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Taking into consideration the requirements of real-time embedded systems, the processor scheduler must guarantee a constant scheduling frequency, providing determinism and predictability of tasks execution. The purpose of this study is to implement the nMPRA (multi pipeline register architecture) processor into field-programmable gate array, and to integrate the already existing scheduling methods, thus providing a preemptive schedulability analysis of the proposed architecture based on the pipeline assembly line and hardware scheduler. This study describes a hardware implementation of the real-time scheduler named nHSE (hardware scheduler engine for n tasks) and presents the results obtained using the appropriate schedulability methods used in real-time environments. The scheduling and task switch operations are the main source of non-determinism, being successfully dealt with real-time nMPRA concept, in order to improve the system's functionality. Some mechanisms used for synchronisation and inter-task communication are also taken into consideration.

A smart intuitionistic fuzzy-based framework is designed to facilitate adaptability by providing continuous changes in the size of time slice to scheduler at run time. The present work models a round-robin scheduler with its imprecise parameters. To manage the impreciseness among parameters and to improve the performance, an intuitionistic fuzzy-based round-robin scheduler is implemented. IFRR scheduler integrates the two components, namely intuitionistic fuzzy inference system and hybrid round-robin scheduling approach. Intuitionistic fuzzy inference system is implemented to handle the impreciseness of burst time to provide a dynamic time slice to scheduler, whereas hybrid round-robin scheduling approach is used to make a decision on selection of next task to run. The prove the performance, the proposed scheduler is compared with the other baseline round-robin schedulers. The results prove the efficiency of scheduler in terms of average waiting time, average turnaround time, average normalized turnaround time, and number of context switches.

Task-based programming models for shared memory—such as Cilk Plus and OpenMP 3—are well established and documented. However, with the increase in parallel, many-core, and heterogeneous systems, a number of research-driven projects have developed more diversified task-based support, employing various programming and runtime features. Unfortunately, despite the fact that dozens of different task-based systems exist today and are actively used for parallel and high-performance computing (HPC), no comprehensive overview or classification of task-based technologies for HPC exists. In this paper, we provide an initial task-focused taxonomy for HPC technologies, which covers both programming interfaces and runtime mechanisms. We demonstrate the usefulness of our taxonomy by classifying state-of-the-art task-based environments in use today.

Several real-world scientific and industrial workflow applications adopt elastic and cost-efficient cloud services to fulfill their requirement. There are two stakeholders in the system, namely the user and the provider each of which tries to maximize its profit and at the same time minimize their possible overall costs. Since task scheduling algorithms for workflow executions determine which task should be possibly executed on what resources, they have a drastic impact on both the user’s quality of experiences and on the underlying resource utilization. Indeed, an efficient task scheduling algorithm can meet service-level agreement (SLA) for both sides. For the sake of the importance of the issue, this survey presents a subjective taxonomy on the task scheduling schemes in the literature for workflow executions in cloud computing environments to be a guideline for future improvement. It classifies the literature based on the proposed algorithms in the literature, objectives, stakeholders’ requirements, and evaluation metrics. This survey highlights research trends, challenges, research gaps, potential solutions, and future direction. It can also pave the way for further processing, improvement and strengthening of existing approaches or devising novel ones for interested researchers in the field of task scheduling problems.

In this paper, we analyze the scheduler log of a production supercomputer that contains complete job information, which is in contrast to many existing (publicly available) HPC logs that only have largely limited job information. We not only provide an in-depth statistical analysis of failed jobs from the scheduler log, but also demonstrate how the scheduler log, which is available in a detailed form, can be leveraged to predict job failures. For the latter, we first conduct a feature analysis based on the framework of ‘weight of evidence’ and ‘information value’ to uncover the impact of each workload attribute (feature) on the failure or success of a job, thereby enabling us to identify key features. We then conduct a comparative performance study of six data-driven machine learning models for predicting job failures in a HPC system based on the scheduler log. Our experiment results show that tree-based models exhibit superior performance in terms of both prediction accuracy and computational cost. We also demonstrate that our feature analysis improves the computational efficiency of each machine learning model without losing its prediction performance.

This article delves into the critical realm of modern computer cluster management. It focuses on the effect that the increasing heterogeneity of the clusters has on the workload managers. The proposed schedulers consider node properties instead of job properties to make decisions, which is something not currently done by mainstream scheduling algorithms. In order to increase the knowledge in this topic, this paper proposes two novel algorithms whose main task is to choose the best compute nodes to schedule the incoming jobs. To this effect, they exclusively take into account the properties of the nodes, instead of the common trend of considering the properties of the jobs. The experimental results show that these algorithms outperform well-known heuristic algorithms found in the literature.

Heterogeneous chips that combine CPUs and FPGAs can distribute processing so that the algorithm tasks are mapped onto the most suitable processing element. New software-defined high-level design environments for these chips use general purpose languages such as C++ and OpenCL for hardware and interface generation without the need for register transfer language expertise. These advances in hardware compilers have resulted in significant increases in FPGA design productivity. In this paper, we investigate how to enhance an existing software-defined framework to reduce overheads and enable the utilization of all the available CPU cores in parallel with the FPGA hardware accelerators. Instead of selecting the best processing element for a task and simply offloading onto it, we introduce two schedulers, Dynamic and LogFit, which distribute the tasks among all the resources in an optimal manner. A new platform is created based on interrupts that removes spin-locks and allows the processing cores to sleep when not performing useful work. For a compute-intensive application, we obtained up to 45.56% more throughput and 17.89% less energy consumption when all devices of a Zynq-7000 SoC collaborate in the computation compared against FPGA-only execution.

Recently, Egypt has recognized the pivotal role of High Performance Computing in advancing science and innovation. Additionally, Egypt realizes the importance of collaboration between different institutions and universities to consolidate their own computational and data resources into a unified platform to serve different disciplines (e.g., scientific, industrial, governmental). Otherwise, additional resources would be needed to be purchased with the associated cost, effort, and time difficulties (e.g., setup, administration, maintenance, etc.). Thus, this paper delves into the architecture and capabilities of the EN-HPCG grid using two different workload management systems: (i) Slurm (Open-Source) and (ii) PBS Pro (Licensed). This paper compares the performance of the grid between Slurm and PBS Pro in specific high-throughput computing (HTC) applications using the NAS Grid parallel benchmark (NGB) to determine which workload manager is more suitable for EN-HPCG. The evaluation includes grid-level performance metrics such as throughput, and the number of tasks completed as a function of time. Also, the presented methodology aims to assist potential partners in their decision-making process to join the EN-HPCG grid, with a focus on the site speed-up metric. Our results showed that, unless an open-source solution without cost and license problems is an obligation (in which case, Slurm is the viable solution), then it is not advisable to integrate a cluster with high-speed hardware with a cluster possessing outdated hardware when using the Slurm scheduler. In contrast, the PBS Pro scheduler takes into account online decision-making in a dynamic environment using a unified grid.

Hadoop MapReduce processes data on the cluster of commodity hardware (node) in two phases using Map and Reduce tasks. Yet another resource negotiator (YARN), a dynamic resource manager, allocates resources for Map tasks by preserving the data locality. In contrast, it allocates resources to schedule the Reduce tasks on any node in the cluster. The policy’s performance is better in a homogeneous environment, where the nodes’ computing capabilities are identical. However, its performance degrades in a heterogeneous environment when it allocates the containers for scheduling the Reduce tasks on any node that slowdowns the Reduce tasks execution and leads to computational skew. To mitigate the computational skew from the Reduce phase of MapReduce, we proposed the Historical data based Reduce tasks scheduling (HDRTS) technique. The technique has two algorithms: The first algorithm finds node average response time (NART) of each node by interpreting the job history information. The second algorithm allocates the resource on the faster processing node (FPN) to schedule the Reduce tasks. To evaluate the policy’s performance, we have used a very popular benchmark, i.e., the HiBench benchmark suite. Finally, compared with Hadoop’s default policy and several other policies, the proposed HDRTS policy reduces the Reduce tasks execution time for reduce-input-heavy jobs by nearly 25% to 37% significantly. Finally, it mitigates the computational skew and the stragglers from Reduce phase of MapReduce in the heterogeneous environments.

This research paper proposes a novel approach named priority-based load balancing (PLB) for cloud computing environment. The PLB provides a resilient and adaptive task scheduling using multi-queues. Numerous strategies have already been proposed in the past researches to prioritize the tasks and mapping all the tasks to different resources available on the cloud. There is still a hindrance in the performance due to the negligible attention paid to the unused resources and tasks having low priority, eventually leading to starvation problem. To this end, the PLB algorithm has been partitioned into four sub-procedures, namely (i) Starvation-free task allocation, (ii) Inserting tasks into the dispatcher, (iii) Re-ordering tasks inside the queues and eventually, (iv) Mapping tasks onto the Virtual Machines (VMs) calculating the cost incurred for all the corresponding VMs. The sole motivation of this research work is to optimize the performance parameters by allocating all the jobs to all the available resources in the workflow model. It also consolidates the job categorization in the priority-based multi-queues, while filtering tasks from all the queues to overcome the deprivation of low priority tasks. In this paper, a test-bed setup has been deployed using CloudSim 3 and TCS WAN emulator for experimentation and results evaluation. The experimental setup imbibes different aspects such as performance measures, average response time, makespan time in order to ascertain efficiency, resource utilization ratio and bandwidth of the workflow model. The obtained results are further compared with five different approaches including- First Come First Serve, Round Robin, Min–Min, Max–Min and ACO and it was observed that the proposed strategy yielded more efficiency and accuracy in most of the cases. The experimental results have been further validated and demonstrated in order to justify the claims of the proposed approach, being able to tackle out different priority tasks and resource allocation in a stable and optimum manner.