Research on Data Routing Strategy of Deduplication in Cloud Environment

He, Qinlu; Zhang, Fan; Bian, Genqing; Zhang, Weiqi; Duan, Dongli; Li, Zhen; Chen, Chen

doi:10.1109/access.2021.3139757

Cited by 9 publications

(4 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Load balancing is a critical aspect of distributed computing systems, ensuring the efficient utilization of resources and optimal performance across heterogeneous environments. Performance Evaluation of Load Balancing Algorithms for Fog Computing in the Internet of Things [36,37,38] presents an evaluation of load balancing algorithms specifically designed for fog computing in IoT scenarios. The paper highlights the importance of efficient resource allocation to enhance the overall performance of IoT applications.…”

Section: Literature Surveymentioning

confidence: 99%

VMMISD: An Efficient Load Balancing Model for Virtual Machine Migrations via Fused Metaheuristics With Iterative Security Measures and Deep Learning Optimizations

Brahmam,

2024

IEEE Access

View full text Add to dashboard Cite

As cloud computing continues to evolve, the need for efficient and secure management of virtual machine (VM) migrations has become increasingly evident. Traditional models often fall short in optimizing load balancing and energy consumption while ensuring a high level of security. In this work, we propose the Load Balancing and Energy-Efficient Migration Model, an innovative approach that leverages bioinspired algorithms and advanced security measures to optimize VM migrations. The initial novelty of our model is the integration of Genetic Algorithms with Ant Colony Optimization for resource scheduling operations. These algorithms were specifically chosen for their proven effectiveness in solving complex optimization problems by simulating natural processes. Additionally, our model incorporates a Deep Reinforcement Learning-based Iterative-learning Contextual Side chaining Model to enhance security measures. This approach not only learns and adapts to new security threats over time but also utilizes contextual side-chaining to link related security events, thereby providing a robust defense mechanism against potential threats. The affinity between VMs and physical machines is quantified using K Means Clustering and Fuzzy Logic, which ensures optimal load balancing while accounting for the uncertainty inherent in the migration process. Furthermore, we employ Bidirectional Long Short-Term Memory networks with Recurrent Graph Neural Network, for accurate workload prediction and informed migration decision Making process. The selection of these techniques is grounded in their proven capability to analyze historical data and predict future trends with high accuracy levels. Our proposed model demonstrates marked improvements in several key performance metrics. We achieved a 4.5% reduction in makespan, a 4.9% increase in deadline hit ratio, and a 3.9% improvement in task diversity. Furthermore, computational complexity was reduced by 8.3%, VM migration efficiency improved by 2.5%, and the delay of computation was significantly reduced by 9.5%. Importantly, the integration of the Iterative-learning Contextual Side chaining Model significantly enhanced the security and quality of service (QoS) under attack scenarios, resulting in a 10.4% improvement in response speed, a 2.5% reduction in energy consumption during block mining, a 3.9% improvement in throughput, and an 8.5% reduction in storage costs. This Load Balancing and Energy-Efficient Migration Model represents a significant advancement in addressing the challenges of load balancing, energy efficiency, and security in VM migrations. Through the meticulous integration of bioinspired algorithms, advanced security measures, and machine learning techniques, our model provides a comprehensive and innovative solution that markedly improves system performance, reduces energy consumption, and fortifies security, thereby paving the way for a more efficient and secure cloud computing ecosystem.

show abstract

Section: Literature Surveymentioning

confidence: 99%

VMMISD: An Efficient Load Balancing Model for Virtual Machine Migrations via Fused Metaheuristics With Iterative Security Measures and Deep Learning Optimizations

Brahmam,

2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…He et al [30] discussed a data routing strategy using Superchunk for improved system throughput, selecting optimal nodes based on Bloom Filter and storage capacity. The strategy improved the deduplication rate, communication query overhead, and load balancing degree.…”

Section: Related Workmentioning

confidence: 99%

Energy-Efficient Architecture for Optimized IoT Data Transmission from Edge to Cloud

Zeba,

Mashira,

Mim

et al. 2024

Preprint

View full text Add to dashboard Cite

Edge Computing and the Internet of Things (IoT) have recently experienced significant growth and transformed how data is processed and analyzed. Edge computing improves efficiency and reduces latency by processing data locally. However, transmitting data efficiently while conserving energy is still a major issue today, especially considering the volume and redundancy of data. The computational capacity and memory of edge gateways in the network's edge layers are limited, making it challenging to process data effectively. As a result, data transmission often becomes inefficient. To address this issue, our research introduces an energy-efficient architecture for edge gateways in the edge layer. This architecture leverages data deduplication and compression techniques for IoT data transmission from edge to cloud. The research's unique deduplication algorithm eliminates duplicate data, while the Lempel Ziv 4 compression algorithm compresses large data sets effectively. This method not only reduces energy consumption but also minimizes memory usage, facilitating quicker and more efficient data transmission. Consequently, this approach significantly alleviates energy consumption challenges and limited data processing capabilities in the edge layer.

show abstract

“…These tools can be deployed on-premises or in the cloud. Currently, developers and data managers are more oriented towards cloud technologies [6], which move the environment and technical background management to the cloud vendor. Thus, there is no need to worry about the hardware, patching, and updating, but mostly the solution's scalability.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Analytical Select Statements Using Reference Aliases

Kvet,

Papan

2024

IEEE Access

View full text Add to dashboard Cite

Data analytics is an inseparable part of the current information systems. Various tools can provide the analysis and produce results in any graphical form, enclosed by the complex filtering. Behind the scenes is a data layer and methods for accessing, manipulating, and processing data. SQL language and databases can serve that. This paper deals with data processing and performance optimization by focusing on function processing and reference. It points to the existing syntax and statement execution steps but provides various enhancements and performance optimization. Existing feature management solutions include result caching, function-based indexes, virtual columns, materialized views, or optimization of the functions to be directly applicable in SQL or PL/SQL limiting context switches. Oracle Database 23c introduced various performance enhancements and a new approach to column and expression aliases. Our proposed solution focuses on identifying and extracting aliases, storing the references in the memory and database layer, optimizing the transfer between them by swapping, as well as checkpointing and function call migrations. It provides a robust layer and complex architecture enclosing the management by the transactions. Each layer is critically discussed by pointing to the performance, structural advantages, and limitations. Complexly, our proposed architecture brings significant performance benefits for complex analytical queries but can also be applied in online transaction processing.

show abstract

Research on Data Routing Strategy of Deduplication in Cloud Environment

Cited by 9 publications

References 38 publications

VMMISD: An Efficient Load Balancing Model for Virtual Machine Migrations via Fused Metaheuristics With Iterative Security Measures and Deep Learning Optimizations

VMMISD: An Efficient Load Balancing Model for Virtual Machine Migrations via Fused Metaheuristics With Iterative Security Measures and Deep Learning Optimizations

Energy-Efficient Architecture for Optimized IoT Data Transmission from Edge to Cloud

Enhancing Analytical Select Statements Using Reference Aliases

Contact Info

Product

Resources

About