MORA: an Energy-Aware Slack Reclamation Scheme for Scheduling Sporadic Real-Time Tasks upon Multiprocessor Platforms

In this paper, we address the global and preemptive energy-aware scheduling problem of sporadic constrained-deadline tasks on DVFS-identical multiprocessor platforms. We propose an online slack reclamation scheme which profits from the discrepancy between the worst- and actual-case execution time of the tasks by slowing down the speed of the processors in order to save energy. Our algorithm called MORA takes into account the application-specific consumption profile of the tasks. We demonstrate that MORA does not jeopardize the system schedulability and we show by performing simulations that it can save up to 32% of energy (in average) compared to execution without using any energy-aware algorithm.Comment: 11 page

Scheduling of real time embedded systems for resource and energy minimization by voltage scaling

The aspects of real-time embedded computing are explored with the focus on novel real-time scheduling policies, which would be appropriate for low-power devices. To consider real-time deadlines with pre-emptive scheduling policies will require the investigation of intelligent scheduling heuristics. These aspects for various other RTES models like Multiple processor system, Dynamic Voltage Scaling and Dynamic scheduling are the focus of this thesis. Deadline based scheduling of task graphs representative of real time systems is performed on a multiprocessor system; A set of aperiodic, dependent tasks in the form of a task graph are taken as the input and all the required task parameters are calculated. All the tasks are then partitioned into two or more clusters allowing them to be run at different voltages. Each cluster, thus voltage scaled results in the overall minimization of the power utilized by the system. With the mapping of each task to a particular voltage done, the tasks are scheduled on a multiprocessor system consisting of processors that can run at different voltages and frequencies, in such a way that all the timing constraints are satisfied

TimeTrader: Exploiting Latency Tail to Save Datacenter Energy for On-line Data-Intensive Applications

Datacenters running on-line, data-intensive applications (OLDIs) consume significant amounts of energy. However, reducing their energy is challenging due to their tight response time requirements. A key aspect of OLDIs is that each user query goes to all or many of the nodes in the cluster, so that the overall time budget is dictated by the tail of the replies' latency distribution; replies see latency variations both in the network and compute. Previous work proposes to achieve load-proportional energy by slowing down the computation at lower datacenter loads based directly on response times (i.e., at lower loads, the proposal exploits the average slack in the time budget provisioned for the peak load). In contrast, we propose TimeTrader to reduce energy by exploiting the latency slack in the sub- critical replies which arrive before the deadline (e.g., 80% of replies are 3-4x faster than the tail). This slack is present at all loads and subsumes the previous work's load-related slack. While the previous work shifts the leaves' response time distribution to consume the slack at lower loads, TimeTrader reshapes the distribution at all loads by slowing down individual sub-critical nodes without increasing missed deadlines. TimeTrader exploits slack in both the network and compute budgets. Further, TimeTrader leverages Earliest Deadline First scheduling to largely decouple critical requests from the queuing delays of sub- critical requests which can then be slowed down without hurting critical requests. A combination of real-system measurements and at-scale simulations shows that without adding to missed deadlines, TimeTrader saves 15-19% and 41-49% energy at 90% and 30% loading, respectively, in a datacenter with 512 nodes, whereas previous work saves 0% and 31-37%.Comment: 13 page

Energy-Centric Scheduling for Real-Time Systems

Energy consumption is today an important design issue for all kinds of digital systems, and essential for the battery operated ones. An important fraction of this energy is dissipated on the processors running the application software. To reduce this energy consumption, one may, for instance, lower the processor clock frequency and supply voltage. This, however, might lead to a performance degradation of the whole system. In real-time systems, the crucial issue is timing, which is directly dependent on the system speed. Real-time scheduling and energy efficiency are therefore tightly connected issues, being addressed together in this work. Several scheduling approaches for low energy are described in the thesis, most targeting variable speed processor architectures. At task level, a novel speed scheduling algorithm for tasks with probabilistic execution pattern is introduced and compared to an already existing compile-time approach. For task graphs, a list-scheduling based algorithm with an energy-sensitive priority is proposed. For task sets, off-line methods for computing the task maximum required speeds are described, both for rate-monotonic and earliest deadline first scheduling. Also, a run-time speed optimization policy based on slack re-distribution is proposed for rate-monotonic scheduling. Next, an energy-efficient extension of the earliest deadline first priority assignment policy is proposed, aimed at tasks with probabilistic execution time. Finally, scheduling is examined in conjunction with assignment of tasks to processors, as parts of various low energy design flows. For some of the algorithms given in the thesis, energy measurements were carried out on a real hardware platform containing a variable speed processor. The results confirm the validity of the initial assumptions and models used throughout the thesis. These experiments also show the efficiency of the newly introduced scheduling methods

A Practical Framework to Study Low-Power Scheduling Algorithms on Real-Time and Embedded Systems

With the advanced technology used to design VLSI (Very Large Scale Integration) circuits, low-power and energy-efficiency have played important roles for hardware and software implementation. Real-time scheduling is one of the fields that has attracted extensive attention to design low-power, embedded/real-time systems. The dynamic voltage scaling (DVS) and CPU shut-down are the two most popular techniques used to design the algorithms. In this paper, we firstly review the fundamental advances in the research of energy-efficient, real-time scheduling. Then, a unified framework with a real Intel PXA255 Xscale processor, namely real-energy, is designed, which can be used to measure the real performance of the algorithms. We conduct a case study to evaluate several classical algorithms by using the framework. The energy efficiency and the quantitative difference in their performance, as well as the practical issues found in the implementation of these algorithms are discussed. Our experiments show a gap between the theoretical and real results. Our framework not only gives researchers a tool to evaluate their system designs, but also helps them to bridge this gap in their future works

High Performance dynamic voltage/frequency scaling algorithm for real-time dynamic load management and code mobility

Modern cyber-physical systems assume a complex and dynamic interaction between the real world and the computing system in real-time. In this context, changes in the physical environment trigger changes in the computational load to execute. On the other hand, task migration services offered by networked control systems require also management of dynamic real-time computing load in nodes. In such systems it would be difficult, if not impossible, to analyse off-line all the possible combinations of processor loads. For this reason, it is worthwhile attempting to define new flexible architectures that enable computing systems to adapt to potential changes in the environment. We assume a system composed by three main components: the first one is responsible of the management of the requests arisen when new tasks require to be executed. This management component asks to the second component about the resources available to accept the new tasks. The second component performs a feasibility analysis to determine if the new tasks can be accepted coping with its real-time constraints. A new processor speed is also computed. A third component monitors the execution of tasks applying a fixed priority scheduling policy and additionally controlling the frequency of the processor. This paper focus on the second component providing a "correct" (a task never is accepted if it is not schedulable) and "near-exact" (a task is rarely rejected if it is schedulable) algorithm that can be applicable in practice because its low/medium and predictable computational cost. The algorithm analyses task admission in terms of processor frequency scaling. The paper presents the details of a novel algorithm to analyse tasks admission and processor frequency assignment. Additionally, we perform several simulations to evaluate the comparative performance of the proposed approach. This evaluation is made in terms of energy consumption, task rejection ratios, and real computing costs. The results of simulations show that from the cost, execution predictability, and task acceptance points of view, the proposed algorithm mostly outperforms other constant voltage scaling algorithms. © 2011 Elsevier Inc. All rights reserved.This work has been supported by the Spanish Government as part of the SIDIRELI project (DPI2008-06737-C02-02), COBAMI project (DPI2011-28507-C02-02) and by the Generalitat Valenciana (Project ACOMP-2010-038).Coronel Parada, JO.; Simó Ten, JE. (2012). High Performance dynamic voltage/frequency scaling algorithm for real-time dynamic load management and code mobility. Journal of Systems and Software. 85(4):906-919. https://doi.org/10.1016/j.jss.2011.11.284S90691985

Application analyses of ultra-low-energy processor

Abstract. Low energy consumption has become a critical design feature in modern systems. Internet of Things, wearables and other portable devices create increasing demand for low power design where device size is dictated by battery and low energy means longer battery life and smaller physical size. These are crucial features for wearables and especially implantable medical devices. There are several low power and energy efficient techniques which are applied at different abstraction levels of the system design. A technique usually utilizing software control and hardware features is DVFS (dynamic voltage and frequency scaling), a dynamic power management technique which decreases processor clock frequency and supply voltage. Reduction in energy consumption is achieved with the cost of reduced performance. One of the questions with DVFS is how the execution frequencies are defined. This thesis presents a method for frequency optimization for applications executed on a single core processor. Execution trace data is used to profile the application. FreeRTOS operating system is used although tracing can be implemented with any real-time operating system executing tasks as separate threads. Based on profiling and user-defined data, task execution frequencies are defined assuming that execution time scales linearly with the frequency. A near-threshold ARM Cortex M3 with integrated power management and phase-locked loop is used for measurements. The measurements show that energy savings can be achieved without affecting correct application execution. However, the reduction in energy consumption depends highly on the system used and the application execution profile. Iterative testing and frequency optimization are required to ensure adequate performance. For energy efficiency optimization, energy consumption needs to be considered in every phase of the design.Matalan energiankulutuksen prosessorin sovellusanalyysi. Tiivistelmä. Matala energiankulutus on keskeinen ominaisuus nykyisten järjestelmien suunnittelussa. Esineiden Internet ja puettava tietotekniikka luovat tarpeen yhä pienemmälle energiankulutukselle. Laitteen koko määräytyy akun koon mukana. Matala tehonkulutus tarkoittaa pidempää akunkestoa ja pienempää fyysista kokoa. Nämä ovat ratkaisevia ominaisuuksia, erityisesti implantoitaville lääkinnällisille laitteille. Energiatehokkuuteen ja matalaan energiankulutukseen tähtääviä menetelmiä voidaan soveltaa eri abstraktiotasoilla järjestelmän suunnittelussa. Dynaaminen jännitteen ja taajuuden skaalaus on menetelmä, millä pyritään alentamaan dynaamista tehonkulutusta säätelemällä käyttöjännitettä ja kellotaajuutta. Suorituskyvyn kustannuksella on mahdollista saavuttaa matalampi energiankulutus. Keskeinen kysymys on, miten käytettävät kellotaajuudet tulee määritellä. Tässä diplomityössä kehitetään menetelmä, jota voidaan käyttää optimaalisten kellotaajuuksien määrittämiseen. Suorituksen aikana kerättävää dataa käytetään ohjelman profilointiin ja optimointimallin luomiseen. Suoritusdatan kerääminen on kehitetty FreeRTOS-käyttöjärjestelmälle, mutta periaate on sovellettavissa käyttöjärjestelmille, joissa tehtävät suoritetaan erillisissä prosesseissa. Profilointidata hyödynnetään yhdessä käyttäjän syöttämän data kanssa kellotaajuuksien määrittämiseen olettaen, että suoritusaika skaalautuu lineaarisesti kellotaajuden kanssa. Suositustaajuudet määritetään jokaiselle prosessille erikseen. Mittauksissa käytettiin ARM Cortex M3 prosessoria integroidulla tehonhallinnalla ja vaihelukolla. Mittaustulokset osoittavat, että energiankulutusta voidaan pienentää vaikuttamatta sovelluksen virheettömään suoritukseen. Saavutettava hyöty tehonkulutuksessa on riippuvainen käytettävästä järjestelmästä ja sovelluksen suoritusprofiilista. Riittävä suorituskyky täytyy varmistaa iteratiivisella testaamisella ja kellotaajuuksien optimoinnilla. Tehonkulutus ja energiatehokkuus täytyy huomioida suunnitteluprosessin jokaisella osa-alueella, jotta parhaat tulokset saavutetaan

Experiences in Implementing an Energy-Driven Task Scheduler in RT-Linux

Dynamic voltage scaling (DVS) is being increasingly used for power management in embedded systems. Energy is a scarce resource in embedded real-time systems and energy consumption must be carefully balanced against realtime responsiveness. We describe our experiences in implementing an energy driven task scheduler in RT-Linux. We attempt to minimize the energy consumed by a taskset while guaranteeing that all task deadlines are met. Our algorithm, which we call LEDF, follows a greedy approach and schedules as many tasks as possible at a low CPU speed in a power-aware manner. We present simulation results on energy savings using LEDF, and we validate our approach using the RT-Linux testbed on the AMD Athlon 4 processor. Power measurements taken on the testbed closely match the power estimates obtained using simulation. Our results show that DVS results in significant energy savings for practical real-life task sets. We also show that when CPU speeds are restricted to only a few discrete values, this approach saves more energy than currently existing methods

Dynamic Energy Aware Task Scheduling using Run-Queue Peek

Scheduling dependent tasks is one of the most challenging problems in parallel and distributed systems. It is known to be computationally intractable in its general form as well as several restricted cases. An interesting application of scheduling is in the area of energy awareness for mobile battery operated devices where minimizing the energy utilized is the most important scheduling policy consideration. A number of heuristics have been developed for this consideration. In this paper, we study the scheduling problem for a particular battery model. In the proposed work, we show how to enhance a well know approach of accounting for the slack generated at runtime due to the difference between WCET (Worst Case Execution Time) and AET (Actual Execution Time). Our solution exploits the fact that even though some tasks become available based on the actual periodicity of a task they are not executed because the run queue is determined by the schedule generated in the offline phase I of the algorithm using the conservative EDF (Earliest Deadline First) algorithm. We peek at the task run-queue to find such tasks to eliminate wastage of the slack generated. Based on the outcome of the conducted experiments, the proposed algorithm outperformed or matched the performance of the 2-Phase dynamic task scheduling algorithm all the time