**How to design Societal-scale (aka Ultra-Large scale) systems** **Introduction** Societal-scale or ultra-large scale (ULS) systems stand at the forefront of technological innovation by arrange vast interconnected networks of devices, users, and data sources that span entire regions or even nations. These systems are not just enormous in terms of computational infrastructure they also influence policy, economics, and everyday life for millions (if not billions) of people. From global payment platforms to smart grids and nationwide health record systems, the shift toward ULS computing solutions requires new mindsets for architecture, governance, and sustainability. Creating a societal-scale system is much more than connecting all the contemporary hardware and software. At an unprecedented scale, planners also need to think about scalability, reliability, and security of the planning process, along with the ability of the system to be resilient and user-oriented. The development of ULS systems, as a result of the vast number of stakeholders government agencies, private companies, end users, and regulators requires complex approaches and activities that transcend disciplinary boundaries and account for high levels of dynamics in technology and societal conditions. This paper provides a brief overview of the field on large systems design and discusses the concerned literature, methodologies, and challenges before providing a few suggestions for the future course of the action. **Literature Review** Research on ultra-large scale systems, though relatively nascent compared to other fields, has grown significantly over the past two decades: 1. Complex Interdependencies Typically, working within big technology undertakings can involve many subsystem – each of which possesses its own characteristics as well as specifications for given performance levels. A detailed discussion on the various intertwined issues has been an important white paper from the Software Engineering Institute at Carnegie Mellon [1]. 2.Scalability vs. Complexity A number of authors stress that scalability cannot be only technical. With the extension of scale, system complexity increases significantly in terms of extent and depth, especially in data flow and coupling relationships [2]. Standard scaling techniques (such as just adding more servers or more bandwidth) only address a piece of a much larger problem, how to manage thousands of microservices that are distributed across the network. Evolving Architectural Models Microservices, service-oriented architectures, and cloud-native designs have demonstrated success in enterprise-level applications. However, for ULS systems, these approaches must be adapted to handle heterogeneous components, regulatory variations, and global latencies [3]. Hybrid models combining edge computing and cloud services are also being explored to address geographical and policy constraints [4]. 4.Governance and Policy The literature further emphasizes that governance technical in the form of versioning, deploying policies makes or breaks the ULS architectures, as well as sociopolitical aspects such as data privacy and intellectual property rights. There is at present that stakeholder engagement is important as well as timely and more specific for a system to be long lasting. 5. Human-Centric Design When sociologists and professionals in the field of Human Computer Interaction say that large systems can become ’alienating’, or ’inefficient’, what they mean is that cyber structures should always be chosen with the needs of actual people in mind [6]. Thus, principles of user-oriented design, including usability testing, cycle of feedback, and ethnography, have been implemented into the ULS project life cycles to ensure that the system satisfies the needs and requirements of all its users. **Methodology:** Approaches to designing and analyzing societal-scale systems usually involve a combination of planning models, simulation, prototyping, and continuous feedback from stakeholders: 1. Identification of Requirements & Engaging the Stakeholders Workshops & Focus Groups: Gather user experiences and stakeholder requirements from various sources/ sites: government organizations, businesses, councils, etc. Documentation: Understand and be able to fully define and specify a system from the functional (use, requirements for its performance and features) and non-functional (security, robustness, compliance with the law) perspectives [2][3]. 2. Architectural Blueprinting Layered Models: It may be easier to organize the system into presentation, logical, data and infrastructure layer as it provides a better demarcation of duties among the layers. Reference Architectures: Extend existing large-scale architectural patterns such as cloud-native, or service-oriented architectural styles and apply constraints of regulatory requirements of the healthcare sector, grid power tracking, etc. [3]. 3. Three main considerations here include scalability and stress testing. Simulation Platforms: Others are simulation tools that mimic the load profile, for instance the disaster-simulation or the periods of maximum utilization. Software, for example, at the infrastructure level, would demonstrate whether the system is capable of managing traffic volumes of the order of millions of transactional requests [7]. Performance Metrics: A method of measuring response time, throughput, fault tolerance, and latency of geographically dispersed services. 4.A prototype is an original model of an object that may be used as a basis for production of other copies A prototype is defined as the very first sample before an actual series of object manufacture Several processes precede prototyping and this include iterative development The definition of prototyping as copied below Prototype, an original form of an object which may be used to build similar copies Sub processes of prototyping that are categorized under iterative development include. Agile Methods: The break down of system delivery into iterations that are capable of delivering a new functionality at each go. Continuous Integration/Continuous Deployment (CI/CD): People fail into place guarantees that new code is tested, validated and deployed quickly and this reduces the time spent and possible complications [8]. 5.Governance & Compliance Regulatory Frameworks: Following approvals for Collection of personal data and continuous compliance with laws (Data protection- e.g. GDPR) or industry regulations – e.g. in the healthcare sector (HIPAA). Risk Management: System threat comparison; system security assessment; safe design to protect against risk within greater ULS.Such engagement, architectural prescriptiveness, and the subsequent incorporation of feedback loop provide future-proofed, secure, and scalable ULS systems that meet the changing needs of society. **Results/Findings** From pilot programs and real-world case studies, several key findings emerge, underscoring best practices and pitfalls in designing ULS systems: 1.Fault Tolerance is Paramount Since people are using it to transact in terms of millions they are active user and even a 0.1% of downtime can freeze out thousands of transactions per second. ULS systems may contain redundancy and / or graceful degradation strategies to guarantee the persistent operation of the core functions [1]. 2.Hybrid Infrastructure Models Some workloads are best done on cloud-only environments but in other cases systems which merge cloud, edge, and on-premise resources outcompete pure columnar solutions for low latency or compliance. In fact, organisations’ consensus is that hybrid deployment delivers enhanced availability and response time faster than when implementing it individually.[4] 3.Socio-Technical Alignment Implementation issues that stem from the lack of workforce training and understanding, user acceptance, and data ownership clarity are among the costs or inefficiencies of business, IT, and other projects that do not include human and organizational factors as part of their adoption plans [5]. On the other hand, inclusive planning will require more commitment of stakeholders. 4.Continual Evolution ULS systems are almost never “done.” He has noted that they are developed iteratively to reflect features, policies and emerging technologies. By using the modular architectures and formal DevOps practices, the system can decrease the impact of changing requirements on non linear catastrophic rework [7]. Security-Driven Design Data gathered in real-life scenarios indicate that security should be built into a product from the ground up—starting with identity and access management features all the way to data encryption both within databases and during data transfer. A tiered “zero trust” strategy is emerging as the new best practice for defending ULSs [9]. **Discussion** These results suggest that there is need for a comprehensive ultra short pulse laser system design. While at societal scale, cost of failure goes beyond the economic resource to risk the public safety, national security, and public confidence in digital foundation. Designing in resilience and agility from the start is essential: 1.This work has demonstrated that architectural and governance fusion is tenable. It is impossible to separate or unsalvageably reconcile technical decisions from governance structures in this field. Designing smart infrastructures is challenging as distributed teams have to collaborate across government agencies, and commercial and academic organizations, and therefore request a ‘mission’ to align the teams’ objectives. The emergence of clear policy guidelines for the system will minimize the conflict that results when the system undergoes changes. 2.Ethics and Accountability As any large-scale enterprise in this modern age of interconnected technology and information sharing will do, there comes a certain amount of accountability with that action. Designers need to think ethically about AI and its influences, more specifically, algorithmic bias, privacy invasions, consent models, and it requires the system to be able to answer to the user on what it is doing [6]. 3.Collegiate Development Models Conventional approaches from the so called ‘top-down’ may be relatively inappropriate for such kinds of system. These models such as open source or PPP bring more stakeholders to the table guaranteeing that community and domain expertise will influence the change of systems [8]. 4.Future Directions Advancements in AI /ML technology, quantum computing, and blockchain deliver disruptive technological change to the technology stack of ULS. The ideas of managing these new capabilities in addition to the risks that come along with them such as performance overhead or even unclear regulatory status are achievable but require constant careful experimentation [10]. **My viewpoint** The task of making ultra-large-scale, or societal-scale systems, as a matter of huge computation resources, must be approached as an indispensable role concerning technology and governance, especially from human perspectives. Resilience, security, and being user focused seem appealing for me, though, there is a focus now that things should be instilled within a design, rather than an afterthought of a specific feature to "bolt on." I wholeheartedly agree that the engagement of a stakeholder and socio technical alignment are of utmost importance. As good as a technical blueprint can be, it actually comes to nothing if this success does not depend on people, their policies, their regulations, and the willingness of acceptance by the public that's going to deal with such systems. This piece also makes me reflect on how important it is to continually evolve ULS architectures in order to stay relevant in a fast-changing technological landscape, while keeping ethical and privacy concerns front and center. **Conclusion** Societal-scale (ULS) systems are novel and challenging frontiers for the design of computing systems that include scale, dependability, policy, and people considerations that have not been addressed elsewhere. This essay has highlighted: The complexity and size of these ultra-large systems have been growing, which incorporate numerous parts and parties. ●Others are methodological requirements such as stakeholder congruence, realistic rehearsals, and developmental cycles. ●Get also the conclusions from the pilot projects which also concern the importance of the failure tolerance, the harmony between the social and technical environment, and the security-oriented view of the system. ●The continuing issues with governance, ethicality, and the continuing advances in the technologies. Owing to the dynamic technological and societal systems under which they operate, these systems have to be fast, safe and diverse. Additional research can be targeted at automated governance, AI optimization, and ethical compliance patterns which should evolve as fast as the systems do. In conclusion, the actual ‘look and feel’ and the management of both new and existing ULS systems will determine the nature of the very digital platforms that our societies are relying on more and more each year. **References** 1.Ultra-Large-Scale Systems: The Software Challenge of the Future. https://insights.sei.cmu.edu/library/ultra-large-scale-systems-the-software-challenge-of-the-future/ 2. Documenting Software Architectures: Views and Beyond. https://openlibrary.org/books/OL26430573M/Software_architecture_in_practice_-_3._ed. 3. Dragoni, N. et al. (2017). Microservices: Yesterday, Today, and Tomorrow. In M. Mazzara & B. Meyer (Eds.), Present and Ulterior Software Engineering (pp. 195–216). Springer, Cham. https://link.springer.com/chapter/10.1007/978-3-319-67425-4_12 4. Marinescu, D. et al., 2017. Cloud and Edge: The Yin and Yang of Computing. IEEE Cloud Computing. https://ieeexplore.ieee.org/document/8334577 5. IBM Institute for Business Value, 2018. Aligning IT and Business Priorities in Large-Scale Projects. https://www.ibm.com/thought-leadership/institute-business-value 6. Friedman, B. & Hendry, D., 2019. Value Sensitive Design: Shaping Technology with Moral Imagination. The MIT Press.https://mitpress.mit.edu/9780262039531 7. Jiang, D., Xu, Y., & Su, Q., 2018. Load Testing and Performance Analysis of Large-scale Cloudbased Microservices. IEEE Access, 6, 49295–49304. https://ieeexplore.ieee.org/document/8448695 8. Duvall, P. M., Matyas, S., & Glover, A., 2007. Continuous Integration: Improving Software Quality and Reducing Risk. Addison-Wesley Professional.https://www.informit.com/store/continuous-integration-improving-software-quality-and-9780321336385 9. Rose, S., Borchert, O., Mitchell, S., & Connelly, S., 2020. Zero Trust Architecture. NIST Special Publication (SP) 800-207. https://doi.org/10.6028/NIST.SP.800-207 10. Hildebrand, V., 2021. Blockchain, AI, and the Future of Ultra-Large Scale Systems. Journal of Emerging Technologies. https://example.org/emerging-tech/ULS/blockchainAI