Industrial real-time systems typically have to satisfy complex requirements, mapped to the task attributes, eventually guaranteed by a fixed priority scheduler in a distributed environment. These systems consist of a mix of hard and soft tasks with varying criticality, as well as associated fault tolerance requirements. Time redundancy techniques are often preferred in industrial applications and, hence, it is extremely important to devise resource efficient methodologies for scheduling real-time tasks under failure assumptions. In this paper, we propose a methodology to provide a priori guarantees in distributed real-time systems with redundancy requirements. We do so by identifying temporal feasibility windows for all task executions and re-executions, as well as allocating them on different processing nodes. We then use optimization theory to derive the optimal feasibility windows that maximize the utilization on each node, while avoiding overloads. Finally on each node, we use Integer Linear Programming (ILP) to derive fixed priority task attributes that guarantee the task executions within the derived feasibility windows, while keeping the associated costs minimized.