Two Stage Deadline Aware Cloudlet Scheduler for Time Critical Workloads

In modern computing environments such as cloud, edge, and fog computing, as well as IoT networks and real-time systems, meeting workload deadlines is critical to ensure reliability, quality of service, and user satisfaction. The traditional scheduling algorithms often fail to adequately address the constraints associated with the workloads, particularly deadlines, the dynamic nature of workloads and resources, and the inherent resource limitations. Deadlines are the most important constraints, especially for time-sensitive applications. Achieving deadline compliance requires optimal resource provisioning, scheduling, resource allocation, resource scaling, workload migration, etc. This paper proposes a novel deadline-aware cloudlet scheduling algorithm, the Two-Stage Deadline-Aware Cloudlet Scheduling Algorithm (TSDACS), designed to minimize deadline misses through efficient resource provisioning and scaling strategies. In stage one, the algorithm provisions virtual machines with suitable configurations and quantities based on the requirements of the cloudlets to ensure they can be processed within their deadlines. Cloudlets are then scheduled onto the virtual machines in a way that minimizes deadline violations. In stage two, if the initially provisioned virtual machines fail to meet the deadlines, horizontal scaling is applied, up to a limited threshold, to enhance the performance and the deadline compliance. Experimental results demonstrate that the proposed TSDACS algorithm outperforms existing approaches, such as CPDALB, DBS, and RDLBS2, in terms of deadline miss ratio, makespan, response time, and cost efficiency, while maintaining competitive VM utilization and effective load balancing.