Maryline Chetto

A real-time scheduling framework for embedded systems with environmental energy harvesting

Real-time scheduling refers to the problem in which there is a deadline associated with the execution of a task. In this paper, we address the scheduling problem for a uniprocessor platform that is powered by a renewable energy storage unit and uses a recharging system such as photovoltaic cells. First, we describe our model where two constraints need to be studied: energy and deadlines. Since executing tasks require a certain amount of energy, classical task scheduling like earliest deadline is no longer convenient. We present an on-line scheduling scheme, called earliest deadline with energy guarantee (EDeg), that jointly accounts for characteristics of the energy source, capacity of the energy storage as well as energy consumption of the tasks, and time. In order to demonstrate the benefits of our algorithm, we evaluate it by means of simulation. We show that EDeg outperforms energy non-clairvoyant algorithms in terms of both deadline miss rate and size of the energy storage unit.

Optimal Scheduling for Real-Time Jobs in Energy Harvesting Computing Systems

In this paper, we study a scheduling problem, in which every job is associated with a release time, deadline, required computation time, and required energy. We focus on an important special case where the jobs execute on a uniprocessor system that is supplied by a renewable energy source and use a rechargeable storage unit with limited capacity. Earliest deadline first (EDF) is a class one online algorithm in the classical real-time scheduling theory where energy constraints are not considered. We propose a semi-online EDF-based scheduling algorithm theoretically optimal (i.e., processing and energy costs neglected). This algorithm relies on the notions of energy demand and slack energy, which are different from the well known notions of processor demand and slack time. We provide an exact feasibility test. There are no restrictions on this new scheduler: each job can be one instance of a periodic, aperiodic, or sporadic task with deadline.

A Note on EDF Schedulingfor Real-Time Energy Harvesting Systems

Energy harvesting is the capture of ambient energy, its conversion into a usable form, and its storage for immediate or future use. Interest in energy harvesting has increased over the last decade because of its environmental friendliness and its ability to power devices without electric wires. This term has been frequently applied in recent years in the context of small autonomous embedded devices such as wireless sensor nodes. In this paper, we address the scheduling problem for a single processor device that executes preemptable time critical tasks. Each one has a certain energy requirement and arrives at an unpredictable time. We ask the question whether the traditional task scheduling algorithm earliest deadline first (EDF) is convenient for energy harvesting environments. The paper shows that EDF has a zero competitive factor but nevertheless is optimal for online non-idling settings.

Performance Evaluation of Real-Time Scheduling Heuristics for Energy Harvesting Systems

Energy constrained systems can increase their usable lifetimes by extracting energy from their environment. This is known as energy harvesting. This paper investigates scheduling issues in uni-processor real time embedded systems using regenerative energy. Task scheduling should account for the properties of the regenerative energy source which fluctuates, capacity of the energy storage as well as deadlines of the time critical tasks that characterize most of real time embedded systems. In this context, designing efficient scheduling strategies is significantly more complex compared to conventional real-time scheduling. In this paper we compare several scheduling heuristics with the optimal algorithm known as LSA (Lazy Scheduling Algorithm). We report results of an experiment study in terms of percentage of deadlines satisfied.

Dynamic scheduling of periodic skippable tasks in an overloaded real-time system

The need for supporting dynamic real-time environments where changes in workloads may occur requires a scheduling framework that explicitly addresses overload conditions, allows the system to achieve graceful degradation and supports a mechanism capable of determining the load to be shed from the system to handle the overload. In applications ranging from video reception to air-craft control, tasks enter periodically and have response time constraints, but missing a deadline is acceptable, provided most deadlines are met. Such tasks are said to be occasionally skippable and have an assigned skip parameter. We look at the problem of uniprocessor scheduling of skippable periodic tasks which consists in maximizing the robustness of the system defined as the global completion ratio. In this paper, we propose a novel scheduling Skip-over algorithm, called RLP/T, a variant of Earliest-Deadline First which adjusts the system workload such that tasks adhere to their timing and skip constraints and guarantees the best robustness.

Dynamic Adjustment of the Chaos-Based Security in Real-Time Energy Harvesting Sensors

Wireless Sensor Networks (WSNs) are a growing field of research since they are used in many applications. Nevertheless, they are subject to many requirements such as real-time constraints, energy limitations, and security requirements for the communications. Energy Harvesting is a new paradigm in WSNs: sensor nodes are powered by energy harvested from the ambient, rather than by non-rechargeable batteries which permits a potentially perpetual operation. However, energy harvesting poses new challenges in the design of WSNs, in that energy availability fluctuates over the time. In this paper, we investigate the following fundamental question: how should the harvested energy be managed to guarantee data security in all circumstances? Our contributions in this paper are twofold: First, we propose the Deadline Mechanism to dynamically cope with energy shortage and guarantee the highest possible quality of protection by the use of different encryption algorithms. Second, we design a new chaos based cryptosystem suitable for WSNs. Indeed, the proposed encryption/decryption scheme is robust against all known attacks. Experiments show that it is at least 7 times faster than the AES algorithm and also, faster than many chaos based cryptosystems of the literature. Our objective is to identify low-complexity policies that achieve close-to-optimal performance, in terms of maximizing the average long-term importance of the reported data.

A Nonclairvoyant Real-Time Scheduler for Ambient Energy Harvesting Sensors

Ambient energy harvesting also known as energy scavenging is the process where energy is obtained from the environment, converted, and stored to power small devices such as wireless sensors. We present a variant of EDF scheduling algorithm called EH-EDF (Energy Harvesting-Earliest Deadline First). Decisions are taken at run-time without having prior knowledge about the future energy production and task characteristics. We gauge the performance of EH-EDF by means of simulations in order to show its benefits. We evaluate and compare several variants of EH-EDF in terms of percentage of feasible task sets. Metrics such as average length of the idle times are also considered. Simulations tend to demonstrate that no online scheduler can reach optimality in a real-time energy harvesting environment.

Clairvoyance and online scheduling in real-time energy harvesting systems

Real-time energy harvesting systems are designed using a microprocessor, a rechargeable energy storage unit and an energy harvester. The theoretical analysis shows that an optimal solution to the underlying online scheduling problem requires time lookahead which can be incompatible with the common stochastic nature of ambient energy.

Dynamic Scheduling of Skippable Periodic Tasks in Weakly-Hard Real-Time Systems

This paper deals with dynamic scheduling in real-time systems that have quality of service requirements. We assume that tasks are periodic and may miss their deadlines, occasionally, as defined by the so-called Skip-Over model. In this paper, we present a dynamic scheduling algorithm, called RLP (Red as Late as possible), a variant of earliest deadline to make slack stealing and get better performance in terms of ratio of periodic task instances which complete before their deadline. Simulation results show that RLP outperforms the two conventional skip-over algorithms, namely RTO (Red Tasks Only) and BWP (Blue When Possible), introduced about ten years ago. Finally, we present the integration of these QoS scheduling services into CLEOPATRE, a free open-source library which offers selectable real-time facilities on shelves

Task Partitioning Strategies for Multicore Real-Time Energy Harvesting Systems

In this paper, we propose task partitioning heuristics for multicore real-time energy harvesting systems. Both timing constraints and energy requirements are considered. Our objective is to determine a partitioning that guarantees absence of both energy starvation and deadline missing under Earliest Deadline First (EDF) scheduling. First, we describe the system model and formalize the assignment problem in real-time energy harvesting systems. Then, we describe and analyze the performance of partitioning heuristics for identical processors. We explore how the task sorting criteria and the energy constraints can favor the heuristics feasibility performance. Moreover, we estimate through simulation a sufficient energy reservoir capacity that guarantees the best performance.