Harigeeta: Cic Mechanism with Euclidean Steiner Tree for Service Latency Prediction in Delay-Sensitive Cloud Services

• Sharma, Rahul Kumar ^[2] ; Singh, Sarvpal ^[1]
  1. [1] University of Technology

     University of Technology

     Rusia

     
  2. [2] Madan Mohan Malviya University of Technology, Gorakhpur, India, 37003. Research scholar, Information technology and Computer Application Department, MMMUT, Gorakhpur, India, 3700
• Localización: ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, ISSN-e 2255-2863, Vol. 13, Nº. 1, 2024
• Idioma: inglés
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• Resumen

  • Data establishment and resource provision are the most crucial tasks in the data center. To achieve minimum service latency, it is required to have a balance between the virtual machine and physical machine for proper execution of any query into the cloud data center. Cloud services have a huge market in the world trade. These services have a large impact on every field, also on research. Latency is a major problem in the growth of the cloud market in a real time scenario. Online trade, marketing and banking have a large market of cloud services, which require minimum latency in the real-time response otherwise the whole market would be destroyed. Latency prediction plays a crucial role in managing the load on the data center. To perfectly maintain a request waiting queue, it is required to predict accurate latency between the virtual machines in the data center. If any approach can predict accurate latency in the data center for any particular request, then it can perfectly manage the waiting queue for the cloud data center. Thus, prediction plays a crucial role in reducing latency in the execution of any request to the cloud data center. This article presents an online latency prediction approach for VMs to improve load balancing. A Euclidean Circle Steiner Tree point is proposed. Results show compression with existing mechanisms and get 8-12 % more accuracy in latency prediction.