Real-time trust aware scheduling in fog-cloud systems

Fog computing offers cloud-like facilities at the network edge, delivering reduced response times to latency sensitive applications. It comprises of fog devices/micro data centers/cloudlets located between users and the cloud data center. Fog devices are generally susceptible to privacy, security, and trust issues. We propose RT-TADS (Real Time-Trust Aware Dynamic Scheduling), a scheduling algorithm that accounts for privacy, trust and real-time performance. To compute the trustworthiness of fog devices, we propose a trust computation model. This model factors in direct and recommended trust techniques for each fog device, and updates their aggregated trust values at regular intervals. User tasks are tagged as: private, semi-private, and public. Fog devices are classified as: extremely highly trusted, highly trusted, normal trusted, low trusted, and untrusted. RT-TADS maps the input jobs according to their privacy constraints on trustworthy fog devices, which increases the overall Success Ratio, hence improving real-time performance. Using the Bitbrain dataset, the real-time performance of RT-TADS has been demonstrated, versus comparable algorithms. The results indicate that the proposed RT-TADS offers an average improvement of 13%, 45%, and 71% in task success ratio compared to RLTCM, no-trust, and cdc-only respectively.