Pre-visualization techniques with live lensing system enhances workflow for interactive performance creation.

Vincs & Divers, with dancer Steph Hutchinson, present a new system for real-time previsualization in Motion Builder that enables choreographers and artists making interactive 3D work to make on-the-fly lensing decisions. Using motion capture to drive a ‘character’ created from a cloth simulation in real time, the presentation highlights the advantage of live lensing for interactive work-flow in creating 3D dance visualizations. This work forms part of Vincs’ ARC Discovery project ‘Building innovative capacity in Australian dance through new visualization technologies’ (DP120101695).

Hierarchical workflow management in wireless sensor network

To build the service-oriented applications in a wireless sensor network (WSN), the workflow can be utilized to compose a set of atomic services and execute the corresponding pre-designed processes. In general, WSN applications rely closely on the sensor data which are usually inaccurate or even incomplete in the resource-constrained WSN. Then, the erroneous sensor data will affect the execution of atomic services and furthermore the workflows, which form an important part in the bottom-to-up dynamics of WSN applications. In order to alleviate this issue, it is necessary to manage the workflow hierarchically. However, the hierarchical workflow management remains an open and challenging problem. In this paper, by adopting the Bloom filter as an effective connection between the sensor node layer and the upper application layer, a hierarchical workflow management approach is proposed to ensure the QoS of workflow-based WSN application . The case study and experimental evaluations demonstrate the capability of the proposed approach.

Layered workflow scheduling algorithm

Workflow applications require workflow processing in which workflow tasks are processed based on their dependencies. With the emergency of complex distributed systems such as grids and clouds, efficient workflow scheduling (WFS) algorithms have become the core components of the workflow management systems (WfMS). Thus, WFS that allocates each task in the workflow to a relevant resource with the aim of improving system performance and end user satisfaction is fundamentally important. In this paper, we propose a new workflow scheduling algorithm called Layered Workflow Scheduling Algorithm (LWFS) for scheduling workflow applications. We studied the efficacy of the LWFS scheduling experimentally and compared its performance with approaches including Improved Critical Path using Descendant Prediction (ICPDP), Highest Level First with Estimated Time (HLFET), Modified Critical Path (MCP) and Earliest Time First (ETF). The results of the experiments show that the proposed approach outperforms other approaches.

Workflow scheduling on distributed systems

Growing evidence shows that in obtaining high performance, a well-managed time-constrained workflow scheduling is needed. Efficient workflow scheduling is critical for achieving high performance especially in heterogeneous computing system. However, it is a great challenge to improve performance and to optimize several objectives simultaneously. We propose a workflow scheduling algorithm that minimizes the makespan of the workflow application modeled by a Directed Acyclic Graph (DAG). The new proposed scheduling algorithm is named Multi Dependency Joint (MDJ) Algorithm. The performance of MDJ is compared with existing algorithms such as, Highest Level First with Estimated Time (HLFET), Modified Critical Path (MCP) and Earliest Time First (ETF). As a result, the experiments show that our proposed MDJ algorithm outperforms HLEFT, MCP, and EFT with a 7% lower overall completion time.

Necessary and sufficient checkpoint selection for temporal verification of high-confidence cloud workflow systems

On-time completion is an important temporal QoS (Quality of Service) dimension and one of the fundamental requirements for high-confidence workflow systems. In recent years, a workflow temporal verification framework, which generally consists of temporal constraint setting, temporal checkpoint selection, temporal verification, and temporal violation handling, has been the major approach for the high temporal QoS assurance of workflow systems. Among them, effective temporal checkpoint selection, which aims to timely detect intermediate temporal violations along workflow execution plays a critical role. Therefore, temporal checkpoint selection has been a major topic and has attracted significant efforts. In this paper, we will present an overview of work-flow temporal checkpoint selection for temporal verification. Specifically, we will first introduce the throughput based and response-time based temporal consistency models for business and scientific cloud workflow systems, respectively. Then the corresponding benchmarking checkpoint selection strategies that satisfy the property of “necessity and sufficiency” are presented. We also provide experimental results to demonstrate the effectiveness of our checkpoint selection strategies, and finally points out some possible future issues in this research area.

A novel time computation model based on algorithm complexity for data intensive scientific workflow design and scheduling

Scientific workflow offers a framework for cooperation between remote and shared resources on a grid computing environment (GCE) for scientific discovery. One major function of scientific workflow is to schedule a collection of computational subtasks in well-defined orders for efficient outputs by estimating task duration at runtime. In this paper, we propose a novel time computation model based on algorithm complexity (termed as TCMAC model) for high-level data intensive scientific workflow design. The proposed model schedules the subtasks based on their durations and the complexities of participant algorithms. Characterized by utilization of task duration computation function for time efficiency, the TCMAC model has three features for a full-aspect scientific workflow including both dataflow and control-flow: (1) provides flexible and reusable task duration functions in GCE;(2) facilitates better parallelism in iteration structures for providing more precise task durations;and (3) accommodates dynamic task durations for rescheduling in selective structures of control flow. We will also present theories and examples in scientific workflows to show the efficiency of the TCMAC model, especially for control-flow. Copyright©2009 John Wiley & Sons, Ltd.

Workflow temporal verification for monitoring parallel business processes

Workflow temporal verification is conducted to guarantee on-time completion, which is one of the most important QoS (Quality of Service) dimensions for business processes running in the cloud. However, as today's business systems often need to handle a large number of concurrent customer requests, conventional response-time based process monitoring strategies conducted in a one-by-one fashion cannot be applied efficiently to a large batch of parallel processes because of significant time overhead. Similar situations may also exist in software companies where multiple software projects are carried out at the same time by software developers. To address such a problem, based on a novel runtime throughput consistency model, this paper proposes a QoS-aware throughput based checkpoint selection strategy, which can dynamically select a small number of checkpoints along the system timeline to facilitate the temporal verification of throughput constraints and achieve the target on-time completion rate. Experimental results demonstrate that our strategy can achieve the best efficiency and effectiveness compared with the state-of-the-art as and other representative response-time based checkpoint selection strategies.

An effective energy testing framework for cloud workflow activities

Cloud computing as the latest computing paradigm has shown its promising future in business workflow systems facing massive concurrent user requests and complicated computing tasks. With the fast growth of cloud data centers, energy management especially energy monitoring and saving in cloud workflow systems has been attracting increasing attention. It is obvious that the energy for running a cloud workflow instance is mainly dependent on the energy for executing its workflow activities. However, existing energy management strategies mainly monitor the virtual machines instead of the workflow activities running on them, and hence it is difficult to directly monitor and optimize the energy consumption of cloud workflows. To address such an issue, in this paper, we propose an effective energy testing framework for cloud workflow activities. This framework can help to accurately test and analyze the baseline energy of physical and virtual machines in the cloud environment, and then obtain the energy consumption data of cloud workflow activities. Based on these data, we can further produce the energy consumption model and apply energy prediction strategies. Our experiments are conducted in an OpenStack based cloud computing environment. The effectiveness of our framework has been successfully verified through a detailed case study and a set of energy modelling and prediction experiments based on representative time-series models.