Preserving Resource Handiness and Exigency-Based Migration Algorithm (PRH-EM) for Energy Efficient Federated Cloud Management Systems

Karthikeyan, R.; Sundaravadivazhagan, B.; Cyriac, Robin; Balachandran, Praveen Kumar; Shitharth, S.

doi:10.1155/2023/7754765

Cited by 11 publications

(5 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In particular, blockchain technology offers sophisticated and thoughtful solutions for handling security issues in healthcare systems. Recent studies 24 state that research has shown that the blockchain aids in securely storing sensitive data gathered from numerous sources with improved accountability and traceability. Table 1 lists the various security issues and the suitable blockchain solutions for each.…”

Section: Introductionmentioning

confidence: 99%

A quantum trust and consultative transaction-based blockchain cybersecurity model for healthcare systems

Shitharth

Mouratidis

2023

Sci Rep

View full text Add to dashboard Cite

Many researchers have been interested in healthcare cybersecurity for a long time since it can improve the security of patient and health record data. As a result, a lot of research is done in the field of cybersecurity that focuses on the safe exchange of health data between patients and the medical setting. It still has issues with high computational complexity, increased time consumption, and cost complexity, all of which have an impact on the effectiveness and performance of the complete security system. Hence this work proposes a technique called Consultative Transaction Key Generation and Management (CTKGM) to enable secure data sharing in healthcare systems. It generates a unique key pair based on random values with multiplicative operations and time stamps. The patient data is then safely stored in discrete blocks of hash values using the blockchain methodology. The Quantum Trust Reconciliation Agreement Model (QTRAM), which calculates the trust score based on the feedback data, ensures reliable and secure data transfer. By allowing safe communication between patients and the healthcare system based on feedback analysis and trust value, the proposed framework makes a novel contribution to the field. Additionally, during communication, the Tuna Swarm Optimization (TSO) method is employed to validate nonce verification messages. Nonce message verification is a part of QTRAM that helps verify the users during transmission. The effectiveness of the suggested scheme has been demonstrated by comparing the obtained findings with other current state-of-the-art models after a variety of evaluation metrics have been analyzed to test the performance of this security model.

show abstract

Section: Introductionmentioning

confidence: 99%

A quantum trust and consultative transaction-based blockchain cybersecurity model for healthcare systems

Shitharth

Mouratidis

2023

Sci Rep

View full text Add to dashboard Cite

show abstract

“…A user's access to a specific quantity of CPU, memory, network bandwidth, or disc resources can also be limited using quota mechanisms. [10] IV.…”

Section: Authentication and Authorizationmentioning

confidence: 99%

IaaS Cloud Architecture Distributed Cloud Infrastructures and Virtualized Data centers

2023

IRJMETS

View full text Add to dashboard Cite

In data centers, expensive, rigid mainframe-based systems have replaced flexible distributed architectures built on commodity technology. These architectures may be dynamically divided, molded, and modified to accommodate many business procedures and shifting service requirements. Virtualization is crucial in facilitating data center deployment as it separates the system and network from the fundamental physical components and storage management systems. By leveraging virtualized infrastructures, organizations can consolidate servers and provision resources on-demand, leading to heightened rates of server use and significant expenses as well as energy savings. To ensure optimal data center operations, efficiency, and security, it is crucial to control these virtual networks in a maintainable manner. The cloud network manager is crucial at this point in the data center architecture. Delivering infrastructure as a service, which converts conventional data centers into cloud-like designs, is its primary duty. Data centers of the future will resemble personal Internet as a Service platform in terms of being able to execute virtualized services in a flexible and agile manner. The virtual infrastructure manager coordinates the digital deployment of resources, sometimes referred to as the cloud operating system in this context, which also oversees the monitoring and management network for natural and virtual services. By managing the data center as a cloud, it is also feasible to add distant services from other federated data centers or industrial clouds to the local infrastructure.

show abstract

“…The suggested work (Karthikeyan et al , 2023) begins with classifying data centres according to MIPS and cost before allocating jobs, providing the cloud customer with services and resources that are both scalable and efficient. VMs are given the largest jobs only if their capacity is lower than the total capacity of all data centres.…”

Section: Major Issues In Cloud Load Balancingmentioning

confidence: 99%

“…The EQ (Karthikeyan et al , 2023) algorithm is used to construct VM groupings. Dynamic source and sink models are the inspiration for the EO’s design.…”

Section: Proposed Systemmentioning

confidence: 99%

“…In equation (5), the greater the value of, the more powerful the exploration competence. Karthikeyan et al (2023) says that the greater the value of equation (5), the more powerful the exploration competence and are equivalent to 1 and 2, respectively. The final exponential term by substituting equation (5) for equation (4) is as follows: …”

Section: Proposed Systemmentioning

confidence: 99%

See 1 more Smart Citation

Clustering based EO with MRF technique for effective load balancing in cloud computing

N.,

Lathigara,

Aluvalu

et al. 2023

IJPCC

View full text Add to dashboard Cite

Purpose Cloud computing (CC) refers to the usage of virtualization technology to share computing resources through the internet. Task scheduling (TS) is used to assign computational resources to requests that have a high volume of pending processing. CC relies on load balancing to ensure that resources like servers and virtual machines (VMs) running on real servers share the same amount of load. VMs are an important part of virtualization, where physical servers are transformed into VM and act as physical servers during the process. It is possible that a user’s request or data transmission in a cloud data centre may be the reason for the VM to be under or overloaded with data. Design/methodology/approach VMs are an important part of virtualization, where physical servers are transformed into VM and act as physical servers during the process. It is possible that a user’s request or data transmission in a cloud data centre may be the reason for the VM to be under or overloaded with data. With a large number of VM or jobs, this method has a long makespan and is very difficult. A new idea to cloud loads without decreasing implementation time or resource consumption is therefore encouraged. Equilibrium optimization is used to cluster the VM into underloaded and overloaded VMs initially in this research. Underloading VMs is used to improve load balance and resource utilization in the second stage. The hybrid algorithm of BAT and the artificial bee colony (ABC) helps with TS using a multi-objective-based system. The VM manager performs VM migration decisions to provide load balance among physical machines (PMs). When a PM is overburdened and another PM is underburdened, the decision to migrate VMs is made based on the appropriate conditions. Balanced load and reduced energy usage in PMs are achieved in the former case. Manta ray foraging (MRF) is used to migrate VMs, and its decisions are based on a variety of factors. Findings The proposed approach provides the best possible scheduling for both VMs and PMs. To complete the task, improved whale optimization algorithm for Cloud TS has 42 s of completion time, enhanced multi-verse optimizer has 48 s, hybrid electro search with a genetic algorithm has 50 s, adaptive benefit factor-based symbiotic organisms search has 38 s and, finally, the proposed model has 30 s, which shows better performance of the proposed model. Originality/value User’s request or data transmission in a cloud data centre may cause the VMs to be under or overloaded with data. To identify the load on VM, initially EQ algorithm is used for clustering process. To figure out how well the proposed method works when the system is very busy by implementing hybrid algorithm called BAT–ABC. After the TS process, VM migration is occurred at the final stage, where optimal VM is identified by using MRF algorithm. The experimental analysis is carried out by using various metrics such as execution time, transmission time, makespan for various iterations, resource utilization and load fairness. With its system load, the metric gives load fairness. How load fairness is worked out depends on how long each task takes to do. It has been added that a cloud system may be able to achieve more load fairness if tasks take less time to finish.

show abstract

Preserving Resource Handiness and Exigency-Based Migration Algorithm (PRH-EM) for Energy Efficient Federated Cloud Management Systems

Cited by 11 publications

References 27 publications

A quantum trust and consultative transaction-based blockchain cybersecurity model for healthcare systems

A quantum trust and consultative transaction-based blockchain cybersecurity model for healthcare systems

IaaS Cloud Architecture Distributed Cloud Infrastructures and Virtualized Data centers

Clustering based EO with MRF technique for effective load balancing in cloud computing

Contact Info

Product

Resources

About