Daniel Mossé

Dynamic and aggressive scheduling techniques for power-aware real-time systems

In this paper we address power-aware scheduling of periodic hard real-time tasks using dynamic voltage scaling. Our solution includes three parts: (a) a static (off-line) solution to compute the optimal speed, assuming worst-case workload for each arrival, (b) an on-line speed reduction mechanism to reclaim energy by adapting to the actual workload, and (c) an online, adaptive and speculative speed adjustment mechanism to anticipate early completions of future executions by using the average-case workload information. All these solutions still guarantee that all deadlines are met. Our simulation results show that the reclaiming algorithm saves a striking 50% of the energy, over the static algorithm. Further our speculative techniques allow for an additional approximately 20% savings over the reclaiming algorithm. In this study, we also establish that solving an instance of the static power-aware scheduling problem is equivalent to solving an instance of the reward-based scheduling problem [1, 4] with concave reward functions.

Power-aware scheduling for periodic real-time tasks

We address power-aware scheduling of periodic tasks to reduce CPU energy consumption in hard real-time systems through dynamic voltage scaling. Our intertask voltage scheduling solution includes three components: 1) a static (offline) solution to compute the optimal speed, assuming worst-case workload for each arrival, 2) an online speed reduction mechanism to reclaim energy by adapting to the actual workload, and 3) an online, adaptive and speculative speed adjustment mechanism to anticipate early completions of future executions by using the average-case workload information. All these solutions still guarantee that all deadlines are met. Our simulation results show that our reclaiming algorithm alone outperforms other recently proposed intertask voltage scheduling schemes. Our speculative techniques are shown to provide additional gains, approaching the theoretical lower-bound by a margin of 10 percent.In this paper, we address power-aware scheduling of periodic tasks to reduce CPU energy consumption in hard real-time systems through dynamic voltage scaling. Our intertask voltage scheduling solut...

The effects of energy management on reliability in real-time embedded systems

The slack time in real-time systems can be used by recovery schemes to increase system reliability as well as by frequency and voltage scaling techniques to save energy. Moreover, the rate of transient faults (i.e., soft errors caused, for example, by cosmic ray radiations) also depends on system operating frequency and supply voltage. Thus, there is an interesting trade-off between system reliability and energy consumption. This work first investigates the effects of frequency and voltage scaling on the fault rate and proposes two fault rate models based on previously published data. Then, the effects of energy management on reliability are studied. Our analysis results show that, energy management through frequency and voltage scaling could dramatically reduce system reliability, and ignoring the effects of energy management on the fault rate is too optimistic and may lead to unsatisfied system reliability.

Determining optimal processor speeds for periodic real-time tasks with different power characteristics

In this paper, we provide an efficient solution for periodic real-time tasks with (potentially) different power consumption characteristics. We show that a task T/sub i/ can run at a constant speed S/sub i/ at every instance without hurting optimality. We sketch an O(n/sup 2/ log n) algorithm to compute the optimal S/sub i/ values. We also prove that the EDF (Earliest Deadline First) scheduling policy can be used to obtain a feasible schedule with these optimal speed values.

Energy aware scheduling for distributed real-time systems

Power management has become popular in mobile computing as well as in server farms. Although a lot of work has been done to manage the energy consumption on uniprocessor real-time systems, there is less work done on their multicomputer counterparts. For a set of real-time tasks with precedence constraints executing on a distributed system, we propose new static and dynamic power management schemes. Assuming a given static schedule generated from any list scheduling heuristic algorithm, our static power management scheme uses the static slack (if any) based on the degree of parallelism in the schedule. To consider the run-time behavior of tasks, an on-line dynamic power management technique is proposed to further explore the idle periods of processors. By comparing our static technique with the simple static power management, where the static slack is distributed to the schedule proportionally, we find that our static scheme can save an average of 10% more energy. When combined with dynamic schemes, our schemes significantly improve energy savings.

Fault-tolerance through scheduling of aperiodic tasks in hard real-time multiprocessor systems

Real time systems are being increasingly used in several applications which are time critical in nature. Fault tolerance is an important requirement of such systems, due to the catastrophic consequences of not tolerating faults. We study a scheme that provides fault tolerance through scheduling in real time multiprocessor systems. We schedule multiple copies of dynamic, aperiodic, nonpreemptive tasks in the system, and use two techniques that we call deallocation and overloading to achieve high acceptance ratio (percentage of arriving tasks scheduled by the system). The paper compares the performance of our scheme with that of other fault tolerant scheduling schemes, and determines how much each of deallocation and overloading affects the acceptance ratio of tasks. The paper also provides a technique that can help real time system designers determine the number of processors required to provide fault tolerance in dynamic systems. Lastly, a formal model is developed for the analysis of systems with uniform tasks.

Increasing PCM main memory lifetime

The introduction of Phase-Change Memory (PCM) as a main memory technology has great potential to achieve a large energy reduction. PCM has desirable energy and scalability properties, but its use for main memory also poses challenges such as limited write endurance with at most 107 writes per bit cell before failure. This paper describes techniques to enhance the lifetime of PCM when used for main memory. Our techniques are (a) writeback minimization with new cache replacement policies, (b) avoidance of unnecessary writes, which write only the bit cells that are actually changed, and (c) endurance management with a novel PCM-aware swap algorithm for wear-leveling. A failure detection algorithm is also incorporated to improve the reliability of PCM. With these approaches, the lifetime of a PCM main memory is increased from just a few days to over 8 years.

The interplay of power management and fault recovery in real-time systems

We describe how to exploit the scheduling slack in a real-time system to reduce energy consumption and achieve fault tolerance at the same time. During failure-free operation, a task takes checkpoints to enable recovery from failure. Additionally, the system exploits the slack to conserve energy by reducing the processor speed. If a task fails, it will restart from a saved checkpoint and execute at maximum speed to guarantee that the deadlines are met. We show that the number of checkpoints and their placements interact in subtle ways with the power management policy. We study two checkpoint placement policies for aperiodic tasks and analytically derive the optimal number of checkpoints to conserve energy under each. This optimal number allows the CPU speed to be slowed down to the level that yields minimum energy consumption, while still guaranteeing recoverability of tasks under each checkpointing policy. The results show that traditional periodic checkpointing is not the best policy for the combined purpose of conserving energy and guaranteeing recovery. Instead, better energy savings are possible through a nonuniform distribution of checkpoints that takes into account the energy consumption and reliability factors. Depending on the amount of slack and the checkpointing overhead, energy can be reduced by up to 68 percent under nonuniform checkpointing. We also demonstrate the applicability of these checkpoint placement policies to periodic tasks.

Optimal reward-based scheduling for periodic real-time tasks

Reward-based scheduling refers to the problem in which there is a reward associated with the execution of a task. In our framework, each real-time task comprises a mandatory and an optional part. The mandatory part must complete before the tasks deadline, while a nondecreasing reward function is associated with the execution of the optional part, which can be interrupted at any time. Imprecise computation and Increased-Reward-with-Increased-Service models fall within the scope of this-framework. In this paper, we address the reward-based scheduling problem for periodic tasks. An optimal schedule is one where mandatory-parts complete in a timely manner and the weighted average reward is maximized. For linear and concave reward functions, which are most common, we 1) show the existence of an optimal schedule where the optional service time of a task is constant at every instance and 2) show how to efficiently compute this service time. We also prove the optimality of Rate Monotonic Scheduling (with harmonic periods), Earliest Deadline First, and Least Laxity First policies for the case of uniprocessors when used with the optimal service times we computed. Moreover, we extend our result by showing that any policy which can fully utilize all the processors is also optimal for the multiprocessor periodic reward-based scheduling. To show-that our optimal solution is pushing the limits of reward-based scheduling, we further prove that, when the reward functions are convex, the problem becomes NP-Hard. Our static optimal solution, besides providing considerable reward improvements over the previous suboptimal strategies, also has a major practical benefit. Run-time overhead is eliminated and existing scheduling disciplines may be used without modification with the computed optimal service times.

Providing a bidirectional abstraction for unidirectional ad hoc networks

Several routing protocols for mobile ad hoc networks work efficiently only in bidirectional networks. Unidirectional links may exist in a real network due to variations in transmission power of different nodes, noise or other signal propagation phenomena, and heterogeneity in the transmission hardware of nodes in the network. We introduce a sub-layer called sub routing layer, SRL, between the network and the MAC layer to provide a bidirectional abstraction of the unidirectional network to the routing protocols. We present a scalable and efficient way to provide this abstraction by finding and maintaining multi-hop reverse routes to each unidirectional link. We simulate SRL and a modified version of AODV (ad hoc on demand distance vector) that uses SRL to route packets in a unidirectional network. We observed that with SRL, the packet delivery of AODV in unidirectional networks increases substantially. Further our simulations indicate that reverse routes are often only a few hops long and hence the overhead of using SRL is very low.