Artificial Intelligence
Depth-limited search for real-time problem solving
Real-Time Systems
Extending a blackboard architecture for approximate processing
Real-Time Systems
Algorithms for Scheduling Imprecise Computations
Computer - Special issue on real-time systems
Algorithms for scheduling imprecise computations with timing constraints
SIAM Journal on Computing
Performance evaluation of scheduling algorithms for imprecise computer systems
Journal of Systems and Software
Image transfer: an end-to-end design
SIGCOMM '92 Conference proceedings on Communications architectures & protocols
SIGMETRICS '93 Proceedings of the 1993 ACM SIGMETRICS conference on Measurement and modeling of computer systems
Deliberation scheduling for problem solving in time-constrained environments
Artificial Intelligence
Operational rationality through compilation of anytime algorithms
Operational rationality through compilation of anytime algorithms
Scheduling Algorithms for Multiprogramming in a Hard-Real-Time Environment
Journal of the ACM (JACM)
Foundations of Real-Time Computing: Scheduling and Resource Management
Foundations of Real-Time Computing: Scheduling and Resource Management
Hard Real-Time Systems
Optimal Reward-Based Scheduling for Periodic Real-Time Tasks
IEEE Transactions on Computers
Comment on 'On-Line Scheduling Policies for a Class of IRIS Real-Time Tasks'
IEEE Transactions on Computers
An optimal voltage synthesis technique for a power-efficient satellite application
Proceedings of the 39th annual Design Automation Conference
Real-Time Scheduling of Hierarchical Reward-Based Tasks
RTAS '03 Proceedings of the The 9th IEEE Real-Time and Embedded Technology and Applications Symposium
Maximizing rewards for real-time applications with energy constraints
ACM Transactions on Embedded Computing Systems (TECS)
Journal of Systems and Software
Power-Aware Scheduling for Periodic Real-Time Tasks
IEEE Transactions on Computers
Maximizing the system value while satisfying time and energy constraints
IBM Journal of Research and Development
Multi-version scheduling in rechargeable energy-aware real-time systems
Journal of Embedded Computing - Real-Time Systems (Euromicro RTS-03)
IEEE Transactions on Computers
Utility Accrual Real-Time Scheduling under Variable Cost Functions
IEEE Transactions on Computers
System-wide energy minimization for real-time tasks: Lower bound and approximation
ACM Transactions on Embedded Computing Systems (TECS)
Optimal service level allocation in environmentally powered embedded systems
Proceedings of the 2009 ACM symposium on Applied Computing
Power management in energy harvesting embedded systems with discrete service levels
Proceedings of the 14th ACM/IEEE international symposium on Low power electronics and design
An energy management framework for energy harvesting embedded systems
ACM Journal on Emerging Technologies in Computing Systems (JETC)
Dynamic power management in environmentally powered systems
Proceedings of the 2010 Asia and South Pacific Design Automation Conference
Dual-mode r-reliable task model for flexible scheduling in reliable real-time systems
EUC'06 Proceedings of the 2006 international conference on Embedded and Ubiquitous Computing
Compositional real-time scheduling framework for periodic reward-based task model
Journal of Systems and Software
International Journal of Web and Grid Services
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We consider a real-time task model where a task receives a "reward" that depends on the amount of service received prior to its deadline. The reward of the task is assumed to be an increasing function of the amount of service that it receives, i.e., the task has the property that it receives increasing reward with increasing service (IRIS). We focus on the problem of on-line scheduling of a random arrival sequence of IRIS tasks on a single processor with the goal of maximizing the average reward accrued per task and per unit time. We describe and evaluate several policies for this system through simulation and through a comparison with an unachievable upper bound. We observe that the best performance is exhibited by a two-level policy where the top-level algorithm is responsible for allocating the amount of service to tasks and the bottom-level algorithm, using the earliest deadline first (EDF) rule, is responsible for determining the order in which tasks are executed. Furthermore, the performance of this policy approaches the theoretical upper bound in many cases. We also show that the average number of preemptions of a task under this two-level policy is very small.