Abstract
SummaryFog 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.
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