Leveraging Hyperforce for Scalable and Secure Multi-Cloud Solutions: An Architectural and Implementation Guide
DOI:
https://doi.org/10.47604/ijsm.2881Keywords:
Multi-Cloud Strategies, Hyperforce, Salesforce, Architectural Design, Data Residency, Scalability, Digital Transformation, Cloud Computing, Implementation GuideAbstract
Purpose: In today’s fast-paced digital world, businesses are turning to multi-cloud strategies to boost their performance, flexibility, and resilience. This paper aims to provide a straightforward guide on leveraging Salesforce’s Hyperforce in a multi-cloud environment. Hyperforce is designed to work with major public cloud providers, offering unmatched scalability, data residency compliance, and efficiency. The primary objective is to help IT professionals and decision-makers fully leverage Hyperforce in a multi-cloud strategy to drive digital transformation and maintain competitiveness.
Methodology: This paper explores the key aspects necessary for a successful Hyperforce and multi-cloud setup, including architectural design, implementation steps, data management, security, performance optimization, and cost management. Through a combination of clear technical insights and real-world examples, the paper illustrates the challenges and best practices associated with implementing Hyperforce in a multi-cloud environment.
Findings: By spreading workloads across different cloud platforms, companies can enhance redundancy, avoid vendor lock-in, and optimize resource utilization. The paper identifies critical areas such as architectural design, security, and cost management that are essential for successful multi-cloud adoption using Hyperforce. Additionally, the paper delves into emerging trends and future innovations in the multi-cloud space, providing insights into the evolving landscape and what lies ahead for Hyperforce.
Unique Contribution to Theory, Practice and Policy: The paper recommends that IT professionals and decision-makers focus on fully understanding and utilizing Hyperforce’s capabilities within a multi-cloud strategy. This involves following best practices in architectural design, implementation, data management, and security to maximize the benefits of Hyperforce. The insights and tools provided in this paper are intended to equip organizations with the knowledge necessary to effectively implement Hyperforce and drive their digital transformation efforts forward.
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References
Achar, S. (2021). Enterprise saas workloads on new-generation infrastructure-as-code (iac) on multi-cloud platforms. Global Disclosure of Economics and Business, 10(2), 55-74.
Alonso, J., Orue-Echevarria, L., Casola, V., Torre, A. I., Huarte, M., Osaba, E., & Lobo, J. L. (2023). Understanding the challenges and novel architectural models of multi-cloud native applications–a systematic literature review. Journal of Cloud Computing, 12(1), 6.
Alshammari, M. M., Alwan, A. A., Nordin, A., & Abualkishik, A. Z. (2021). Data backup and recovery with a minimum replica plan in a multi-cloud environment. In Research Anthology on Privatizing and Securing Data (pp. 794-814). IGI Global.
Benhssayen, K., & Ettalbi, A. (2021). Semantic interoperability framework for IAAS resources in multi-cloud environment. International Journal of Computer Science & Network Security, 21(2), 1-8.
Cai, X., Geng, S., Wu, D., Cai, J., & Chen, J. (2020). A multicloud-model-based many-objective intelligent algorithm for efficient task scheduling in internet of things. IEEE Internet of Things Journal, 8(12), 9645-9653.
Cao, X., Bo, H., Liu, Y., & Liu, X. (2023). Effects of different resource-sharing strategies in cloud manufacturing: A Stackelberg game-based approach. International Journal of Production Research, 61(2), 520-540.
Dubey, M., & Singh, K. Multi-Cloud Management Strategies-A Comprehensive.
Gundu, S. R., Panem, C. A., & Thimmapuram, A. (2020). Hybrid IT and multi cloud an emerging trend and improved performance in cloud computing. SN Computer Science, 1(5), 256.
Heilig, L., Lalla-Ruiz, E., & Voß, S. (2020). Modeling and solving cloud service purchasing in multi-cloud environments. Expert systems with applications, 147, 113165.
Jiang, F., Ferriter, K., & Castillo, C. (2020, April). A cloud-agnostic framework to enable cost-aware scheduling of applications in a multi-cloud environment. In NOMS 2020-2020 IEEE/IFIP Network Operations and Management Symposium (pp. 1-9). IEEE.
Kanth, T. C. (2023). Contemporary Devops Strategies For Augmenting Scalable And Resilient Application Deployment Across Multi-Cloud Environments.
Lahmar, F., & Mezni, H. (2021). Security-aware multi-cloud service composition by exploiting rough sets and fuzzy FCA. Soft Computing, 25(7), 5173-5197.
Mohammadzadeh, A., & Masdari, M. (2023). Scientific workflow scheduling in multi-cloud computing using a hybrid multi-objective optimization algorithm. Journal of Ambient Intelligence and Humanized Computing, 14(4), 3509-3529.
Naidu, P. R., Guruprasad, N., & Gowda, V. D. (2021, May). A high-availability and integrity layer for cloud storage, cloud computing security: from single to multi-clouds. In Journal of Physics: Conference Series (Vol. 1921, No. 1, p. 012072). IOP Publishing.
Pachala, S., Rupa, C., & Sumalatha, L. (2021). An improved security and privacy management system for data in multi-cloud environments using a hybrid approach. Evolutionary Intelligence, 14, 1117-1133.
Rajeshwari, B. S., Dakshayini, M., & Guruprasad, H. S. (2022). Workload balancing in a multi-cloud environment: challenges and research directions. Operationalizing Multi-Cloud Environments: Technologies, Tools and Use Cases, 129-144.
Rajput, K. Y., Li, X., Lakhan, A., Zhang, J., Mahesar, A. R., & Sajnani, D. K. (2024, May). Task Scheduling in Multi-Cloud Environments for Spark Workflow under Performance Uncertainty. In 2024 27th International Conference on Computer Supported Cooperative Work in Design (CSCWD) (pp. 2752-2757). IEEE.
Souri, A., Rahmani, A. M., Navimipour, N. J., & Rezaei, R. (2020). A hybrid formal verification approach for QoS-aware multi-cloud service composition. Cluster Computing, 23, 2453-2470.
Tomarchio, O., Calcaterra, D., & Modica, G. D. (2020). Cloud resource orchestration in the multi-cloud landscape: a systematic review of existing frameworks. Journal of Cloud Computing, 9(1), 49.
Zhu, Q. H., Tang, H., Huang, J. J., & Hou, Y. (2021). Task scheduling for multi-cloud computing subject to security and reliability constraints. IEEE/CAA Journal of Automatica Sinica, 8(4), 848-865.
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Copyright (c) 2024 Divya Asuri Narasimha Chary
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