Slowdown factors determine the extent of slowdown a computing system can experience based on functional and performance requirements. Dynamic Voltage Scaling (DVS) of a processor based on slowdown factors can lead to considerable energy savings. The problem of DVS in the presence of task synchronization has not yet been addressed. We compute slowdown factors for tasks which synchronize for access to shared resources. Tasks synchronize to enforce mutually exclusive access to these resources and can be blocked by lower priority tasks. We compute static slowdown factors for the tasks which guarantee meeting all the task deadlines. Our simulation experiments show on an average 25% energy gains over the known slowdown techniques.

Procrastination scheduling has gained importance for energy efficiency due to the rapid increase in the leakage power consumption. Under procrastination scheduling, task executions are delayed to extend processor shutdown intervals, thereby reducing the idle energy consumption. We propose algorithms to compute the maximum procrastination intervals for tasks scheduled by either the fixed priority or the dual priority scheduling policy. We show that dual priority scheduling always guarantees longer shutdown intervals than fixed priority scheduling. We further combine procrastination scheduling with dynamic voltage scaling to minimize the total static and dynamic energy consumption of the system. Our simulation experiments show that the proposed algorithms can extend the sleep intervals up to 5 times while meeting the timing requirements. The results show up to 18% energy gains over dynamic voltage scaling.

Slowdown factors determine the extent of slowdown a computing system can experience based on functional and performance requirements. Dynamic Voltage Scaling (DVS) of a processor based on slowdown factors can lead to considerable energy savings. We address the problem of computing slowdown factors for dynamically scheduled tasks with specified deadlines. We present an algorithm to compute a near optimal constant slowdown factor based on the bisection method. As a further generalization, for the case of tasks with varying power characteristics, we present the computation of near optimal slowdown factors as a solution to convex optimization problem using the ellipsoid method. The algorithms are practically fast and have the same time complexity as the algorithms to compute the feasibility of a task set. Our simulation results show on an average 20% energy gains over known slowdown techniques using static slowdown factors and 40 % gains with dynamic slowdown. 1.

Energy-efficient data transmission is an important issue for mobile communication devices. In this paper, we study throughput and value-based packet scheduling over a wireless link under time and energy constraints. A mobile node is required to deliver multiple data streams to multiple receivers. These data streams may have different importance and incur different energy costs. In this research, point-topoint wireless data transmission is studied. The objective is to develop schemes that selectively transmit data streams at certain power levels so that the data throughput or the value of data delivery is maximized without violating the energy and time constraints. Exploiting the special properties of the throughput maximization problem, we propose an iterative algorithm that yields the optimal transmission schedule. The value maximization problem is formulated as a convex optimization problem and the optimal results can be obtained by solving the Kuhn-Tucker conditions. When time and transmission power are allocated at discrete slots and levels, respectively, a dynamic programming approach is proposed to find the optimal value.

Techniques like dynamic voltage scaling (DVS) and modulation scaling provide the ability to perform an energy-delay tradeoff in the computation and communications subsystems. Slowdown based on performance requirements has shown to be energy efficient while meeting timing requirements. We address the problem of computing slowdown factors for a non-preemptive task system based on the Earliest Deadline First scheduling policy. We present a stack based slowdown algorithm based on the optimal feasibility test for non-preemptive systems. We also propose a dynamic slack reclamation policy to further enhance the energy savings. The algorithms are practically fast, and have the same time complexity as the feasibility test for non-preemptive systems. The simulation results for our test examples show on an average 15% energy gains using static slowdown factors and 20% gains with dynamic slowdown over the known slowdown techniques.

Abstract—For some realtime systems, it is possible to tradeoff precision for timeliness. For such systems, typically considered under the imprecise computation model, a function assigns reward to the application depending on the amount of computation allotted to it. Also, these systems often have stringent energy constraints since many such applications run on battery powered devices. We address in this paper, the problem of maximizing rewards for imprecise computation systems that have energy constraints, more specifically, the problem of determining the voltage at which each task runs as well as the number of optional cycles such that the total reward is maximal while time and energy constraints are satisfied. We propose a quasi-static approach that is able to exploit, with low online overhead, the dynamic slack that arises from variations in the actual number of task execution cycles. In our quasi-static approach, the problem is solved in two steps: first, at design-time, a set of voltage/optional-cycles assignments are computed and stored (offline phase); second, the selection among the precomputed assignments is left for runtime, based on actual completion times and consumed energy (online phase). The advantages of the approach are demonstrated through numerous experiments with both synthetic examples and a real life application. Index Terms—Energy management, imprecise computation, quasi-static, realtime. I.

Slowdown factors determine the extent of slowdown that a computing system can experience based on functional and performance requirements. Dynamic voltage scaling (DVS) of a processor based on slowdown factors can lead to considerable energy savings. This paper addresses the problem of DVS in the presence of task synchronization. Tasks synchronize to enforce mutually exclusive access to the shared resources and can be blocked by lower priority tasks. Task slowdown factors that guarantee meeting all task deadlines are computed. Both static and dynamic priority scheduling viz. rate monotonic (RM) scheduling and earliest deadline first (EDF) scheduling, respectively, are studied.

Abstract. Reward-based scheduling has been investigated for flexible applications in which an approximate but timely result is acceptable. Meanwhile, significant research efforts have been made on voltage scheduling which exploits the tradeoff between the processor speed and the energy consumption. In this paper, we address the combined scheduling problem of maximizing the total reward of hard real-time systems with a given energy budget for the general task model. We present an optimal off-line algorithm and an efficient on-line algorithm for jobs with their own release-times/deadlines. Our work is the first significant result for the general task model. 1

In some real-time applications, it is desirable to trade off precision for timeliness. For such systems, considered typically under the Imprecise Computation model, a function assigns reward to the application depending on the amount of computation allotted to it. Also, many such applications run on battery-powered devices where the energy consumption is of utmost importance. We address in this paper the problem of energy minimization for Imprecise-Computation systems that have reward and time constraints. We propose a Quasi-Static (QS) approach that exploits, with low on-line overhead, the dynamic slack that arises from variations in the actual number of execution cycles: first, at design-time, a set of solutions are computed and stored (off-line phase); second, the selection among the precomputed assignments is left for run-time, based on actual values of time and reward (on-line phase). 1

There exist real-time systems for which it is possible to trade off precision for timeliness. In these cases, a function assigns reward to the application depending on the amount of computation allotted to it. At the same time, many such applications run on battery-powered devices with stringent energy constraints. This paper addresses the problem of maximizing rewards subject to time and energy constraints. We propose a quasi-static approach where the problem is solved in two steps: first, at design-time, a number of solutions are computed and stored (off-line phase); second, one of the precomputed solutions is selected at run-time based on actual values of time and energy (on-line phase). Thus our approach is able to exploit, with low on-line overhead, the dynamic slack caused by tasks executing less number of cycles than in the worst case. We conduct numerous experiments in order to show the advantages of our approach.