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

References

[1] Ackers, B. and Eccles, N.S., 2015. Mandatory corporate social responsibility assurance practices: The case of King III in South Africa. Accounting, Auditing & Accountability Journal, 28(4), pp.515-550.

[2] Ahmad, M., Hijaz, F., Shi, Q. and Khan, O., 2015, October. Crono: A benchmark suite for multithreaded graph algorithms executing on futuristic multicores. In 2015 IEEE International Symposium on Workload Characterization (pp. 44-55). IEEE.

[3] Ajmani, N. and Kumar, D., 2017. Achieving and Sustaining Secured Business Operations. An Executive’s Guide to Planning and Management. Editorial Apress.

[4] Alcaraz, C. and Zeadally, S., 2015. Critical infrastructure protection: Requirements and challenges for the 21st century. International journal of critical infrastructure protection, 8, pp.53-66.

[5] Al-Dhuraibi, Y., Paraiso, F., Djarallah, N. and Merle, P., 2017. Elasticity in cloud computing: state of the art and research challenges. IEEE Transactions on services computing, 11(2), pp.430-447.

[6] Benzekki, K., El Fergougui, A. and Elbelrhiti Elalaoui, A., 2016. Software‐defined networking (SDN): a survey. Security and communication networks, 9(18), pp.5803-5833.

[7] Bermbach, D., Wittern, E. and Tai, S., 2017. Cloud service benchmarking (pp. 27-28). New York, NY: Springer International Publishing.

[8] Betser, J. and Hecht, M., 2015. Big Data on clouds (BDOC). Cloud Services, Networking, and Management, pp.361-391.

[9] Brunnert, A., van Hoorn, A., Willnecker, F., Danciu, A., Hasselbring, W., Heger, C., Herbst, N., Jamshidi, P., Jung, R., von Kistowski, J. and Koziolek, A., 2015. Performance-oriented DevOps: A research agenda. arXiv preprint arXiv:1508.04752.

[10] Chen, Z., Hu, W., Wang, J., Zhao, S., Amos, B., Wu, G., Ha, K., Elgazzar, K., Pillai, P., Klatzky, R. and Siewiorek, D., 2017, October. An empirical study of latency in an emerging class of edge computing applications for wearable cognitive assistance. In Proceedings of the Second ACM/IEEE Symposium on Edge Computing (pp. 1-14).

[11] Cho, B., Ng, A. and Sun, C., 2017, April. CoVim: incorporating real-time collaboration capabilities into comprehensive text editors. In 2017 IEEE 21st International Conference on Computer Supported Cooperative Work in Design (CSCWD) (pp. 192-197). IEEE.

[12] Ciuciu, I., Panetto, H., Debruyne, C., Aubry, A., Bollen, P., Valencia-Garcia, R., Mishra, A., Fensel, A. and Ferri, F. eds., 2015. On the move to meaningful internet systems: OTM 2015 workshops. Springer International Publishing.

[13] Copil, G., Moldovan, D., Truong, H.L. and Dustdar, S., 2016. Continuous elasticity: Design and operation of elastic systems. IT-Information Technology, 58(6), pp.329-348.

[14] Coutinho, E.F., de Carvalho Sousa, F.R., Rego, P.A.L., Gomes, D.G. and de Souza, J.N., 2015. Elasticity in cloud computing: a survey. annals of telecommunications-annales des télécommunications, 70(7), pp.289-309.

[15] Coutinho, E.F., de Carvalho Sousa, F.R., Rego, P.A.L., Gomes, D.G. and de Souza, J.N., 2015. Elasticity in cloud computing: a survey. annals of telecommunications-annales des télécommunications, 70(7), pp.289-309.

[16] David, O., 2017. Anomaly Detection for Phishing Target Identification in Cloud-Based Web Applications.

[17] Edmonds, A., Bohnert, T.M., Metsch, T., Harsh, P., Carella, G., Ferreira, L., Gomes, A., Katsaros, G., Khatibi, S., Marcarini, A. and Muller, J., 2015. Final Overall Architecture Definition, Release 2.

[18] Eliasson, L., Bewley, A.P., Mughal, F., Johnston, K.M., Kuznik, A., Patel, C. and Lloyd, A.J., 2017. Evaluation of psoriasis patients’ attitudes toward benefit–risk and therapeutic trade-offs in their choice of treatments. Patient preference and adherence, pp.353-362.

[19] Fulmer, K.L., 2015. Business continuity planning: A step-by-step guide with planning forms. Rothstein Publishing.

[20] Gurkok, C., 2017. Securing cloud computing systems. In Computer and Information Security Handbook (pp. 897-922). Morgan Kaufmann.

[21] Hubbe, M.A., Metts, J.R., Hermosilla, D., Blanco, M., Yerushalmi, L., Haghighat, F., Lindholm-Lehto, P., Khodaparast, Z., Kamali, M. and Elliott, A., 2016. Wastewater treatment and reclamation: a review of pulp and paper industry practices and opportunities. BioResources, (3).

[22] Jansson‐Boyd, C.V., Robison, R.A., Cloherty, R. and Jimenez‐Bescos, C., 2017. Complementing retrofit with engagement: exploring energy consumption with social housing tenants. International Journal of Energy Research, 41(8), pp.1150-1163.

[23] Jia, Q., Xie, R., Huang, T., Liu, J. and Liu, Y., 2017. The collaboration for content delivery and network infrastructures: A survey. IEEE Access, 5, pp.18088-18106.

[24] Jumagaliyev, A., Whittle, J. and Elkhatib, Y., 2017, December. Using dsml for handling multi-tenant evolution in cloud applications. In 2017 IEEE International Conference on Cloud Computing Technology and Science (CloudCom) (pp. 272-279). IEEE.

[25] Kranz, J.J., Hanelt, A. and Kolbe, L.M., 2016. Understanding the influence of absorptive capacity and ambidexterity on the process of business model change–the case of on‐premise and cloud‐computing software. Information systems journal, 26(5), pp.477-517.

[26] Kranz, M., 2016. Building the internet of things: Implement new business models, disrupt competitors, transform your industry. John Wiley & Sons.

[27] Kumar, T.V., 2017. Designing Resilient Multi-Tenant Applications Using Java Frameworks.

[28] Latif, A., Waring, J., Watmough, D., Barber, N., Chuter, A., Davies, J., Salema, N.E., Boyd, M.J. and Elliott, R.A., 2016. Examination of England's New Medicine Service (NMS) of complex health care interventions in community pharmacy. Research in social and administrative pharmacy, 12(6), pp.966-989.

[29] Ma, K., Abraham, A., Yang, B. and Sun, R., 2016. Intelligent Web Data Management: Software Architectures and Emerging Technologies. Springer International Publishing.

[30] Mace, J., Bodik, P., Fonseca, R. and Musuvathi, M., 2015. Retro: Targeted resource management in multi-tenant distributed systems. In 12th USENIX Symposium on Networked Systems Design and Implementation (NSDI 15) (pp. 589-603).

[31] Madi, T., Majumdar, S., Wang, Y., Jarraya, Y., Pourzandi, M. and Wang, L., 2016, March. Auditing security compliance of the virtualized infrastructure in the cloud: Application to OpenStack. In Proceedings of the Sixth ACM Conference on Data and Application Security and Privacy (pp. 195-206).

[32] Malik, S.U.R., Khan, S.U., Ewen, S.J., Tziritas, N., Kolodziej, J., Zomaya, A.Y., Madani, S.A., Min-Allah, N., Wang, L., Xu, C.Z. and Malluhi, Q.M., 2016. Performance analysis of data intensive cloud systems based on data management and replication: a survey. Distributed and Parallel Databases, 34(2), pp.179-215.

[33] Matthew, O.O., 2016. Establishing a standard scientific guideline for the evaluation and adoption of multi-tenant database.

[34] Meyler, K., Buchanan, S., Scholman, M., Svendsen, J.G. and Rangama, J., 2017. Microsoft Hybrid Cloud Unleashed with Azure Stack and Azure. Sams Publishing.

[35] Mian, J., Dolan, T., Eleanor, E. and Ní Bhreasail, Á., 2017. Resilience of Digitally Connected Infrastructure Systems.

[36] Michael, N., Ramannavar, N., Shen, Y., Patil, S. and Sung, J.L., 2017, April. Cloudperf: A performance test framework for distributed and dynamic multi-tenant environments. In Proceedings of the 8th ACM/SPEC on International Conference on Performance Engineering (pp. 189-200).

[37] Mihret, D.G. and Grant, B., 2017. The role of internal auditing in corporate governance: a Foucauldian analysis. Accounting, Auditing & Accountability Journal, 30(3), pp.699-719.

[38] Morar, M., Kumar, A., Abbott, M., Gautam, G.K., Corbould, J. and Bhambhani, A., 2017. Robust Cloud Integration with Azure. Packt Publishing Ltd.

[39] Muñoz, P., Sallent, O. and Pérez-Romero, J., 2017, May. Capacity self-planning in small cell multi-tenant 5G networks. In 2017 IFIP/IEEE Symposium on Integrated Network and Service Management (IM) (pp. 1109-1114). IEEE.

[40] Musiani, F., Troncoso, C. and Halpin, H., 2016. DELIVERABLE D2.

[41] Nambiar, M., Kattepur, A., Bhaskaran, G., Singhal, R. and Duttagupta, S., 2016. Model driven software performance engineering: Current challenges and way ahead. ACM SIGMETRICS Performance Evaluation Review, 43(4), pp.53-62.

[42] Nissen, V., 2017. Digital transformation of the consulting industry—introduction and overview. In Digital Transformation of the Consulting Industry: Extending the Traditional Delivery Model (pp. 1-58). Cham: Springer International Publishing.

[43] Padnick, J. and DevOps, P., 2015. A Comprehensive Guide to Building a Scalable Web App on Amazon Web Services—Part 1.

[44] Pasham, S.D., 2017. AI-Driven Cloud Cost Optimization for Small and Medium Enterprises (SMEs). The Computertech, pp.1-24.

[45] Rhodes, D., Barker, D., Happy, D., Humphreys, D., Lund, D., Hamid, F., Varrall, G., Macdonald, G., Lota, J., Barrett, M. and Beach, M., 2016. 5G innovation opportunities--A discussion paper.

[46] Roberts, E., Anderson, B.A., Skerratt, S. and Farrington, J., 2017. A review of the rural-digital policy agenda from a community resilience perspective. Journal of Rural Studies, 54, pp.372-385.

[47] Russo, D., 2015. HIDE: User centred Domotic evolution toward Ambient Intelligence.

[48] Sookhak, M., Gani, A., Talebian, H., Akhunzada, A., Khan, S.U., Buyya, R. and Zomaya, A.Y., 2015. Remote data auditing in cloud computing environments: a survey, taxonomy, and open issues. ACM Computing Surveys (CSUR), 47(4), pp.1-34.

[49] Sterman, J., Amengual, M., Gibbons, R., Gulati, R., Henderson, R., Jay, J., Keith, D., King, A., Lyneis, J. and Repenning, N., 2015. Stumbling towards sustainability. Leading sustainable change, pp.50-80.

[50] Stocker, V., Smaragdakis, G., Lehr, W. and Bauer, S., 2017. The growing complexity of content delivery networks: Challenges and implications for the Internet ecosystem. Telecommunications Policy, 41(10), pp.1003-1016.

[51] Sun, C., 2017, April. Reflections on collaborative editing research: From academic curiosity to real-world application. In 2017 IEEE 21st International Conference on Computer Supported Cooperative Work in Design (CSCWD) (pp. 10-17). IEEE.

[52] Suneja, S., Isci, C., De Lara, E. and Bala, V., 2015. Exploring vm introspection: Techniques and trade-offs. Acm Sigplan Notices, 50(7), pp.133-146.

[53] Tang, B., Sandhu, R. and Li, Q., 2015. Multi‐tenancy authorization models for collaborative cloud services. Concurrency and Computation: Practice and Experience, 27(11), pp.2851-2868.

[54] Vu, H., Phung, D., Nguyen, T.D., Trevors, A. and Venkatesh, S., 2017. Energy-based models for video anomaly detection. arXiv preprint arXiv:1708.05211.

[55] Walraven, S., De Borger, W., Vanbrabant, B., Lagaisse, B., Van Landuyt, D. and Joosen, W., 2015, December. Adaptive performance isolation middleware for multi-tenant saas. In 2015 IEEE/ACM 8th International Conference on Utility and Cloud Computing (UCC) (pp. 112-121). IEEE.

[56] Wang, P., Gao, R.X. and Fan, Z., 2015. Cloud computing for cloud manufacturing: benefits and limitations. Journal of Manufacturing Science and Engineering, 137(4), p.040901.

[57] Wasson, C.S., 2015. System engineering analysis, design, and development: Concepts, principles, and practices. John Wiley & Sons.

[58] Weber, R., 2016. Performing property cycles. Journal of Cultural Economy, 9(6), pp.587-603.

[59] Williams, D.N. and Lautenschlager, M., 2016. 5th Annual Earth System Grid Federation Face-to-Face Conference report, Monterey, California, December 7-11, 2015 (No. DOE/SC-0181). USDOE Office of Science (SC), Washington, DC (United States). Biological and Environmental Research (BER).

[60] Zhu, J. and Mostafavi, A., 2017. Performance assessment in complex engineering projects using a system-of-systems framework. IEEE Systems Journal, 12(1), pp.262-273.

@article{1711329,
      author = {Olushola Damilare Odejobi, Kabir Sholagberu Ahmed},
      title = {Performance Evaluation Model for Multi-Tenant Microsoft 365 Deployments Under High Concurrency},
      journal = {Iconic Research And Engineering Journals},
      year = {2018},
      volume = {1},
      number = {11},
      pages = {92-107},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1711329.pdf},
      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},
      month = {May},
  }