The use of multi-clouds has taken the form of a leading architecture trend in organizations that desire a resilient, vendor-independent enterprise, regulatory-compliant, and cost-performance trade-off platforms. Nevertheless, allocating workloads, data and identities among heterogeneous cloud service providers (CSPs) present challenging and dynamic security issues. In this paper, the author offers an in-depth and detailed discussion of the current changes in the multi-cloud security frameworks, focusing on the architectural foundations, threat patterns, governance processes, and new technologies that are defining the secure multi-cloud environments. The abstract expounds the rationale behind the need of multi-cloud security, constraints in the single-cloud security posture, and the need to have coherent but provider-understanding security models. In this paper, the researcher has synthesized academic literature, industry white papers, and standards that have been published before 2024 to arrive at major security trends, such as zero trust architecture and systems, cloud security posture management (CSPM), cloud workload protection systems (CWPP), identity-centric security, confidential computing, policy-as-code, and AI-assisted threat detection. A multi-layered approach has been suggested that incorporates the governance, identity, data, network, and application security among various CSPs with Interoperability and compliance. The discussion and results lead to a qualitative assessment of the efficiency of the proposed framework in comparison to the existing findings and a significant enhancement of the visibility, decrease of the risk, and consistency in the operations. The conclusion provides the future direction of research, such as autonomous security orchestration and cryptography resilience. The paper provides a systematic resource to researchers and practitioners developing secure, scalable and compliant multi-cloud environments
Ahmed Hassan· International Journal of Eme...· 0 citations
The rapid expansion of Cloud–IoT ecosystems has introduced unprecedented scalability and flexibility but also created complex attack surfaces vulnerable to evolving cyber threats. Traditional intrusion detection and fault-tolerance techniques struggle to address adversarial attacks that exploit machine-learning models and disrupt IoT service continuity. This paper proposes a self-healing Cloud–IoT architecture enhanced by adversarial machine learning (AML) to autonomously detect, mitigate, and recover from malicious disruptions. The framework integrates adversarial-resilient anomaly detection, dynamic attack classification, and automated healing modules that leverage reinforcement learning (RL) and predictive models to restore system functionality with minimal human intervention. Experimental evaluations demonstrate improved robustness, reduced downtime, and higher detection accuracy under various adversarial scenarios, proving the effectiveness of AML-driven self-healing mechanisms for next-generation distributed systems. This work highlights essential design considerations and presents future directions for secure, autonomous, and resilient Cloud–IoT infrastructures.
Salma El-Sayed, Ahmed Hassan· International Journal of Art...· 0 citations
Edge computing is a promising paradigm for deploying latency-sensitive applications (Apps) as it brings resources closer to end users. Edge Apps often adopt a microservice (MS) architecture, breaking monolithic Apps into lightweight, containerized MSs that can be dynamically and independently deployed. However, managing such Apps involves three key challenges: (i) optimizing the placement of MSs to reduce both response time and resource overhead, (ii) handling MS migration or relocation as users move while minimizing App service disruption (App downtime), and (iii) enabling MS sharing across Apps while ensuring performance guarantees. We formulate this as an optimization problem, named Multi-microservice Application Placement (MAP), prove its NP-hardness, and introduce STEP (State and Topology-aware Edge-MS Placement), a polynomial-time heuristic. STEP distinguishes itself from prior work by: (i) jointly considering stateful and stateless MS characteristics in deployment decisions, (ii) exploiting MS shareability to reduce resource usage, (iii) balancing response latency, App downtime, and resource utilization, and (iv) leveraging multiple versions of the same MS to adapt quality of service to available edge resources. Our results in a small-scale scenario show that STEP achieves near-optimal performance with only 7% higher CPU cost than the optimal solution. Large-scale real-time experiments on a Kubernetes cluster demonstrate that STEP consistently outperforms competing methods, achieving up to 50% lower deployment costs while delivering 50% gain in app quality and saving 15% in radio resources with over 90% request success rates.
Madhura Adeppady, Yenchia Yu, Ali Rahmanian et al.· IEEE Transactions on Network...· 0 citations