Processor Allocation Research Articles

Optimal embedding of hypercube into exchanged hypercube and optical multi-mesh hypercube

Embedding a (guest) graph into (host) another graph can be utilized for structural simulation, processor allocation, and algorithm porting in the field of parallel architecture. Dilation and edge congestion are two prominent indicators which evaluate the quality of the embedding. In this paper, we consider the guest graph as hypercube network and the host graphs to be exchanged hypercube and optical multi-mesh hypercube. We propose new ordering which minimizes dilation and edge congestion. We also compute the layout of embedding hypercube into the aforementioned interconnection networks.

Optimization of layout for embedding half hypercube into conventional tree architectures

SummaryEmbedding a graph into another graph can be utilized for structural simulation, processor allocation, and algorithm porting in the field of parallel architecture. This has the potential to enhance the physical layout of network‐on‐chip (NoC) devices as well as to investigate their virtualization possibilities. Layout is one of the many indicators of graph embedding. An optimal layout in NoC design can result in a decreased wiring area and cost, as well as the reduction in communication delay between parallel processing components. In this work, the guest graph is the half hypercube, which possess efficient routing, fault tolerance, and Hamiltonian features. The edge isoperimetric problems is solved for the half hypercube using a novel technique called the isochronal ordering. The host graphs considered in our work are complete binary trees, ‐rooted complete binary trees, and other few predominantly investigated tree based architectures.

Scheduling energy consumption-constrained workflows in heterogeneous multi-processor embedded systems

Heterogeneous multi-processor architecture which achieves rich functionalities with different types of processors, is widely used to provide powerful calculating capability while keeping energy consumption under control. Although this architecture can improve system flexibility for adapting to future requirement variations, it results in a complex multi-task scheduling problem for system designers to produce a reasonable schedule that satisfies all deadline, data dependency, and energy limitation constraints. In this paper, we concentrate on the energy consumption-constrained scheduling problem of workflows in heterogeneous multi-processor embedded systems. First, we model the workflows and energy consumption of processors, and formulate the energy consumption-constrained scheduling problem as an optimization one whose objective is to shorten the schedule length of workflows as much as possible. Then, with an improved energy per-assignment strategy, we propose a novel energy difference coefficient-based scheduling algorithm to produce an approximately optimal allocation of processors, frequencies, and start times for each task while guaranteeing that the data dependency and energy limitation constraints are satisfied. Finally, experiments on both randomly-generated and real-world workflows are conducted to verify the reliability and efficiency of the proposed approach.

Hyper‐heuristic method for processor allocation in parallel tasks scheduling

SummaryScheduling the tasks of parallel scientific applications is very important for efficient utilization of resources and reducing the overall execution time (makespan). Parallel applications typically include both data parallelism and task parallelism. It is known that the scheduling problem on multiprocessor systems problem is NP‐Hard even for applications involving pure task parallelism. The problem becomes more difficult when data parallelism is also taken into consideration. These problems usually considered in two steps, processor allocation and task scheduling, and various algorithms have been proposed. In this study, we introduce a genetic algorithm based hyper‐heuristic approach for the processor allocation problem. Experimental results indicate that the algorithm provides better performance compared to various greedy algorithms.

Three Processor Allocation Approaches towards EDF Scheduling for Performance Asymmetric Multiprocessors

With the rapid development of high-performance computing and parallel computing technology, by virtue of its cost-effectiveness, strong scalability, and easy programming, the multiprocessor system has gradually become the mainstream computing platform. Meanwhile, a growing number of researchers pay attention to the performance of multiprocessor systems, especially the task scheduling problem, which has an important impact on the system performance. Most of the current research works on task scheduling algorithms are based on the homogeneous computing environment. On the contrary, research works focusing on more complex performance asymmetric multiprocessor environments still remain rare. In this paper, we compare the effects of three earliest deadline first algorithms under different processor allocation strategies on performance asymmetric multiprocessors. We propose an efficient schedulability analysis for an allocation strategy that assigns high-priority tasks to the slowest idle processor. Experimental results show that the strategy of allocating processors with optimum speeds for high-priority tasks can schedule more task sets than the other two allocation strategies. The strategy that prioritizes the slowest processors for high-priority tasks has the smallest number of task migrations, and the strategy has the highest effective processor utilization.

A reinforcement learning based job scheduling algorithm for heterogeneous computing environment

Efficient job scheduling for heterogeneous computing environments has attracted widespread attention, jobs are usually modeled as directed acyclic graphs(DAG). Optimizing scheduling can improve system throughput. We propose a two-stage scheduling algorithm, which calculates task selection and processor allocation respectively. In task selection stage, we utilize a bidirectional graph convolution network to learn DAG structural features, and a fully-connected network to generate proper scheduling scheme. In the processor allocation stage, we propose a heuristic based on optimistic cost table(OCT) and task duplication, which trade-off scheduling allocation better. Experiments of various scheduling scenarios have been conducted, and the results show that the proposed algorithm has better scheduling performance than the compared heterogeneous DAG scheduling algorithms.

Computation Offloading Method for Large-Scale Factory Access in Edge-Edge Collaboration Mode

Large-scale manufacturing enterprises have complex business processes in their production workshops, and the edge-edge collaborative business model cannot adapt to the traditional computation offloading methods, which leads to the problem of load imbalance. For this problem, a computation offloading algorithm based on edge-edge collaboration mode for large-scale factory access is proposed, called the edge and edge collaborative computation offloading (EECCO) algorithm. First, the method partitions the directed acyclic graphs (DAGs) on edge server and terminal industrial equipment, then updates the tasks using a synchronization policy based on set theory to improve the accuracy effectively, and finally achieves load balancing through processor allocation. The experimental results show that the method shortens the processing time by improving computational resource utilization and employs a heterogeneous distributed system to achieve high computing performance when processing large-scale task sets.

A Dynamic Task Scheduling Strategy for Multi-Access Edge Computing in IRS-Aided Vehicular Networks

Multi-access Edge Computing (MEC) has played an important role in realizing intelligent beyond 5G (B5G) vehicular networks. The computation tasks of intelligent applications can be offloaded to and processed by near-end-user MEC servers to meet strict latency requirements. However, the latency of provided services is dependent on MEC processor scheduling and millimeter wave (mmWave) transmission conditions for the urban B5G vehicular networks. To alleviate the mmWave signal attenuation caused by buildings, Intelligent Reflecting Surface (IRS) has been regarded as efficient and prospective infrastructure. In this article, we study the IRS-aided MEC-served vehicular networks and analyze the relationship between computation resource allocation and offloading policy at an intersection. Considering the vehicle mobility patterns, transmission conditions, and task sizes, we optimize the task scheduling by improving the allocation of limited processors and IRS resource. Moreover, the mutual interference among concurrent transmissions is taken into account. Assuming the moving directions available, a dynamic task scheduling algorithm is proposed which considers both the communications and computations. The simulation results illustrate that our proposal outperforms benchmark methods in terms of task offloading rate, computing rate, and finish rate for the IRS-aided MEC-served vehicular networks.

Resilient Scheduling of Moldable Parallel Jobs to Cope With Silent Errors

We study the resilient scheduling of moldable parallel jobs on high-performance computing (HPC) platforms. Moldable jobs allow for choosing a processor allocation before execution, and their execution time obeys various speedup models. The objective is to minimize the overall completion time or the makespan, when jobs can fail due to silent errors and hence may need to be re-executed after each failure until successful completion. Our work generalizes the classical scheduling framework for failure-free jobs. To cope with silent errors, we introduce two resilient scheduling algorithms, <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Lpa-List</small> and <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Batch-List</small> , both of which use the <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">List</small> strategy to schedule the jobs. Without knowing a priori how many times each job will fail, <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Lpa-List</small> relies on a local strategy to allocate processors to the jobs, while <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Batch-List</small> schedules the jobs in batches and allows only a restricted number of failures per job in each batch. We prove approximation ratios for the two algorithms under several prominent speedup models (e.g., roofline, communication, Amdahl, power, monotonic, and a mix model). An extensive set of simulations is conducted to evaluate different variants of the two algorithms, and the results show that they consistently outperform some baseline heuristics. Overall, our best algorithm is within a factor of 1.6 of a lower bound on average over the entire set of experiments, and within a factor of 4.2 in the worst case.

Energy-aware scheduling for dependent tasks in heterogeneous multiprocessor systems

Heterogeneous multiprocessor platform has been widely adopted as an effective approach to providing strong calculation capability while keeping complexity and energy consumption under control in large-scale systems. Although this platform is able to achieve efficient cost reduction and flexibility enhancement in the design and development process of real-time applications, it brings a serious and complex multi-task scheduling problem, especially for dependent tasks with energy consumption constraints. All tasks should be scheduled according to appropriate strategies such that their dependence requirements and energy consumption limitations would be satisfied even in the worst-case situations. In this work, we focus on the energy-aware scheduling problem of dependent tasks in heterogeneous multiprocessor systems. First, we model the dependent tasks and heterogeneous processors, and formulate the energy-aware scheduling problem as a constrained optimization one with an objective of minimizing the schedule length of tasks. Then, by adopting an efficient task prioritization strategy and a weight-based energy distribution strategy, we propose a list-based energy-aware scheduling algorithm to seek an approximate optimal start time and processor allocation for each task, guaranteeing that all tasks would be executed efficiently while meeting the dependence and energy requirements. Experiments with randomly generated tasks are conducted to evaluate the performances of the proposed approach in terms of schedule length, optimal solution ratio, and execution time.

Performance Evaluation of Contiguous and Noncontiguous Processor Allocation Based on Common Communication Patterns for 2D Mesh Interconnection Network

Several processor allocation studies show that the performance of noncontiguous allocation is dramatically better than that of contiguous allocation, but this is not always true. The communication pattern may have a great effect on the performance of processor allocation algorithms. In this paper, the performance of well-known allocation algorithms is re-considered based on several communication patterns, including Near Neighbor, Ring, All-to-All, Divide and Conquer Binomial Tree (DQBT), Fast Fourier Transform (FFT), One-to-All, All-to-One, and Random. The allocation algorithms investigated include the contiguous First Fit (FF) and Best Fit (BF) and the noncontiguous Paging(0), Greedy Available Busy List (GABL) and Multiple Buddy Strategy (MBS). In near neighbor, FFT and DQBT, the simulation results show that the performance of contiguous allocation is dramatically better than that of the noncontiguous allocation in terms of response time; except for MBS in DQBT. In All-to-All, the results show that the performance of contiguous FF and BF is better than that of the noncontiguous MBS.

Performance analysis and evaluation of ternary optical computer based on asynchronous multiple vacations

Ternary optical computer (TOC) has become a research hotspot in the field because of the advantages such as inherent parallelism, numerous trits, low power consumption, extendibility, bitwise allocability and dynamical bitwise reconfigurability. Meanwhile, its performance evaluation attracts more and more attentions from potential users and researchers. To model its computing ecology more accurately, this paper first builds a three-staged TOC service model by introducing asynchronous multi-vacations and tandem queueing, and then proposes a task scheduling algorithm and an optical processor allocation algorithm with asynchronous vacations of some small optical processors after dividing equally the entire optical processor into several small optical processors which can be used independently. At the same time, the analytical model was established to obtain important performance indicators such as response time, the number of tasks and utilization of optical processor, based on M/M/1 and M/M/n queuing system with asynchronous multi-vacations. In addition, relevant numerical simulation experiments are conducted. The results illustrate that the number of small optical processors, vacation rate and the number of small optical processors allowed to be on vacation have important effects on the system performance. Compared with synchronous vacation, asynchronous vacation not only ensures the system to obtain better maintenance but also improves the system performance to some degree.

Big data-driven scheduling optimization algorithm for Cyber–Physical Systems based on a cloud platform

In this paper, we study big data-driven Cyber–Physical Systems (CPS) through cloud platforms and design scheduling optimization algorithms to improve the efficiency of the system. A task scheduling scheme for large-scale factory access under cloud–edge collaborative computing architecture is proposed. The method firstly merges the directed acyclic graphs on cloud-side servers and edge-side servers; secondly, divide the tasks using a critical path-based partitioning strategy to effectively improve the allocation accuracy; then achieves load balancing through reasonable processor allocation, and finally compares and analyses the proposed task scheduling algorithm through simulation experiments. The experimental system is thoroughly analysed, hierarchically designed, and modelled, simulated, and the experimental data analysed and compared with related methods. The experimental results prove the effectiveness and correctness of the worst-case execution time analysis method and the idea of big data-driven CPS proposed in this paper and show that big data knowledge can help improve the accuracy of worst-case execution time analysis. This paper implements a big data-driven scheduling optimization algorithm for Cyber–Physical Systems based on a cloud platform, which improves the accuracy and efficiency of the algorithm by about 15% compared to other related studies.

Scheduling energy‐conscious tasks in distributed heterogeneous computing systems

AbstractDistributed heterogeneous systems have been widely adopted in industrial applications by providing high scalability and performance while keeping complexity and energy consumption under control. However, along with the increase in the number of computing nodes, the energy consumption of distributed heterogeneous systems dramatically grows and is extremely hard to predict. Energy‐conscious task scheduling, which tries to assign appropriate priorities and processors to tasks such that the system energy requirement would be met, has received extensive attention in recent years. However, many approaches reduce energy consumption by extending the completion time. In this article, we focus on the scheduling problem of energy‐conscious tasks in distributed heterogeneous computing systems and provide an efficient approach to mitigate energy consumption while minimizing the overall makespan of parallel applications. First, based on the heterogeneous earliest finish time, a fitness function is proposed to balance the makespan and energy consumption. Then, by improving the crossover and mutation operations of the traditional genetic algorithm, we proposed an efficient scheduling approach named energy‐conscious genetic algorithm to optimize the priorities and processor allocation of tasks, with objectives of minimizing the system energy and makespan. Experiment results on real‐world applications and simulations with randomly generated task graphs demonstrate that the proposed approach outperforms in energy‐saving and makespan reducing.

Two-level utilization-based processor allocation for scheduling moldable jobs

Most modern parallel programs are written with the moldable property. However, most existing parallel computing systems treat such parallel programs as rigid jobs for scheduling, resulting in two drawbacks. The first is inflexibility and inefficiency in processor allocation, leading to resource fragmentation and thus poor performance. The second is about usage inconvenience, requiring users to figure out the best number of processors for executing a job. As HPC as a service emerges, moldable job scheduling has become an important research issue for achieving both high performance and user convenience. This paper presents our research work on developing new processor allocation approaches for moldable job scheduling based on two-level resource utilization calculation, preemptive job execution, and dual-criteria iterative improvement. A series of simulation experiments have been conducted to evaluate the proposed approaches and compare them to previous methods. The experimental results demonstrate significant performance improvement in terms of average turnaround time.

Performance Analysis and Evaluation of Ternary Optical Computer Based on a Queueing System With Synchronous Multi-Vacations

The ternary optical computer (TOC) has attracted increasing attention from its providers and potential customers because of the advantages of its optical processor, such as low power consumption, numerous trits, parallelism, dynamical reconfigurability and bitwise allocability. The analysis of its performance has become an urgent problem to be solved in recent years. This paper builds a four-stage TOC service model by introducing synchronous multi-vacations and tandem queueing. Here, a vacation refers specifically to an optical processor vacation. Additionally, we propose a processor-divided-equally(PDE) strategy and a task scheduling and optical processor allocation algorithm under this strategy. Assuming that the intervals of task arrival follow a homogeneous Poisson process, we obtain some important performance indicators, such as the mean response time, mean number of tasks, and optical processor utilization under the PDE strategy, which are based on the M/M/1 and M/M/n queueing systems with exhaustive service and synchronous multi-vacations. The numerical results illustrate that the number of small optical processors has an important effect on the performance of TOC and that high vacation rate can improve the system performance to some extent.

Optimization of processor allocation for domain decomposed Monte Carlo calculations

Load imbalance plagues domain decomposed Monte Carlo calculations when sources are not uniform. Parallel efficiency for domain decomposed Monte Carlo transport calculations improves through a nonuniform allocation of processors over subdomains. We optimize the allocation with runtime diagnostics collected during a calibration step, then complete the full calculation. The diagnostic-based approach is compared to implicit filtering, an optimization algorithm for bound constrained noisy optimization problems. We consider both forward and hybrid radiation transport calculations to measure performance.

QL-HEFT: a novel machine learning scheduling scheme base on cloud computing environment

Cloud computing is a computing model that fully utilizes the resources on the Internet to maximize the utilization of resources. Due to a large number of users and tasks, it is important to achieve efficient scheduling of tasks submitted by users. Task scheduling is one of the crucial and challenging non-deterministic polynomial-hard problems in cloud computing. In task scheduling, obtaining shorter makespan is an important objective and is related to the pros and cons of the algorithm. Machine learning algorithms represent a new method for solving this type of problem. In this paper, we propose a novel task scheduling algorithm called QL-HEFT that combines Q-learning with the heterogeneous earliest finish time (HEFT) algorithm to reduce the makespan. The algorithm uses the upward rank (ranku) value of HEFT as the immediate reward in the Q-learning framework. The agent can obtain better learning results to update the Q-table through the self-learning process. The QL-HEFT algorithm is divided into two major phases: a task sorting phase based on Q-learning for obtaining an optimal order and a processor allocation phase using the earliest finish time strategy. Experiments show that QL-HEFT achieves a shorter makespan compared to three other classical scheduling algorithms as well as good performances in terms of the average response time.

Sensitivity Analysis of Strictly Periodic Tasks in Multi-Core Real-Time Systems

In the design phase of real-time systems, it cannot be expected that the timing attributes of all tasks are completely specified and never changed. The increased computation times or shortened periods in a schedulable system often cause deadlines to be missed. In such situations, sensitivity analysis is an effective approach to provide quantitative indications for the design modification, by identifying the borderlines on parameter variations while keeping the systems schedulable. In this paper, we propose a new approach to analyze the sensitivity of the timing parameters of tasks with strict periods in multi-core real-time systems. We first analyze a schedulability condition to determine whether a task is schedulable on a given processor without changing the start times of the existing tasks. Then, following a game theory analogy, we design recursive algorithms to compute the permissible changes in the task timing parameters, by allowing each task to optimize its own start time and processor allocation. Finally, we conduct experiments with randomly generated tasks to show that our approach is more efficient than the existing solutions to solve the sensitivity problem. The proposed approach has a wide range of applications, only guiding the design of multi-core systems, but also improving the robustness of a design subject to future changes.

Revenue maximisation for scheduling deadline-constrained mouldable jobs on high performance computing as a service platforms

Traditionally, high-performance computing (HPC) systems usually deal with the so-called best-effort jobs which do not have deadlines and are scheduled in an as-quick-as-possible manner. Recently the concept of HPC as a service (HPCaaS) was proposed, aiming to transform HPC facilities and applications into a more convenient and accessible service model. To achieve that goal, there will be new issues to explore, such as scheduling jobs with deadlines and maximising the revenue of service providers. This paper presents a reservation-based dynamic scheduling approach for scheduling deadline-constrained mouldable jobs with the aim of maximising a service provider's revenue. The proposed approach has been evaluated with a series of simulation experiments. The experimental results indicate that our scheduling approach can achieve significantly higher revenue than previous methods. In the experiments, we also explored several research issues, including waiting queue sequencing, processor allocation decisions on time and space, admission control, and partial rescheduling.