Fog computing brings the cloud closer to a user with the help of a micro data center ( $mdc$ ), leading to lower response times for delay sensitive applications. RT-SANE ( R eal- T ime S ecurity A ware scheduling on the N etwork E dge) supports batch and interactive applications, taking account of their deadline and security constraints. RT-SANE chooses between an $mdc$ (in proximity to a user) and a cloud data center ( $cdc$ ) by taking account of network delay and security tags. Jobs submitted by a user are tagged as: private, semi-private and public, and $mdcs$ and $cdcs$ are classified as: trusted, semi-trusted and untrusted. RT-SANE executes private jobs on a user’s local $mdcs$ or pre-trusted $cdcs$ , and semi-private and public jobs on remote $mdcs$ and $cdcs$ . A security and performance-aware distributed orchestration architecture and protocol is made use of in RT-SANE. For evaluation, workload traces from the CERIT-SC Cloud system are used. The effect of slow executing straggler jobs on the Fog framework are also considered, involving migration of such jobs. Experiments reveal that RT-SANE offers a higher “success ratio” (successfully completed jobs) to comparable algorithms, including consideration of security tags.

Fog computing is a promising option for time sensitive vehicular over-the-air (OTA) updates, as it can offer enhanced network durability and lower communication delays, versus the cloud. Fog node utilization is non-deterministic, largely owing to vehicular traffic patterns. The resultant over provisioning of resources manifests itself in increased communication and handover delays. Based on an analysis of regional traffic patterns, our proposed algorithm determines the optimal number of fog nodes required for OTA updates. The efficacy of our proposed approach is demonstrated using a case study that considers handover delay, propagation delay, transmission rate and vehicular mobility to predict the OTA update time. We employ a machine learning model for predicting the communication delay between fog devices and vehicles. Using European WiFi hotspot signal strength NYC dataset and 5G dataset, we observe that the proposed approach increases net reserve fog resources by 26.57% on average, and reduces the OTA update time by 5.34%. We test the scalability of our approach by analyzing the performance in terms of average throughput while varying the number of vehicles and OTA update size. The performance of the proposed OTA update scheme on simulations has been corroborated by implementation on a real-world testbed.

Cloud Computing has changed the strategy of the way of providing different services to business and government agent as well as to sole users. Cloud Computing provides scalable and on demand services to users irrespective of their physical area. But this computing technology has many challenges. One of them is on demand resource allocation. In this paper a resource allocation method is proposed based on Analytical Hierarchy Process (AHP) called Multicriteria Preference Synthesis (MPS) method. MPS method combines many AHP synthesis methods (Additive Normalization, EigenVector, Multicriteria Synthesis etc.) to assign the priorities to the users' tasks. In MPS method Error Criteria is used to provide the consistency in providing the priorities to users' tasks if any violation is there.