Hybrid Rendering with Scheduling under Uncertainty

Tamm, Georg; Krüger, Jens

doi:10.1109/tvcg.2014.2303092

Cited by 4 publications

(3 citation statements)

References 28 publications

(21 reference statements)

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“…The waiting time of subsequent jobs is obviously prolonged, thus reducing system resource utilization and throughput rate. To overcome this disadvantage, a short job priority scheduling strategy [6] was proposed; however, this method might delay a large job to arrive at the earliest time, which can cause job scheduling unfairness. The FirstFit strategy aims to evaluate jobs in the queue and then scheduling the first job with available resources, whereas the BestFit strategy aims to determine the job with the maximum resource satisfaction in the present system [7].…”

Section: State Of the Artmentioning

confidence: 99%

A Novel Optimization Strategy for Job Scheduling based on Double Hierarchy

Zhao¹,

Gao²,

Zheng³

et al. 2017

JESTR

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At present, the high-performance cluster system has been widely applied to multitask and multiuser data processing procedures. However, computation loads can be influenced by the job scheduling optimization strategy (JSOS) of the cluster system, which can cause imbalance during job scheduling process, job starvation, and resource fragmentation. This situation can further result to problems, such as dissatisfactory resource utilization and lengthy job response, turnover, and completion times. First, a double hierarchical job scheduling model was proposed in this study to optimize the job scheduling strategy of the cluster system. Second, this study analyzed the hierarchical tasks in the scheduling model and the factors, namely, resource utilization and job completion time that influence them. The reasonability of the JSOS was also verified. Finally, the optimization strategy for job scheduling was compared with the first-come, firstserved (FCFS) and FirstFit strategies. Result shows that compared with FCFS and FirstFit strategies, the proposed job scheduling strategy increased resource utilization by 6.3% and 0.8%, and reduced average response time by 22.05% and 1.12%, average turnover time by 9.82% and 2.01%, and completion time by 10.45% and 1.11%, respectively. Thus, the proposed double hierarchical job scheduling strategy not only improves system resource utilization but also reduces job response, turnover and job completion times. The experimental result is consistent with the expected requirements, and the study provides a feasible scheme for the job scheduling optimization problem in the cluster system.

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Section: State Of the Artmentioning

confidence: 99%

A Novel Optimization Strategy for Job Scheduling based on Double Hierarchy

Zhao¹,

Gao²,

Zheng³

et al. 2017

JESTR

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“…The render farm with a pool of finite rendering resources receives the rendering jobs, chooses appropriate resources according to the demands of rendering users and scheduling rendering jobs according to some policies. Generally speaking, the research spot is focus on optimizing and design of scheduling policy [9,26,19]. But many issues are based the on the premise of obtaining the application running time in advance or have not a-priori knowledge on execution time prediction [17], which will negatively impacts the efficiency of scheduling policy, especially for the static policy from the view of system.…”

Section: Introductionmentioning

confidence: 99%

A Statistics Based Prediction Method for Rendering Application

et al. 2016

Lecture Notes in Computer Science

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As an interesting commercial application, rendering plays an important role in the field of animation and movie production. Generally, render farm is used to rendering mass images concurrently according to the independence among frames. How to scheduling and manage various rendering jobs efficiently is a significant issue for render farm. Therefore, the prediction of rendering time for frames is relevant for scheduling, which offers the reference and basis for scheduling method. In this paper a statistics based prediction method is addressed. Initially, appropriate parameters which affect the rendering time are extracted and analyzed according to parsing blend formatted files which offers a general description for synthetic scene. Then, the sample data are gathered by open source software Blender and J48 classification algorithm is used for predicting rendering time. The experimental results show that the proposed method improve the prediction accuracy about 60% and 75.74% for training set and test set, which provides reasonable basis for scheduling jobs efficiently and saving rendering cost.

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“…Remote rendering has also been employed for scientific data (volume rendering) (Tamm & Krüger, 2014). The authors used hybrid rendering to attenuate the network latency.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Cloud Rendering for Industrial-Plant Cad Models

MOREIRA¹

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Industrial-plant CAD models play an important role in engineering project management. Despite the advances in computing power in past decades, rendering these models remains challenging due to their complexity and large data volume. Different areas of computing have succeeded in adopting cloud services to process massive data. However, when it comes to cloud rendering, there is still a lack of cloud rendering services for CAD models. In this paper, we propose a hybrid cloud rendering architecture for CAD models, dividing the rendering task between client and server. In addition to reducing server overhead, this approach affords greater resilience to the system against variations of network latency. Finally, this work also introduces a metaheuristic-based workload selection algorithm to determine the set of objects to be drawn on the client side. Our results demonstrate that the proposed methodology allows efficient visualization of massive CAD models even under adverse conditions such as clients with limited devices and high connection latency. Lastly, we discuss remaining research opportunities for cloud rendering, opening avenues for future improvements.

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Hybrid Rendering with Scheduling under Uncertainty

Cited by 4 publications

References 28 publications

A Novel Optimization Strategy for Job Scheduling based on Double Hierarchy

A Novel Optimization Strategy for Job Scheduling based on Double Hierarchy

A Statistics Based Prediction Method for Rendering Application

Hybrid Cloud Rendering for Industrial-Plant Cad Models

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