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. Hassan· International Journal of Eme...· 0 citations
A self-healing Cloud–IoT architecture enhanced by adversarial machine learning (AML) to autonomously detect, mitigate, and recover from malicious disruptions is proposed, proving the effectiveness of AML-driven self-healing mechanisms for next-generation distributed systems.
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
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
