International Journal of Electrical and Computer Engineering (IJECE) Vol. 12, No. 5, October 2022, pp. 5630~5643 ISSN: 2088-8708, DOI: 10.11591/ijece.v12i5.pp5630-5643  5630 Journal homepage: http://ijece.iaescore.com A combined computing framework for load balancing in multi-tenant cloud eco-system Amith Shekhar Chandrashekhar 1 , Sharvani Gunakimath Suryakanth 2 1 Department of Information Science and Engineering, GM Institute of Technology, Davangere, India 2 Department of Computer Science, RV College of Engineering, Bengaluru, India Article Info ABSTRACT Article history: Received Jul 23, 2021 Revised May 27, 2022 Accepted Jun 25, 2022 Since the world is getting digitalized, cloud computing has become a core part of it. Massive data on a daily basis is processed, stored, and transferred over the internet. Cloud computing has become quite popular because of its superlative quality and enhanced capability to improvise data management, offering better computing resources and data to its user bases (UBs). However, there are many issues in the existing cloud traffic management approaches and how to manage data during service execution. The study introduces two distinct research models: data center virtualization framework under multi-tenant cloud-ecosystem (DCVF-MT) and collaborative workflow of multi-tenant load balancing (CW-MTLB) with analytical research modeling. The sequence of execution flow considers a set of algorithms for both models that address the core problem of load balancing and resource allocation in the cloud computing (CC) ecosystem. The research outcome illustrates that DCVF-MT, outperforms the one-to- one approach by approximately 24.778% performance improvement in traffic scheduling. It also yields a 40.33% performance improvement in managing cloudlet handling time. Moreover, it attains an overall 8.5133% performance improvement in resource cost optimization, which is significant to ensure the adaptability of the frameworks into futuristic cloud applications where adequate virtualization and resource mapping will be required. Keywords: Cloud computing Load balancing Multi-tenancy Task scheduling Virtualization This is an open access article under the CC BY-SA license. Corresponding Author: Amith Shekhar Chandrashekhar Department of Information Science and Engineering, GM Institute of Technology Davangere, Karnataka, India Email: amithsc@gmit.ac.in 1. INTRODUCTION The cloud computing (CC) paradigm has been evolving for the last two decades to improve data and information systems' quality and service modeling. The evolution witnessed the transition from desktop computing technologies to client-server architecture where virtualization plays a vital role in resource provisioning and sharing. With the invention of cloud computing models, the researchers explored the limited features associated with the web-enabled computing models in practice due to their limited capability to handling a large corpus of data streams with the uncertainty involved [1], [2]. The researchers also realized that CC technologies could be a good substitution for traditional web-based computing systems. The CC technologies offer better solution space to deal with a large corpus of data processing and offer a basis to provide a different level of agreements upon software frameworks as a service, virtualized platform-as-a- service, and Infrastructural resources as a service [3]–[6]. The computing models deliver various resources over the Internet with ease of processing and task execution through these different service layers. The service delivery models are also associated with various profit-making policies which leverage the economic