<p>To address the challenges of latency-aware resource management and task scheduling in cloud environments (as well as to mitigate bandwidth limitations, operational costs, and security concerns), the concept of edge computing has been introduced. In edge-based infrastructures, computational resources are positioned at the edge of the network, in close proximity to end-user devices. Realizing the benefits of such infrastructures requires comprehensive resource management policies, which themselves present numerous challenges. To tackle these, this paper proposes a solution to the problem of task and workflow ensemble scheduling in edge environments. A key objective of the proposed approach is to maximize user satisfaction. To this end, we incorporate optional tasks alongside mandatory ones within each workflow to better address varying user requirements. For instance, in multimedia applications, additional processing can enhance media quality and improve user satisfaction. Real-time cloud services also exemplify applications that demand such capabilities. To model task dependencies and determine their execution order within applications, we employ a workflow ensemble model. The proposed solution utilizes the Late Acceptance Hill Climbing (LAHC) algorithm to solve the scheduling problem. Our aim is to identify optimal task-to-resource assignments that maximize the number of workflows completed within their respective deadlines and budgets. Simulation results demonstrate that the proposed algorithm performs efficiently in terms of execution time, number of completed workflows, and monetary cost when compared to existing approaches.</p>