Reliability-aware Scheduling Research Articles

Online energy-efficient scheduling of DAG tasks on heterogeneous embedded platforms

Heterogeneous distributed computing systems are becoming more and more common to process practical applications in many embedded systems, where energy efficiency is very important, especially for battery-powered systems. This paper studies the problem of how to adaptively schedule dynamic applications on the fly on a heterogeneous embedded system such that the system’s energy consumption can be minimized, while meeting applications’ response time and reliability requirements simultaneously. We first study the problem of scheduling a set of static applications. This problem is formulated as an integer nonlinear programming problem by deriving the response time equation of directed acyclic graph (DAG) applications. A reliability-aware scheduling strategy is designed to solve this problem. On top of this strategy, we develop an algorithm that tries to use as few processors as possible to finish the computation because, in this way, the system’s static energy can be saved. We then extend this solution to handle the problem of scheduling dynamic applications. By developing a task migration algorithm and a schedule adjustment algorithm, the system can adaptively handle suspended and newly released applications on the fly. Extensive simulation experiments are conducted, and the results demonstrate the high efficiency of our proposed approaches against some classic heuristics and a state-of-the-art approach.

RASA: Reliability-Aware Scheduling Approach for FPGA-Based Resilient Embedded Systems in Extreme Environments

Field-programmable gate arrays (FPGAs) offer the flexibility of general-purpose processors along with the performance efficiency of dedicated hardware that essentially renders it as a platform of choice for modern-day robotic systems for achieving real-time performance. Such robotic systems when deployed in harsh environments often get plagued by faults due to extreme conditions. Consequently, the real-time applications running on FPGA become susceptible to errors which call for a reliability-aware task scheduling approach, the focus of this article. We attempt to address this challenge using a hybrid offline-online approach. Given a set of periodic real-time tasks that require to be executed, the offline component generates a feasible preemptive schedule with specific preemption points. At runtime, these preemption events are utilized for fault detection. Upon detecting any faulty execution at such distinct points, the reliability-aware scheduling approach, RASA, orchestrates the recovery mechanism to remediate the scenario without jeopardizing the predefined schedule. Effectiveness of the proposed strategy has been verified through simulation-based experiments and we observed that the RASA is able to achieve 72% of task acceptance rate even under 70% of system workloads with high fault occurrence rates.

Mitigating Lifetime-Energy-Makespan Issues in Reliability-Aware Workflow Scheduling for Big Data

The emergence of cloud computing in big data era has exerted a substantial impact on our daily lives. The conventional reliability-aware workflow scheduling (RWS) is capable of improving or maintaining system reliability by fault tolerance techniques such as replication and checkpointing based recovery. However, the fault tolerant techniques used in RWS would inevitably result in higher system energy consumption, longer execution time, and worse thermal profiles that would in turn lead to a decreased hardware lifespan. To mitigate the lifetime-energy-makespan issues of RWS in cloud computing systems for big data, we propose a novel methodology that decomposes the complicated studied problem. In this methodology, we provide three procedures to solve the energy consumption, execution makespan, and hardware lifespan issues in cloud systems executing real-time workflow applications. We implement numerous simulation experiments to validate the proposed methodology for RWS. Simulation results clearly show that the proposed RWS strategies outperform comparative approaches in reducing energy consumption, shortening execution makespan, and prolonging system lifespan while maintaining high reliability. The improvements on energy saving, reduction on makespan, and increase in lifespan can be up to 23.8%, 18.6%, and 69.2%, respectively. Results also show the potentiality of the proposed method to develop a distributed analysis system for big data that serves satellite signal processing, earthquake early warning, and so on.

Reliability-aware Scheduling and Routing for Messages in Time-sensitive Networking

Time-sensitive Networking (TSN) on Ethernet is a promising communication technology in the automotive and industrial automation industries due to its real-time and high-bandwidth communication capabilities. Time-triggered scheduling and static routing are often adopted in these areas due to high requirements on predictability for safety-critical applications. Deadline-constrained routing and scheduling in TSN have been studied extensively in past research. However, scheduling and routing with reliability requirements in the context of transient faults are not yet studied. In this work, we propose an Satisfiability Modulo Theory-based technique to perform scheduling and routing that takes both reliability constraints and end-to-end deadline constraints into consideration. Heuristics have been applied to improve the scalability of the solution. Extensive experiments have been conducted to demonstrate the efficiency of our proposed technique.

A reliability-aware scheduling algorithm for parallel task executing on cloud computing system

A reliability-aware scheduling algorithm for parallel task executing on cloud computing system

A reliability-aware scheduling algorithm for parallel task executing on cloud computing system

As cloud computing is established on the massive cheap server clusters, it causes compute nodes' software and hardware to go wrong. Different computing nodes and communications links have different failure rate. For the parallel task scheduling problem that cloud users have requirements for deadlines and executing reliability, we put forward to generate all possible execution schemes of a parallel task on a cloud computing system. All the execution schemes are constructed into an execution scheme graph (ESG), in which a path from the start point to end point corresponds to an execution scheme of a parallel task. Based on ESG, we propose the maximum reliability execution scheme solving algorithm MRES that searches the execution schemes which have maximum reliability cost while meeting the parallel task's deadline requirement. The experimental results show that MRES algorithm can effectively improve the executing success rate.

Reliability-Aware and Deadline-Constrained Mobile Service Composition Over Opportunistic Networks

An opportunistic link between two mobile devices or nodes can be constructed when they are within each other’s communication range. Typically, cyber–physical environments consist of a number of mobile devices that are potentially able to establish opportunistic contacts and serve mobile applications in a cost-effective way. Opportunistic mobile service computing is a promising paradigm capable of utilizing the pervasive mobile computational resources around the users. Mobile users are thus allowed to exploit nearby mobile services to boost their computing capabilities without investment in their resource pool. Nevertheless, various challenges, especially its quality-of-service and reliability-aware scheduling, are yet to be addressed. Existing studies and related scheduling strategies consider mobile users to be fully stable and available. In this article, we propose a novel method for reliability-aware and deadline-constrained service composition over opportunistic networks. We leverage the Krill–Herd-based algorithm to yield a deadline-constrained, reliability-aware, and well-executable service composition schedule based on the estimation of completion time and reliability of schedule candidates. We carry out extensive case studies based on some well-known mobile service composition templates and a real-world opportunistic contact data set. The comparison results suggest that the proposed approach outperforms existing ones in terms of success rate and completion time of composed services. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Recently, the rapid development of mobile devices and mobile communication leads to the prosperity of mobile service computing. Services running on mobile devices within a limited range are allowed to be composed to coordinate through wireless communication technologies and perform complex tasks and business processes. Despite its great potential, mobile service compositions remains a challenge since the mobility of users and devices imposes high unpredictability on the execution of tasks. A careful investigation into existing methods has found their various limitations, e.g., assuming time-invariant availability of mobile services. This article presents a novel reliability-aware and deadline-constrained service composition method for mobile opportunistic networks. Instead of assuming time-invariant availability of mobile nodes, the proposed method is capable of estimating service availability at run-time and leveraging a Krill–Herd-based algorithm to yield the deadline-constrained, reliability-aware, and well-executable service composition schedules. Case studies based on well-known service composition templates and real-world data sets suggest that it outperforms traditional ones in terms of success and completion time of composed services. It can thus aid the design and optimization of composite services as well as their smooth execution in a mobile environment. It can help practitioners better manage the reliability and performance of real-world applications built upon mobile services.

A smart energy and reliability aware scheduling algorithm for workflow execution in DVFS-enabled cloud environment

The energy consumption is one of the major concerns addressed by recent researches in the green cloud environment. As a result, to decrease the enormous increase in energy consumption, one of the most promising scheduling techniques used nowadays is the Dynamic Voltage Frequency Scaling (DVFS) technique. DVFS reduces energy consumption by lowering the processors’ frequency for virtual machines (VMs); this results in an increase in the occurrence of errors during the execution of the workflow, which decreases the reliability of the system. As a consequence, this paper addresses the DVFS problem by proposing a new Smart Energy and Reliability Aware Scheduling algorithm (SERAS) for workflow execution in the cloud environment. The SERAS approach split the target deadline of workflow across tasks. Afterward, the proposed algorithm decreases the frequency of processors for VMs using the DVFS technique without missing the tasks’ deadline. As a consequence, the SERAS algorithm allocates the tasks to the most appropriate VMs with suitable frequencies levels while guaranteeing both the reliability and the completion time requirement of green cloud systems. To vindicate the effectiveness of the SERAS algorithm in real-world applications, we carried out a series of experiments on four real workflows generated using a scientific toolkit. Also, we performed comprehensive experiments with recent researches. The results showed that the SERAS algorithm outperforms its competitors while keeping both the reliability and completion time requirements. Furthermore, the estimated time complexity and average execution time show the applicability of the SERAS algorithm compared with their competitors.

A hybrid scheduling platform: a runtime prediction reliability aware scheduling platform to improve HPC scheduling performance

The performance of scheduling algorithms for HPC jobs highly depends on the accuracy of job runtime values. Prior research has established that neither user-provided runtimes nor system-generated runtime predictions are accurate. We propose a new scheduling platform that performs well in spite of runtime uncertainties. The key observation that we use for building our platform is the fact that two important classes of scheduling strategies (backfilling and plan based) differ in terms of sensitivity to runtime accuracy. We first confirm this observation by performing trace-based simulations to characterize the sensitivity of different scheduling strategies to job runtime accuracy. We then apply gradient boosting tree regression as a meta-learning approach to estimate the reliability of the system-generated job runtimes. The estimated prediction reliability of job runtimes is then used to choose a specific class of scheduling algorithm. Our hybrid scheduling platform uses a plan-based scheduling strategy for jobs with high expected runtime accuracy and backfills the remaining jobs on top of the planned jobs. While resource sharing is used to minimize fragmentation of resources, a specific ratio of CPU cores is reserved for backfilling of less predictable jobs to avoid starvation of these jobs. This ratio is adapted dynamically based on the resource requirement ratio of predictable jobs among recently submitted jobs. We perform extensive trace-driven simulations on real-world production traces to show that our hybrid scheduling platform outperforms both pure backfilling and pure plan-based scheduling algorithms.

Reliability aware scheduling of bag of real time tasks in cloud environment

Cloud environment uses data center with a huge number of computational resources, and the probability of failing any of the resources increases with scale. Failures cause unavailability of services, which affects the reliability of the system. It is essential to consider the reliability issue for application deployment in the cloud, considering the failure of the resources. In this work, we address the reliability aware scheduling of tasks with hard deadlines in the cloud environment. We design, analyze and provide solutions for two special cases of the problem where (a) tasks have a common deadline on the machines with equal failure rate, and (b) tasks with equal execution time. For the general case of the problem, we propose two-phase heuristic approaches, one is the task ordering, and other is tasks mapping to machines. The performance of different task orderings and task mapping approaches is evaluated through simulation using synthetic and real traces. Based on the simulation result, the earliest due date ordering of tasks and mapping of the current task to the most reliable machine along with long task dropping performs better in general settings. We observe that task repetition and replication further improve the performance of the heuristics.

Optimizing Soft Error Reliability Through Scheduling on Heterogeneous Multicore Processors

Reliability to soft errors is an increasingly important issue as technology continues to shrink. In this paper, we show that applications exhibit different reliability characteristics on big, high-performance cores versus small, power-efficient cores, and that there is significant opportunity to improve system reliability through reliability-aware scheduling on heterogeneous multicore processors. We monitor the reliability characteristics of all running applications, and dynamically schedule applications to the different core types in a heterogeneous multicore to maximize system reliability. Reliability-aware scheduling improves reliability by 25.4 percent on average (and up to 60.2 percent) compared to performance-optimized scheduling on a heterogeneous multicore processor with two big cores and two small cores, while degrading performance by 6.3 percent only. We also introduce a novel system-level reliability metric for multiprogram workloads on (heterogeneous) multicores. We provide a trade-off analysis among reliability-, power- and performance-optimized scheduling, and evaluate reliability-aware scheduling under performance constraints and for unprotected L1 caches. In addition, we also extend our scheduling mechanisms to multithreaded programs. The hardware cost in support of our reliability-aware scheduler is limited to 296 bytes per core.

Reliability-Aware Runtime Adaption Through a Statically Generated Task Schedule

Device scaling, increasing number of components in a single chip, varying environmental issues, and aging effects have brought severe reliability challenges that impose tight constraints on the operation of a system. To cope with these challenges, this paper proposes a reliability-aware scheduling framework that combines static and dynamic analyses to improve the overall system resiliency to different kinds of faults (i.e., intermittent, transient, and permanent). The static analysis technique employs genetic algorithms to optimize the overall system reliability by considering reliability level (RL) as an intermediate scheduling dimension and creating a task-to-RL mapping. This enables the RL-to-core mapping to be efficiently adapted at runtime according to fault rate variations, while the task-to-RL mapping can still be reused. The dynamic analysis tracks faults appearing in each core and measures the time correlation of those faults to update the RL-to-core mapping. The proposed reliability-aware framework is implemented in a state-of-the-art runtime system, Delaware Adaptive Run-Time System, so as to quantitatively show the advantages of using the overall framework in existing multicore platforms. Experimental results show that the proposed technique delivers up to 30% improvement in application execution time and up to 72% improvement in faults occurring at runtime.

Reliability-aware scheduling for reducing system-wide energy consumption for weakly hard real-time systems

Abstract While energy consumption is the primary concern for the design of real-time embedded systems, reliability and Quality of Service (QoS) are becoming increasingly important in the development of today’s pervasive computing systems. In this paper, we present a reliability-aware energy management (RAEM) scheme for reducing the energy consumption for weakly hard real-time systems with (m, k)-constraints, which requires that at least m out of any k consecutive jobs of a task meet their deadlines. In order to ensure the (m, k)-constraints while preserving the system reliability, we propose to partition the real-time jobs into mandatory and optional ones as well as to reserve recovery space for mandatory ones in an adaptive way. Moreover, efficient on-line scheduling techniques are proposed to reduce the system-wide energy, which is consumed not only by the processor but also by other peripheral devices at run-time. Through extensive simulations, our experiment results demonstrate that the proposed techniques can significantly outperform the previous research in reducing system-wide energy consumption for weakly hard real-time systems while preserving the system reliability.

Soft and Hard Reliability-Aware Scheduling for Multicore Embedded Systems with Energy Harvesting

For multicore embedded systems powered by energy harvesting, it is necessary to develop intelligent resource allocation techniques that adjust the application execution strategy on-the-fly to adapt to changing energy supply from the harvesting system. To cope with the complexity of managing applications with data dependencies on such systems, we propose a hybrid design-time/run-time framework for resource allocation that takes into consideration variations in solar radiance and execution time, transient faults, and permanent faults due to aging effects. Our framework generates schedule templates at design-time with an emphasis on energy efficiency and uses lightweight online management schemes to react to run-time system dynamics. Experimental results indicate that our framework presents improvements in performance and adaptivity, with up to 23.2 percent miss rate reduction compared to prior work, 43.6 percent performance benefits from adaptive run-time workload management, and up to 24.5 percent expected system lifetime improvement with aging-aware allocation of workload partitions.

Latency Improvement Strategies for Reliability-Aware Scheduling in Industrial Wireless Sensor Networks

We propose novel strategies for end-to-end reliability-aware scheduling in Industrial Wireless Sensor Networks (IWSNs). Because of stringent reliability requirements in industrial applications where missed packets may have disastrous or lethal consequences, all IWSN communication standards are based on Time Division Multiple Access (TDMA), allowing for deterministic channel access on the MAC layer. We therefore extend an existing generic and scalable reliability-aware scheduling approach by the name of SchedEx. SchedEx has proven to quickly produce TDMA schedules that guarantee a user-defined end-to-end reliability levelρ_for all multihop communication in a WSN. Moreover, SchedEx executes orders of magnitude faster than recent algorithms in the literature while producing schedules with competitive latencies. We generalize the original problem formulation from single-channel to multichannel scheduling and propose a scalable integration into the existing SchedEx approach. We further introduce a novel optimal bound that produces TDMA schedules with latencies around 20% shorter than the original SchedEx algorithm. Combining the novel strategies with multiple sinks, multiple channels, and the introduced optimal bound, we could through simulations verify latency improvements by almost an order of magnitude, reducing the TDMA superframe execution times from tens of seconds to seconds only, which allows for a utilization of SchedEx for many time-critical control applications.

Fault Tolerant Task Scheduling on Computational Grid Using Checkpointing Under Transient Faults

Application scheduling is crucial for grid computing environment. The failure of grid resources poses a great challenge to it. Most existing application scheduling algorithms deal with resource failures by employing reliability-aware scheduling without considering performance and do not adequately provide fault tolerance to them. In this paper, we proposed a fault tolerant task scheduling algorithm for independent and dependent (workflows) tasks considering reliability as well as the performance of grid resources. We focused on the Weibull distributed failures of grid resources in spite of commonly adopted assumption of Poisson failure distribution. To handle such failures, rollback recovery via checkpoint/restart is used for improving system dependability and reliability. The optimal checkpointing frequency is used with the goal to minimize the fault tolerance overhead (expected waste time). Based on minimal wasted time, a new factor known as capacity decreasing factor is generated. It considers both the performance and failure characteristics of the resources. Finally, the efficient scheduling decision is made using genetic algorithm considering the capacity decreasing factor by generating the new computing capacity of the resources in the presence of failures. The efficient scheduling solution is generated having both optimal performance (makespan) and reliability (i.e., the lowest tendency to fail). Further, precedence constraint of sub-tasks is also considered, where ordering of tasks is performed considering the precedence relationship and fault tolerance overhead. The simulation results show that our proposed fault tolerant scheduling algorithm achieves better performance and execution reliability than other previous algorithms in the presence of failures.

Resource Allocation and Scheduling in Cloud Computing: Policy and Algorithm

ABSTRACTCloud computing is a new distributed commercial computing model that aims at providing computational resources or services to users over a network in a low-cost manner. Resource allocation and scheduling (RAS) is the key focus of cloud computing, and its policy and algorithm have a direct effect on cloud performance and cost. This paper presents five major topics in cloud computing, namely locality-aware task scheduling; reliability-aware scheduling; energy-aware RAS; Software as a Service (SaaS) layer RAS; and workflow scheduling. These five topics are then classified into three parts: performance-based RAS; cost-based RAS; and performance- and cost-based RAS. A number of existing RAS policies and algorithms are discussed in detail accordingly with regard to their given parameters. In addition, a comparative analysis of five identified problems with their representative algorithms is performed. Finally, some future research directions of cloud RAS are pointed out.

End-to-End Reliability-Aware Scheduling for Wireless Sensor Networks

Wireless sensor networks (WSNs) are gaining popularity as a flexible and economical alternative to field-bus installations for monitoring and control applications. For mission-critical applications, communication networks must provide end-to-end reliability guarantees, posing substantial challenges for WSN. Reliability can be improved by redundancy, and is often addressed on the MAC layer by resubmission of lost packets, usually applying slotted scheduling. Recently, researchers have proposed a strategy to optimally improve the reliability of a given schedule by repeating the most rewarding slots in a schedule incrementally until a deadline. This Incrementer can be used with most scheduling algorithms but has scalability issues which narrows its usability to offline calculations of schedules, for networks that are rather static. In this paper, we introduce SchedEx, a generic heuristic scheduling algorithm extension, which guarantees a user-defined end-to-end reliability. SchedEx produces competitive schedules to the existing approach, and it does that consistently more than an order of magnitude faster. The harsher the end-to-end reliability demand of the network, the better the SchedEx performs compared to the Incrementer. We further show that SchedEx has a more evenly distributed improvement impact on the scheduling algorithms, whereas the Incrementer favors schedules created by certain scheduling algorithms.

Reliability-aware scheduling strategy for heterogeneous distributed computing systems

Heterogeneous computing systems are promising computing platforms, since single parallel architecture based systems may not be sufficient to exploit the available parallelism with the running applications. In some cases, heterogeneous distributed computing (HDC) systems can achieve higher performance with lower cost than single-machine supersystems. However, in HDC systems, processors and networks are not failure free and any kind of failure may be critical to the running applications. One way of dealing with such failures is to employ a reliable scheduling algorithm. Unfortunately, most existing scheduling algorithms for precedence constrained tasks in HDC systems do not adequately consider reliability requirements of inter-dependent tasks. In this paper, we design a reliability-driven scheduling architecture that can effectively measure system reliability, based on an optimal reliability communication path search algorithm, and then we introduce reliability priority rank (RRank) to estimate the task’s priority by considering reliability overheads. Furthermore, based on directed acyclic graph (DAG) we propose a reliability-aware scheduling algorithm for precedence constrained tasks, which can achieve high quality of reliability for applications. The comparison studies, based on both randomly generated graphs and the graphs of some real applications, show that our scheduling algorithm outperforms the existing scheduling algorithms in terms of makespan, scheduling length ratio, and reliability. At the same time, the improvement gained by our algorithm increases as the data communication among tasks increases.