A Survey of Fault-Tolerance Techniques for Embedded Systems from the Perspective of Power, Energy, and Thermal Issues

The relentless technology scaling has provided a significant increase in processor performance, but on the other hand, it has led to adverse impacts on system reliability. In particular, technology scaling increases the processor susceptibility to radiation-induced transient faults. Moreover, technology scaling with the discontinuation of Dennard scaling increases the power densities, thereby temperatures, on the chip. High temperature, in turn, accelerates transistor aging mechanisms, which may ultimately lead to permanent faults on the chip. To assure a reliable system operation, despite these potential reliability concerns, fault-tolerance techniques have emerged. Specifically, fault-tolerance techniques employ some kind of redundancies to satisfy specific reliability requirements. However, the integration of fault-tolerance techniques into real-time embedded systems complicates preserving timing constraints. As a remedy, many task mapping/scheduling policies have been proposed to consider the integration of fault-tolerance techniques and enforce both timing and reliability guarantees for real-time embedded systems. More advanced techniques aim additionally at minimizing power and energy while at the same time satisfying timing and reliability constraints. Recently, some scheduling techniques have started to tackle a new challenge, which is the temperature increase induced by employing fault-tolerance techniques. These emerging techniques aim at satisfying temperature constraints besides timing and reliability constraints. This paper provides an in-depth survey of the emerging research efforts that exploit fault-tolerance techniques while considering timing, power/energy, and temperature from the real-time embedded systems’ design perspective. In particular, the task mapping/scheduling policies for fault-tolerance real-time embedded systems are reviewed and classified according to their considered goals and constraints. Moreover, the employed fault-tolerance techniques, application models, and hardware models are considered as additional dimensions of the presented classification. Lastly, this survey gives deep insights into the main achievements and shortcomings of the existing approaches and highlights the most promising ones

Energy-efficient checkpointing in high-throughput cycle-stealing distributed systems

Checkpointing is a fault-tolerance mechanism commonly used in High Throughput Computing (HTC) environments to allow the execution of long-running computational tasks on compute resources subject to hardware or software failures as well as interruptions from resource owners and more important tasks. Until recently many researchers have focused on the performance gains achieved through checkpointing, but now with growing scrutiny of the energy consumption of IT infrastructures it is increasingly important to understand the energy impact of checkpointing within an HTC environment. In this paper we demonstrate through trace-driven simulation of real-world datasets that existing checkpointing strategies are inadequate at maintaining an acceptable level of energy consumption whilst maintaing the performance gains expected with checkpointing. Furthermore, we identify factors important in deciding whether to exploit checkpointing within an HTC environment, and propose novel strategies to curtail the energy consumption of checkpointing approaches whist maintaining the performance benefits

Energy harvesting earliest deadline first scheduling algorithm for increasing lifetime of real time systems

In this paper, a new approach for energy minimization in energy harvesting real time systems has been investigated. Lifetime of a real time systems is depend upon its battery life.  Energy is a parameter by which the lifetime of system can be enhanced.  To work continuously and successively, energy harvesting is used as a regular source of energy. EDF (Earliest Deadline First) is a traditional real time tasks scheduling algorithm and DVS (Dynamic Voltage Scaling) is used for reducing energy consumption. In this paper, we propose an Energy Harvesting Earliest Deadline First (EH-EDF) scheduling algorithm for increasing lifetime of real time systems using DVS for reducing energy consumption and EDF for tasks scheduling with energy harvesting as regular energy supply. Our experimental results show that the proposed approach perform better to reduce energy consumption and increases the system lifetime as compared with existing approaches.

Energy-aware Fault-tolerant Scheduling for Hard Real-time Systems

Over the past several decades, we have experienced tremendous growth of real-time systems in both scale and complexity. This progress is made possible largely due to advancements in semiconductor technology that have enabled the continuous scaling and massive integration of transistors on a single chip. In the meantime, however, the relentless transistor scaling and integration have dramatically increased the power consumption and degraded the system reliability substantially. Traditional real-time scheduling techniques with the sole emphasis on guaranteeing timing constraints have become insufficient. In this research, we studied the problem of how to develop advanced scheduling methods on hard real-time systems that are subject to multiple design constraints, in particular, timing, energy consumption, and reliability constraints. To this end, we first investigated the energy minimization problem with fault-tolerance requirements for dynamic-priority based hard real-time tasks on a single-core processor. Three scheduling algorithms have been developed to judiciously make tradeoffs between fault tolerance and energy reduction since both design objectives usually conflict with each other. We then shifted our research focus from single-core platforms to multi-core platforms as the latter are becoming mainstream. Specifically, we launched our research in fault-tolerant multi-core scheduling for fixed-priority tasks as fixed-priority scheduling is one of the most commonly used schemes in the industry today. For such systems, we developed several checkpointing-based partitioning strategies with the joint consideration of fault tolerance and energy minimization. At last, we exploited the implicit relations between real-time tasks in order to judiciously make partitioning decisions with the aim of improving system schedulability. According to the simulation results, our design strategies have been shown to be very promising for emerging systems and applications where timeliness, fault-tolerance, and energy reduction need to be simultaneously addressed

Energy and Reliability Management in Parallel Real-Time Systems

Historically, slack time in real-time systems has been used as temporal redundancy by rollback recovery schemes to increase system reliability in the presence of faults. However, with advancedtechnologies, slack time can also be used by energy management schemes to save energy. For reliable real-time systems where higher levels of reliability are as important as lower levels of energy consumption, centralized management of slack time is desired.For frame-based parallel real-time applications, energy management schemes are first explored. Although the simple static power management that evenly allocates static slack over a schedule isoptimal for uni-processor systems, it is not optimal for parallel systems due to different levels of parallelism in a schedule. Taking parallelism variations into consideration, a parallel static power management scheme is proposed. When dynamic slack is considered,assuming global scheduling strategies, slack shifting and sharing schemes as well as speculation schemes are proposed for moreenergy savings.For simultaneous management of power and reliability, checkpointing techniques are first deployed to efficiently use slack time and theoptimal numbers of checkpoints needed to minimize energy consumption or to maximize system reliability are explored. Then, an energyefficient optimistic modular redundancy scheme is addressed. Finally, a framework that encompasses energy and reliability management isproposed for obtaining optimal redundant configurations. While exploring the trade-off between energy and reliability, the effects ofvoltage scaling on fault rates are considered

QoS-aware predictive workflow scheduling

This research places the basis of QoS-aware predictive workflow scheduling. This research novel contributions will open up prospects for future research in handling complex big workflow applications with high uncertainty and dynamism. The results from the proposed workflow scheduling algorithm shows significant improvement in terms of the performance and reliability of the workflow applications

Dependable Embedded Systems

This Open Access book introduces readers to many new techniques for enhancing and optimizing reliability in embedded systems, which have emerged particularly within the last five years. This book introduces the most prominent reliability concerns from today’s points of view and roughly recapitulates the progress in the community so far. Unlike other books that focus on a single abstraction level such circuit level or system level alone, the focus of this book is to deal with the different reliability challenges across different levels starting from the physical level all the way to the system level (cross-layer approaches). The book aims at demonstrating how new hardware/software co-design solution can be proposed to ef-fectively mitigate reliability degradation such as transistor aging, processor variation, temperature effects, soft errors, etc. Provides readers with latest insights into novel, cross-layer methods and models with respect to dependability of embedded systems; Describes cross-layer approaches that can leverage reliability through techniques that are pro-actively designed with respect to techniques at other layers; Explains run-time adaptation and concepts/means of self-organization, in order to achieve error resiliency in complex, future many core systems

Operating policies for energy efficient large scale computing

PhD ThesisEnergy costs now dominate IT infrastructure total cost of ownership, with datacentre operators predicted to spend more on energy than hardware infrastructure in the next five years. With Western European datacentre power consumption estimated at 56 TWh/year in 2007 and projected to double by 2020, improvements in energy efficiency of IT operations is imperative. The issue is further compounded by social and political factors and strict environmental legislation governing organisations. One such example of large IT systems includes high-throughput cycle stealing distributed systems such as HTCondor and BOINC, which allow organisations to leverage spare capacity on existing infrastructure to undertake valuable computation. As a consequence of increased scrutiny of the energy impact of these systems, aggressive power management policies are often employed to reduce the energy impact of institutional clusters, but in doing so these policies severely restrict the computational resources available for high-throughput systems. These policies are often configured to quickly transition servers and end-user cluster machines into low power states after only short idle periods, further compounding the issue of reliability. In this thesis, we evaluate operating policies for energy efficiency in large-scale computing environments by means of trace-driven discrete event simulation, leveraging real-world workload traces collected within Newcastle University. The major contributions of this thesis are as follows: i) Evaluation of novel energy efficient management policies for a decentralised peer-to-peer (P2P) BitTorrent environment. ii) Introduce a novel simulation environment for the evaluation of energy efficiency of large scale high-throughput computing systems, and propose a generalisable model of energy consumption in high-throughput computing systems. iii iii) Proposal and evaluation of resource allocation strategies for energy consumption in high-throughput computing systems for a real workload. iv) Proposal and evaluation for a realworkload ofmechanisms to reduce wasted task execution within high-throughput computing systems to reduce energy consumption. v) Evaluation of the impact of fault tolerance mechanisms on energy consumption

Adaptive and Power-aware Fault Tolerance for Future Extreme-scale Computing

Two major trends in large-scale computing are the rapid growth in HPC with in particular an international exascale initiative, and the dramatic expansion of Cloud infrastructures accompanied by the Big Data passion. To satisfy the continuous demands for increasing computing capacity, future extreme-scale systems will embrace a multi-fold increase in the number of computing, storage, and communication components, in order to support an unprecedented level of parallelism. Despite the capacity and economies benefits, making the upward transformation to extreme-scale poses numerous scientific and technological challenges, two of which are the power consumption and fault tolerance. With the increase in system scale, failure would become a norm rather than an exception, driving the system to significantly lower efficiency with unforeseen power consumption. This thesis aims at simultaneously addressing the above two challenges by introducing a novel fault-tolerant computational model, referred to as \textit{Leaping Shadows}. Based on Shadow Replication, Leaping Shadows associates with each main process a suite of coordinated shadow processes, which execute in parallel but at differential rates, to deal with failures and meet the QoS requirements of the underlying application under strict power/energy constraints. In failure-prone extreme-scale computing environments, this new model addresses the limitations of the basic Shadow Replication model, and achieves adaptive and power-aware fault tolerance that is more time and energy efficient than existing techniques. In this thesis, we first present an analytical model based optimization framework that demonstrates Shadow Replication's adaptivity and flexibility in achieving multi-dimensional QoS requirements. Then, we introduce Leaping Shadows as a novel power-aware fault tolerance model, which tolerates multiple types of failures, guarantees forward progress, and maintains a consistent level of resilience. Lastly, the details of a Leaping Shadows implementation in MPI is discussed, along with extensive performance evaluation that includes comparison to checkpoint/restart. Collectively, these efforts advocate an adaptive and power-aware fault tolerance alternative for future extreme-scale computing