AI-Native Telecom Cloud Resilience: A Predictive Service-Assurance and Autonomous Recovery Framework for Saudi Mega-Events—Riyadh Season, Expo 2030, and FIFA World Cup 2034

Abstract

Saudi Arabia’s expanding programme of international sporting, cultural, religious, and technology events is creating a new class of telecommunications operational challenge. Annual events such as Hajj and Riyadh Season already generate highly concentrated digital demand, while upcoming national milestones—including the AFC Asian Cup 2027, Expo 2030 Riyadh, and the FIFA World Cup 2034—will require uninterrupted connectivity across stadiums, exhibition districts, transportation systems, fan zones, airports, hotels, government platforms, and emergency-service networks. The telecommunications challenge associated with these events extends beyond radio-access congestion. Modern mobile services increasingly depend on distributed telecom-cloud environments comprising virtualized network functions, cloud-native network functions, Kubernetes clusters, edge-computing nodes, service-mesh components, storage platforms, transport connectivity, orchestration systems, and multi-vendor infrastructure. A failure in any of these layers can affect large numbers of subscribers even when sufficient radio capacity remains available. Telecom operators must therefore move from infrastructure-centred monitoring toward predictive, service-aware, and autonomously recoverable cloud operations. This study proposes the ResilienceCloud-KSA Framework, an AI-native telecom-cloud resilience model designed for high-demand Saudi mega-events. The framework introduces two original components. The first, EventCloud-Predict, is a multimodal service-risk forecasting engine that combines infrastructure telemetry, application-performance indicators, historical incidents, event schedules, crowd-movement forecasts, service dependencies, change records, and environmental information to estimate service-degradation risks from fifteen minutes to several hours in advance. The second, AutoRecover-KSA, is a five-layer operational architecture that coordinates anomaly detection, service-impact assessment, AI-assisted root-cause analysis, policy-governed remediation, and post-recovery learning across multi-vendor telecom-cloud platforms. Unlike conventional capacity-planning models, the proposed framework concentrates on service continuity and cloud reliability. It addresses failures such as container exhaustion, Kubernetes-node degradation, storage latency, network-function instability, database saturation, service-mesh errors, unsuccessful software changes, virtual-machine failures, edge-cloud disconnection, certificate expiration, and cross-domain dependency breakdowns. ResilienceCloud-KSA continuously evaluates how these technical conditions may affect customer-facing services, including mobile broadband, digital ticketing, public-safety communications, transportation applications, payment platforms, media services, and event-management systems. The research applies a mixed-method conceptual methodology combining a structured review of telecom-cloud assurance, artificial intelligence for IT operations, cloud-native orchestration, predictive maintenance, digital twins, and autonomous networks. The framework is further informed by Saudi mega-event operational requirements, international event-connectivity experience, telecom-cloud reference architectures, and established standards from 3GPP, ETSI, TM Forum, the Cloud Native Computing Foundation, and the O-RAN Alliance. An analytical scenario-based evaluation is used to assess the framework under representative Expo 2030 and FIFA World Cup 2034 operating conditions. The findings suggest four principal outcomes. First, service degradation during mega-events cannot be managed effectively through isolated fault alarms because individual services depend on interconnected radio, transport, core, cloud, edge, application, and data layers. A service-topology model is therefore required to convert infrastructure alarms into customer-impact predictions. Second, predictive risk scoring can improve operational readiness by identifying probable resource exhaustion, network-function instability, and cascading dependency failures before customer experience is affected. Third, policy-controlled autonomous recovery—including workload scaling, container restart, traffic rerouting, service relocation, resource reallocation, rollback, and failover—can reduce recovery time when combined with human approval for high-impact actions. Fourth, a continuously updated digital operational twin can support pre-event testing, real-time decision-making, and post-event learning across geographically distributed venues. ResilienceCloud-KSA includes an explicit human-governance mechanism called the Event Operations Authority Gate. Low-risk, reversible actions may be executed automatically within approved policies, while actions affecting public-safety services, broadcast systems, core-network functions, regulatory controls, or large subscriber populations require authorized human approval. The framework also maintains an explainable audit trail recording the detected condition, predicted impact, recommended response, approval decision, executed action, and measured recovery outcome. The study concludes that successful telecommunications delivery for Saudi mega-events will depend not only on installing additional network capacity, but also on ensuring that the underlying telecom cloud can anticipate failures, preserve service continuity, and recover safely at machine speed. The proposed framework provides Saudi operators with a structured, multi-vendor, AI-native approach to predictive service assurance and autonomous cloud resilience. It supports phased implementation through recurring national events, Expo 2030, and FIFA World Cup 2034 while aligning with Saudi Vision 2030 priorities concerning digital leadership, operational excellence, artificial-intelligence adoption, and resilient national infrastructure.