Security and storage improvement in distributed cloud data centers by increasing reliability based on particle swarm optimization and artificial immune system algorithms

Chamkoori, Alireza; Katebi, Serajdean

doi:10.1002/cpe.7580

Cited by 2 publications

(2 citation statements)

References 32 publications

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“…Performance Efficiency [34,34,36,37,[37][38][39][40][41][42][43][44][45][46] Reliability [47][48][49][50][50][51][52][53][54][55][56][57] Security [58][59][60][61][62][63][64][65][66][67][68][69][70][71][72][73] Operational Policy-Based Functions [74][75][76][77][78][79][80][81][82][83]…”

Section: Quality Attributes Referencesmentioning

confidence: 99%

Efficient Resource Utilization in IoT and Cloud Computing

Prasad,

Dansana,

Bhavsar

et al. 2023

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With the proliferation of IoT devices, there has been exponential growth in data generation, placing substantial demands on both cloud computing (CC) and internet infrastructure. CC, renowned for its scalability and virtual resource provisioning, is of paramount importance in e-commerce applications. However, the dynamic nature of IoT and cloud services introduces unique challenges, notably in the establishment of service-level agreements (SLAs) and the continuous monitoring of compliance. This paper presents a versatile framework for the adaptation of e-commerce applications to IoT and CC environments. It introduces a comprehensive set of metrics designed to support SLAs by enabling periodic resource assessments, ensuring alignment with service-level objectives (SLOs). This policy-driven approach seeks to automate resource management in the era of CC, thereby reducing the dependency on extensive human intervention in e-commerce applications. This paper culminates with a case study that demonstrates the practical utilization of metrics and policies in the management of cloud resources. Furthermore, it provides valuable insights into the resource requisites for deploying e-commerce applications within the realms of the IoT and CC. This holistic approach holds the potential to streamline the monitoring and administration of CC services, ultimately enhancing their efficiency and reliability.

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Section: Quality Attributes Referencesmentioning

confidence: 99%

Efficient Resource Utilization in IoT and Cloud Computing

Prasad,

Dansana,

Bhavsar

et al. 2023

Information

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show abstract

“…Chamkoori and Katebi 32 combined particle swarm optimization and artificial immune system algorithms to improve security, storage, and reliability in distributed cloud data centers. Even though this synergy is just starting, it hints at a significant drop in possible weaknesses.…”

Section: Introductionmentioning

confidence: 99%

Swarm Intelligence with a Chaotic Leader and a Salp algorithm: HDFS optimization for reduced latency and enhanced availability

Jagadish Kumar,

Dhinakaran,

Naresh Kumar

et al. 2024

Concurrency and Computation

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SummaryThe Hadoop distributed file system (HDFS) effectively manages data by segmenting it into blocks distributed across DataNodes in its cluster. While default block sizes in Hadoop 2.x and 1.x are 128 and 64 MB, respectively, they can be customized for larger files. HDFS ensures data reliability by replicating blocks across multiple DataNodes, but this can introduce high latency in cloud storage during heavy network traffic, particularly in big data processing. To address this, we introduce swarm intelligence with a chaotic leader and a salp (SI‐CLS) optimization algorithm. This algorithm reduces network traffic between racks in HDFS by optimizing block distribution. The SI‐CLS algorithm calculates a fitness value for each block, aiming to increase data availability and reduce latency. Performance metrics, including latency, data availability, and load balancing, indicate the effectiveness of SI‐CLS. Adopting this algorithm enhances HDFS performance, ensuring better data availability and lower latency, resulting in improved system reliability.

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Security and storage improvement in distributed cloud data centers by increasing reliability based on particle swarm optimization and artificial immune system algorithms

Cited by 2 publications

References 32 publications

Efficient Resource Utilization in IoT and Cloud Computing

Efficient Resource Utilization in IoT and Cloud Computing

Swarm Intelligence with a Chaotic Leader and a Salp algorithm: HDFS optimization for reduced latency and enhanced availability

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