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In order to enhance the speed and accuracy of cluster scheduling for mobile robots in the medication management system, a cluster scheduling model was established using mobile robots as carriers. The scheduling coding adopted a discrete-continuous dual-mapping encoding method. In order to enhance the computational performance of the scheduling algorithm, an improved harmony search (IHS) algorithm was proposed, which integrated the optimal estimation optimization method. The algorithm combined optimal iteration and directed search for optimal scheduling search, thereby improving the computational speed and accuracy of the algorithm. Adaptive bandwidth adjustment and global random crossover mutation were performed based on the global optimum and harmony, expanding the algorithm's search range and sample diversity, thereby enhancing the algorithm's ability to search and compute the global optimal solution. Furthermore, the improved algorithm was validated on continuous intervals using standard test functions, yielding favorable results. Through comparative experiments, the results show that under identical test conditions, the proposed improved algorithm exhibits superior capability in searching for optimal solutions compared to the control algorithm, resulting in varying degrees of improvement in the accuracy of scheduling results. It is evident that the IHS algorithm effectively optimizes the performance of the automatic medication system scheduling system. [ABSTRACT FROM AUTHOR]

A new layout of the U-shaped automated terminal is more convenient to connect between the terminal and the railway. In this study, a sort of cluster scheduling method for multiple equipment between a U-shaped automated terminal yard and a railway yard is proposed. The innovation points are as follows: (1) Considering that the rail gantry crane (RGC), intelligent guided vehicle (IGV), and double cantilever rail crane (DCRC) usually work in groups, they are grouped and cluster-scheduled. (2) A hybrid integer programming model is established to minimize container transit times, and non-crossing constraints and safe distance constraints are included to reflect complex interactions among terminal equipment. (3) An ADMM (Alternating Direction Method of Multipliers)-based framework is proposed to dualize the hard-edge constraints and break the cluster scheduling problem down into a specific subproblem set of RGCs, IGVs, and DCRCs, and their time cost is iteratively adjusted to improve the solution quality. The experiment results show that the solution of the proposed method, which can effectively avoid IGV conflict, is better than that of the standard Lagrange relaxation (LR) when the number of equipments participating in scheduling increases. This study on multi-equipment cluster scheduling is conducive to improving the collaborative handling and continuous work of terminal equipment, and improving the efficiency of the automated terminal and the sea–rail intermodal transport. [ABSTRACT FROM AUTHOR]

In the past decade, heterogeneous multicore architectures with support for Single Instruction Multiple Thread (SIMT) style computing have become the standard platform of choice for scheduling HPC applications. Here, applications are typically modelled as a set of data-parallel tasks with dependencies represented in the form of a directed acyclic graph (DAG). The relevant execution time information for each constituent task in the DAG is known beforehand and is leveraged by scheduling algorithms (List or Cluster based) to ascertain near-optimal schedules at runtime. However, given an online setting, where applications are submitted by multiple users and the types of applications are not restrictive, the chances of knowing execution time information for every program are highly unlikely. In this context, we propose a class of intelligent algorithms for heterogeneous CPU-GPU platforms that leverage static analysis-assisted machine learning techniques for deciding how device assignments should be made at runtime, thus bypassing the requirement for expensive offline profiling passes. We formalize relevant task-level ranking metrics and discuss how existing scheduling techniques can be adapted for our proposed class of algorithms. We also devise an online cluster scheduling algorithm that supports dynamic task arrival by determining in any given scheduling epoch, mapping decisions for a subset of tasks in a DAG. We perform a detailed comparative analysis between our proposed cluster and list scheduling heuristics via extensive simulation experiments using a variety of heterogeneous multicore platform configurations and observe performance speedups in the range of 1.1–1.5× for cluster scheduling over that of list scheduling. [ABSTRACT FROM AUTHOR]

A new layout of the U-shaped automated terminal is more convenient to connect between the terminal and the railway. In this study, a sort of cluster scheduling method for multiple equipment between a U-shaped automated terminal yard and a railway yard is proposed. The innovation points are as follows: (1) Considering that the rail gantry crane (RGC), intelligent guided vehicle (IGV), and double cantilever rail crane (DCRC) usually work in groups, they are grouped and cluster-scheduled. (2) A hybrid integer programming model is established to minimize container transit times, and non-crossing constraints and safe distance constraints are included to reflect complex interactions among terminal equipment. (3) An ADMM (Alternating Direction Method of Multipliers)-based framework is proposed to dualize the hard-edge constraints and break the cluster scheduling problem down into a specific subproblem set of RGCs, IGVs, and DCRCs, and their time cost is iteratively adjusted to improve the solution quality. The experiment results show that the solution of the proposed method, which can effectively avoid IGV conflict, is better than that of the standard Lagrange relaxation (LR) when the number of equipments participating in scheduling increases. This study on multi-equipment cluster scheduling is conducive to improving the collaborative handling and continuous work of terminal equipment, and improving the efficiency of the automated terminal and the sea–rail intermodal transport.

Summary: Real‐time systems are nowadays extensively used in most time‐critical embedded applications. The existing multiprocessor real‐time scheduling algorithms are based on partitioned and global scheduling approaches. The partitioned‐based algorithms suffer poor utilization bound, load balancing, not compatible for an open system environment, and the global scheduling approach faces problems like high scheduling, migration overhead. Cluster scheduling represents a hybrid scheduling approach which consists of a set of processors as clusters and tasks scheduled to each processor of clusters using a global scheduling approach. The different cluster formation heuristics are investigated for homogenous clusters. The main objective of this article is to propose task utilization‐based cluster formation and harmonic period aware task allocation for a hierarchical real‐time system on a multiprocessor platform. Tasks with high utilization, cluster size tune to a smaller value to achieve high utilization. Tasks with low utilization, the size of cluster tune to larger to reduce migration and scheduling overheads. Experimentation performed on multiprocessor real‐time simulator with a different set of tasks, multiprocessor system, different tasks utilization, and cluster size. Simulation results show that cluster base approach increases the number of tasks scheduled on a multiprocessor system, system utilization, and reduces response time, migration, and preemption overheads. [ABSTRACT FROM AUTHOR]

Multi-tenant GPU clusters are common, where users purchase GPU quota to run their neural network training jobs. However, strict quota-based scheduling often leads to cluster under-utilization, while allowing quota groups to use excess GPUs improves utilization but results in fairness problems. We propose PPS, a probabilistic prediction based scheduler, which uses job history statistics to predict future cluster status for making good scheduling decisions. Different from existing schedulers that rely on deep learning frameworks to adjust bad scheduling decisions and/or require detailed job information, PPS treats jobs as black boxes in that PPS runs a job to completion without adjustment once scheduled and requires only aggregate job statistics. The black-box feature is favorable due to its good generality, compatibility and security, and made possible by the predictability of aggregate resource utilization statistics of large clusters. Extensive experiments show that PPS achieves high cluster utilization and good fairness simultaneously. • Existing GPU cluster scheduling strategies trade-off between fairness and efficiency. • Aggregate job statistics are predictable for large-scale production GPU clusters. • Our scheduler PPS achieves both fairness and efficiency by predicting resource status. • PPS is general, transparent, and secure because it is black-box and non-preemptive. • Tailored procedures are designed to predict the idle resources and resource demands. [ABSTRACT FROM AUTHOR]

Two-stage precoding is a promising transmission strategy for frequency-division duplex (FDD) massive multiple-input multiple-output (MIMO) systems due to its large multiplexing gain with significant overhead reduction in both downlink training and feedback. In this paper, we propose a new agglomerative clustering method to significantly simplify the user clustering process. In order to suppress the residual inter-cluster interference in realistic two-stage precoding transmission, we propose an average signal-to-leakage-plus-noise ratio (SLNR)-based iterative cluster scheduling and outer precoder design scheme to achieve a balance between providing high multiplexing gain and improving per-user rate. For uniform linear arrays (ULAs), a fast implementation of the scheme with discrete Fourier transformation (DFT) approximation is proposed. The numerical results demonstrate the performance improvement of the proposed methods over the existing methods.

Data centers form the core of current information and commercial enterprise. At current scale, any improvement in data center resource utilization leads to substantial savings. We focus on the problem of scheduling jobs in distributed execution environments to improve resource utilization. Cluster schedulers like YARN and Mesos base their scheduling decisions on resource requirements provided by end users. It is hard for end-users to predict the exact amount of resources required for a task/job, especially since resource utilization can vary significantly over time and across tasks. In practice, users make highly conservative estimates of peak utilization across all tasks of a job to ensure job completion, leading to resource fragmentation and severe under utilization in production clusters. We present UBIS, a utilization-aware approach to cluster scheduling, to address resource fragmentation, and to improve cluster utilization and job throughput. UBIS considers actual usage of running tasks and schedules opportunistic work on under-utilized nodes. UBIS monitors resource usage on these nodes and preempts opportunistic containers in the event this over-subscription becomes untenable. In doing so, UBIS effectively utilizes wasted resources, while minimizing adverse effects on regularly scheduled tasks. Our implementation of UBIS on YARN demonstrates improvements of up to 30% in makespan for a representative workload and 25% in individual job durations. • The paper presents a scheduling technique, called UBIS for cloud environments with mixed jobs – some with guaranteed service level requirements (SLAs), and others with flexible requirements. • UBIS utilizes slack in runtime utilization of resources by tasks with guaranteed service level requirements to schedule opportunistic tasks around them. • These opportunistic tasks are terminated if SLAs are likely to be violated, as inferred from dynamic state of the nodes. • By utilizing slack, UBIS significantly increases resource utilization and throughput, while ensuring fairness in highly dynamic environments. • Comprehensive benchmarking of UBIS in cloud deployments with realistic workloads demonstrates significant increase in throughput with associated operational cost reduction, low overhead of monitoring and scheduling, and excellent scalability. [ABSTRACT FROM AUTHOR]

As microservice architecture is becoming more popular than ever, developers intend to transform traditional monolithic applications into service-based applications (composed by a number of services). To deploy a service-based application in clouds, besides the resource demands of each service, the traffic demands between collaborative services are crucial for the overall performance. Poor handling of the traffic demands can result in severe performance degradation, such as high response time and jitter. However, current cluster schedulers fail to place services at the best possible machine, since they only consider the resource constraints but ignore the traffic demands between services. To address this problem, we propose a new approach to optimize the placement of service-based applications in clouds. The approach first partitions the application into several parts while keeping overall traffic between different parts to a minimum and then carefully packs the different parts into machines with respect to their resource demands and traffic demands. We implement a prototype scheduler and evaluate it with extensive experiments on testbed clusters. The results show that our approach outperforms existing container cluster schedulers and representative heuristics, leading to much less overall inter-machine traffic. [ABSTRACT FROM AUTHOR]

Popular data analytics systems including Apache Hadoop, Dryad, and Apache Spark abstract jobs as directed acyclic graphs (DAGs). Speeding up completions for DAG jobs matter in practice in order to support real-time decisions. State-of-the-art works propose clairvoyant schedulers to optimize these goals, however, they assume complete job information as a prior knowledge which includes the precise DAG structure, and fine-grained resource requirement and duration time of each task. This assumption limits their applicability. In this paper, to be more practical, we relax the complete prior knowledge assumption and rely solely on partial prior information, based on which, we design a semi-clairvoyant task scheduler Cobra operating within each job. When managing resources for a job, Cobra adaptively adjusts its resource desires in a multiplicative-increase multiplicative-decrease manner on the basis of the its current resource utilization and the presence of current waiting tasks. When assigning tasks to run on the allocated resources, Cobra strives to satisfy task locality preference by tolerating each task waiting for some time that is bounded by a parameterized threshold. Even with the partial prior job information, when a set of jobs in which each employing Cobra as its task scheduler, run on a cluster that employs the fair job scheduler, we theoretically prove the produced makespan and average job response time are O(1)-competitive in different settings. We implement our design in Spark on YARN system, and use experiments from both real deployments and simulations on Google's trace to verify the performance promotion and sensitivity of Cobra. [ABSTRACT FROM AUTHOR]

Workload consolidation is a common method to improve the resource utilization in clusters or data centers. In order to achieve efficient workload consolidation, the runtime characteristics of a program should be taken into consideration in scheduling. In this paper, we propose a novel index system for efficiently describing the program runtime characteristics. With the help of this index system, programs can be classified by the following runtime characteristics: 1) dependence to multi-dimensional resources including CPU, disk I/O, memory and network I/O; and 2) impact and vulnerability to resource sharing embodied by resource usage and resource sensitivity. In order to verify the effectiveness of this novel index system in workload consolidation, a scheduling strategy, Sche-index, using the new index system for workload consolidation is proposed. Experiment results show that compared with traditional least-loaded scheduling strategy, Sche-index can improve both program performance and system resource utilization significantly. [ABSTRACT FROM AUTHOR]

As microservice architecture is becoming more popular than ever, developers intend to transform traditional monolithic applications into service-based applications (composed by a number of services). To deploy a service-based application in clouds, besides the resource demands of each service, the traffic demands between collaborative services are crucial for the overall performance. Poor handling of the traffic demands can result in severe performance degradation, such as high response time and jitter. However, current cluster schedulers fail to place services at the best possible machine, since they only consider the resource constraints but ignore the traffic demands between services. To address this problem, we propose a new approach to optimize the placement of service-based applications in clouds. The approach first partitions the application into several parts while keeping overall traffic between different parts to a minimum and then carefully packs the different parts into machines with respect to their resource demands and traffic demands. We implement a prototype scheduler and evaluate it with extensive experiments on testbed clusters. The results show that our approach outperforms existing container cluster schedulers and representative heuristics, leading to much less overall inter-machine traffic.

Cluster-based scheduling is recently gaining importance to be applied to mixed-criticality real-time systems on multicore processors platform. In this approach, the cores are grouped into clusters, and tasks that are partitioned among different clusters are scheduled by global scheduler in each cluster. This research work introduces a new cluster-based task allocation scheme for the mixed-criticality real-time task sets on multicore processors. For task allocation, smaller clusters sizes (sub-clusters) are used for mixed-criticality tasks in low criticality mode, while relatively larger cluster sizes are used for high criticality tasks in high criticality mode. In this research paper, the mixed-criticality task set is allocated to clusters using worst-fit heuristic. The tasks from each cluster are also allocated to its sub-clusters, using the same worst-fit heuristic. A fixed-priority response time analysis approach based on Audsley’s approach is used for the schedulability analysis of tasks in each cluster and sub-cluster. If the high criticality job is not completed after its worst case execution time in low mode, then the system is switched to high criticality mode. After mode switch, all the low criticalities tasks are discarded and only high criticality tasks are further executed in high criticality mode. Simulation results indicate that the percentage of schedulable task sets significantly increases under cluster scheduling as compared to partitioned and global mixed-criticality scheduling schemes. [ABSTRACT FROM AUTHOR]

Hadoop is a widely used open source implementation of MapReduce which is a popular programming model for parallel processing large scale data intensive applications in a cloud environment. Sharing Hadoop clusters has a tradeoff between fairness and data locality. When launching a local task is not possible, Hadoop Fair Scheduler (HFS) with delay scheduling postpones the node allocation for a while to a job which is to be scheduled next as per fairness to achieve high locality. This waiting becomes waste when the desired locality could not be achieved within a reasonable period. In this paper, a modified delay scheduling in HFS is proposed and implemented in Hadoop. It avoids the aforementioned waiting of delay scheduler if achieving locality is not possible. Instead of blindly waiting for a local node, the proposed algorithm first estimates the time to wait for a local node for the job and avoids waiting wherever achieving locality is not possible within the predefined delay threshold while accomplishing same locality. The performance of the proposed algorithm is evaluated by extensive experiments and it has been observed that the algorithm works significantly better in terms of response time and fairness achieving up to 20% speedup and up to 38% fairness in certain cases. [ABSTRACT FROM AUTHOR]

This paper proposed a constraint-based scheduling mechanism consisting of a constraint-description method and constraint scheduling algorithm to solve the problem of traditional cluster schedulers' inability to process constraint based issues in heterogeneous clusters. This constraint-description method could use extensible attribute sets to described heterogeneous task requirements and machine attributes, and then described all sorts of constraints. This algorithm used the hard constraints as filter criteria, the soft constraints as selection criteria, and then selected the best machine for task placement. Experiments show that the proposed constraint scheduling mechanism has better usability, lower scheduling response latency and significantly improved task execution efficiency. Thus, the scheduling mechanism can be better used for cluster scheduling in heterogeneous environments. [ABSTRACT FROM AUTHOR]

Cluster scheduling, where processors are grouped into clusters and the tasks that are allocated to one cluster are scheduled by a global scheduler, has attracted attention in multiprocessor real-time systems research recently. In this paper, assuming that an optimal global scheduler is adopted within each cluster, we investigate the worst-case utilization bounds for cluster scheduling with different task allocation/partitioning heuristics. First, we develop a lower limit on the utilization bounds for cluster scheduling with any reasonable task allocation scheme. Then, the lower limit is shown to be the exact utilization bound for cluster scheduling with the worst-fit task allocation scheme. For other task allocation heuristics (such as first-fit, best-fit, first-fit decreasing, best-fit decreasing and worst-fit decreasing), higher utilization bounds are derived for systems with both homogeneous clusters (where each cluster has the same number of processors) and heterogeneous clusters (where clusters have different number of processors). In addition, focusing on an efficient optimal global scheduler, namely the boundary-fair (Bfair) algorithm, we propose a period-aware task allocation heuristic with the goal of reducing the scheduling overhead (e.g., the number of scheduling points, context switches and task migrations). Simulation results indicate that the percentage of task sets that can be scheduled is significantly improved under cluster scheduling even for small-size clusters, compared to that of the partitioned scheduling. Moreover, when comparing to the simple generic task allocation scheme (e.g., first-fit), the proposed period-aware task allocation heuristic markedly reduces the scheduling overhead of cluster scheduling with the Bfair scheduler. [ABSTRACT FROM AUTHOR]