Performance Evaluation Model for Multi-Tenant Microsoft 365 Deployments Under High Concurrency

• Author(s): Olushola Damilare Odejobi; Kabir Sholagberu Ahmed
• Paper ID: 1711329
• Page: 92-107
• Published Date: 31-05-2018
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 1 Issue 11 May-2018

Abstract

The increasing adoption of Microsoft 365 in enterprise environments has accelerated the demand for performance evaluation models that can address the complexities of multi-tenant deployments under high concurrency. As organizations migrate mission-critical workflows to cloud-based productivity suites, ensuring scalability, reliability, and optimal resource allocation becomes paramount. Multi-tenancy introduces unique challenges, including shared infrastructure utilization, fluctuating workloads, and the need to maintain consistent service quality across diverse tenants with varying usage patterns. High concurrency further compounds these issues by stressing authentication services, message queues, data synchronization, and real-time collaboration features. This proposes a performance evaluation model specifically designed to assess and optimize Microsoft 365 deployments operating at large-scale concurrency. The model integrates metrics such as transaction latency, throughput, session distribution, and resource elasticity while embedding quality-of-service indicators like user experience responsiveness, compliance adherence, and fault tolerance. By leveraging cloud performance modeling techniques, queueing theory, and workload simulation, the framework captures the dynamic interplay between tenant isolation, resource contention, and system-level scaling mechanisms. It also accounts for governance requirements such as audit trails, data residency, and regulatory compliance, which significantly impact performance in regulated industries. The model further incorporates predictive analytics and AI-driven monitoring to anticipate bottlenecks, guide capacity planning, and inform policy-driven orchestration strategies. Case scenarios demonstrate how the model enables enterprises and cloud administrators to optimize auto-scaling thresholds, balance loads across geographic regions, and safeguard service-level agreements (SLAs) during peak usage. The proposed performance evaluation model contributes both practical and theoretical insights by providing a structured, multidimensional approach to assessing Microsoft 365 efficiency in multi-tenant, high-concurrency contexts. It offers enterprises a roadmap to achieve resilient, scalable, and secure deployments, ensuring sustainable productivity in increasingly digital and collaborative ecosystems.

Keywords

Microsoft 365, Multi-Tenant Architecture, High Concurrency, Workload Modeling, User Experience Optimization, Service-Level Agreements, Latency Analysis, Throughput Measurement, Resource Utilization, Cloud Scalability, Elasticity, Bottleneck Detection, Workload Balancing, Virtualization Overhead, Application Responsiveness

Citations

IRE Journals:
Olushola Damilare Odejobi, Kabir Sholagberu Ahmed "Performance Evaluation Model for Multi-Tenant Microsoft 365 Deployments Under High Concurrency" Iconic Research And Engineering Journals Volume 1 Issue 11 2018 Page 92-107

IEEE:
Olushola Damilare Odejobi, Kabir Sholagberu Ahmed "Performance Evaluation Model for Multi-Tenant Microsoft 365 Deployments Under High Concurrency" Iconic Research And Engineering Journals, 1(11)