VMS-MCSA: virtual machine scheduling using modified clonal selection algorithm

Ajmera, Kashav; Tewari, Tribhuwan Kumar

doi:10.1007/s10586-021-03320-5

Cited by 22 publications

(3 citation statements)

References 45 publications

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“…The population is then supplemented with mutant antibodies, and the best antibodies are once more chosen in the next step. This cycle continues until the last generation is formed, and no antibodies need to mutate [43][44][45].…”

Section: Clonal Selection Approachmentioning

confidence: 99%

Cost-effective clonal selection and AIS-based load balancing in cloud computing environment

Mosayebi¹,

Azmi²

2023

Preprint

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One of the challenges in cloud computing is increasing efficiency, which calls for the application of task scheduling and load balancing. Numerous load-balancing methods that have been suggested for the cloud environment can increase efficiency. The Throttled policy is one of them that aids in evenly distributing user requests among virtual machines. In the first part of this article, we examined the cost of the Clonal selection algorithm with Throttled load balancing Policy and the Clonal selection algorithm with Distributed Service Broker Policy and contrasted them to the clonal selection and other immune systems mechanisms. Additionally, we suggest two additional mechanisms based on the artificial immune system. In the two proposed mechanisms, algorithms are introduced so that by benefiting from load balancing policies when receiving requests from users, in the shortest possible time and the fastest processing of data and with the lowest cost, they respond to users. In the second part, we introduce two other new algorithms that were created as a result of combining an evolutionary algorithm with the artificial immune system mechanisms. Algorithms have been evaluated and compared with other algorithms using the Cloud Analyst simulator in terms of average response time, average data center processing time, total data transfer cost, and total cost. The results attained show how much better the suggested algorithms are.

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Section: Clonal Selection Approachmentioning

confidence: 99%

Cost-effective clonal selection and AIS-based load balancing in cloud computing environment

Mosayebi¹,

Azmi²

2023

Preprint

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“…When the Hybrid Genetic Algorithm and Modified Particle Swarm Optimization algorithm were implemented in this work and resulted in better efficiency, compared to VMS-MCSA [22], GM-DPSO [23], PSACO [24], and standard algorithms like LR-MMT, IQR-MC. Table 3 shows the overall comparative analysis of the proposed work with other algorithms using all the performance metrics.…”

Section: Iqr-mcmentioning

confidence: 99%

Hybrid Genetic Algorithm and Modified-Particle Swarm Optimization Algorithm (GA-MPSO) for Predicting Scheduling Virtual Machines in Educational Cloud Platforms

Supreeth

Patil

2022

Int. J. Emerg. Technol. Learn.

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Cloud computing is expanding gradually as the number of educational applications is rapidly increasing. To get Educational cloud services, internet connectivity is predominantly important and Cloud Environment uses one of the basic technology to manage the Physical servers effectively ie; Virtualization Technology. In Cloud Computing, the data centers host numerous Virtual Machines (VMs) on top of the Servers. Due to the rapid growth of Educational platforms, the workload of the VM is computationally getting increased. In the Cloud Educational platforms, to execute the jobs IT resources are provisioned over the network. Since the data generated from the client-side is dynamic in nature, it is difficult to allocate the computational resources efficiently. So to enhance the energy efficiency and to provide the resources in an optimized way, a VM Scheduling mechanism with Hybrid Genetic Algorithm-Modified Particle Swarm Optimization (GA-MPSO) is proposed in this work to achieve QoS parameters like reduced Energy consumption, SLA violation, and cost reduction over the heterogeneous environments. The Hybrid G-MPSO develops the optimal range and improves the best range of scheduling the Virtual resources to VMs from Physical Machines (PMs). The proposed approach, when compared to other VM scheduling algorithms, it intensifies the energy consumption to 105KWH, SLA violation rate of 0.08%, reduces the migrations count to 2122, and consumes the overall cost of 2567.68$. The different scheduling methods for VMs are evaluated against the results, which show that the Hybrid GA-MPSO method is far better than the existing algorithms.

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“…However, more research must be done to determine if it can be used in a wide range of situations. Ajmera and Tewari 29 looked at the problem of scheduling virtual machines and came up with VMS‐MCSA, which changes the clonal selection algorithm to make it more efficient. Even though the integration looks good, it must be tested with many types of work.…”

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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VMS-MCSA: virtual machine scheduling using modified clonal selection algorithm

Cited by 22 publications

References 45 publications

Cost-effective clonal selection and AIS-based load balancing in cloud computing environment

Cost-effective clonal selection and AIS-based load balancing in cloud computing environment

Hybrid Genetic Algorithm and Modified-Particle Swarm Optimization Algorithm (GA-MPSO) for Predicting Scheduling Virtual Machines in Educational Cloud Platforms

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

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