Distributed Scheduling of Tasks with Deadlines and Resource Requirements
IEEE Transactions on Computers
Analysis of Checkpointing for Real-Time Systems
Real-Time Systems
Power-Aware Resource Allocation for Independent Tasks in Heterogeneous Real-Time Systems
ICPADS '02 Proceedings of the 9th International Conference on Parallel and Distributed Systems
Energy-Aware Task Allocation for Rate Monotonic Scheduling
RTAS '05 Proceedings of the 11th IEEE Real Time on Embedded Technology and Applications Symposium
Dependability Driven Integration of Mixed Criticality SW Components
ISORC '06 Proceedings of the Ninth IEEE International Symposium on Object and Component-Oriented Real-Time Distributed Computing
Task allocation for maximizing reliability of distributed systems: a simulated annealing approach
Journal of Parallel and Distributed Computing
Maximizing the Fault Tolerance Capability of Fixed Priority Schedules
RTCSA '08 Proceedings of the 2008 14th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications
RTSS '08 Proceedings of the 2008 Real-Time Systems Symposium
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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 reexecutions, 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.