessor system known as hybrid multiprocessor systems that are used as basic building blocks of a multi-core system. The scheduling algorithm is applied by dividing tasks in terms of computation efficiency of these systems. The key novelty of the proposed method is that tasks which are executed partially may be migrated on systems which are under-loaded and having good efficiency. In other words, the scheduling of tasks to cores must be automated to adapt to the changing program behavior and current load on the system. Before migration of remaining tasks, the efficiency of core on such systems is evaluated. A comparative study is carried out by applying other standard scheduling algorithms on the same multi-core systems. Simulation results show that the proposed algorithm gives better performance while executing tasks on various multi-core systems having different computational efficiency. In particular, the load imbalance is improved by 20–30% and execution time is reduced by 35–55% as compared to traditional algorithms. Further, we show that in many cases, our approach is able to deliver better performance by combining it with classical scheduling algorithms.

Healthcare today is one of the most promising, prevailing, and sensitive sectors where patient information like prescriptions, health records, etc., are kept on the cloud to provide high quality on-demand services for enhancing e-health services by reducing the burden of data storage and maintenance to providing information independent of location and time. The major issue with healthcare organization is to provide protected sharing of healthcare data from the cloud to the decision makers, medical practitioners, data analysts, and insurance firms by maintaining confidentiality and integrity. This article proposes a novel and secure threshold based encryption scheme combined with homomorphic properties (TBHM) for accessing cloud based health information. Homomorphic encryption completely eliminates the possibility of any kind of attack as data cannot be accessed using any type of key. The experimental results report superiority of TBHM scheme over state of art in terms throughput, file encryption/decryption time, key generation time, error rate, latency time, and security overheads.

Cloud computing is emerging as a powerful solution to ever-growing storage and processing requirements of an organization and individual without the burden of owning and handling the physical devices. Security is one of the primary concerns in cloud computing for large-scale implementation. Intrusion detection and prevention (IDP) techniques can be applied to secure against intruders. In this paper, we have studied different IDP techniques comprehensively and analyzed their respective strengths and weaknesses on various parameters to provide security in cloud computing. Hypervisor-based and distributed IDS have shown promising security features in cloud computing environment in comparison with traditional IDP techniques.

Adoption of new technical innovation contributes not only by increasing the business goals, but also facilitates the growth and competitiveness of the organization. Organizations including SME are reluctant to migrate their existing system to cloud platform because of various cloud adoption challenges. Various technical and nontechnical factors are responsible for cloud adoption and migration. Unavailability of a well-defined cloud strategy development model, less prior expertise of the cloud domain and unsurely about how and when to initiate cloud adoption or migration are the key challenges while moving to cloud platform. Most of the organizations are now thinking to migrate to a cloud platform, it is imperative for organizations to critically explore the challenges related to their business. Thus, there is the need of defining challenges associated during cloud adoption and a well-defined cloud strategy model for a successful migration to the cloud domain. This paper aims to investigate the key determinants affecting cloud adoption most in the organizations. Further, the paper identifies existing cloud frameworks and critically evaluates them based on their effectiveness and drawbacks. Factors affecting the cloud adoption process are identified, and a hypothetical framework is proposed based on identified variables. Results suggest that technical factors, organizational factors, and some external factors have a positive impact on cloud adoption.

An essential parameter of information security during data transmission is a secure cryptographic system.In this paper a new cryptographic security technique is proposed to secure data from un-authorized access.The proposed system incorporate cryptology technique of encryption inherits the concept of DNA based encryption using a 128-bit key.Besides this key, round key selection technique, random series of DNA based coding and modified DNA based coding are followed by unique method of substitutions.The proposed technique increases size of the cipher text by 33% as compared to conventional DNA and non DNA based algorithms where size of the cipher text becomes almost double of the original file.This reduction in cipher text improves memory utilization along with data security.The paper is organized in six Sections.Section 1, gives the introduction and also briefly describes related work.In Section 2, the proposed model for solving the problem is described.Various steps involved during encryption and decryption are explained in Section 3, and the results obtained by implementing the proposed algorithm are presented and discussed in Section 4. The Section 5 concludes the work and brief outline of the future work is given in Section 6.

The valuable treating of parallelism on an interconnection network entails optimizing inconsistent performance indices, such as the reduction of communication and scheduling overheads and also uniform distribution of load among the nodes. In this kind of a system a number of nodes process the numerous jobs concurrently. A novel dynamic scheduling scheme that supports task unbiased structure approach is proposed for a particular class of multiprocessor networks known as linearly extensible multiprocessor networks. The significance of proposed scheduling scheme is remedying the communication overhead, delay in task execution and efficient processor utilization, which ultimately improves the total execution time. The proposed algorithm is implemented on a set of processors known as nodes which are linked through certain interconnection network. In particular, the performance is evaluated for linear type of multiprocessor architectures. In addition, a comparison is also made by implementing standard scheduling algorithm on same architectures with same number of nodes. The metrics used for comparison are Load Imbalance Factor (LIF), which represents the deviation of load among processors after achieving load balancing and execution time. The comparative simulation study shows that the proposed scheme gives better performance in terms of task scheduling and execution time when implemented on various linearly extensible multiprocessor networks.