Propagation-aware Temporal Verification for Parallel Business Cloud Workflows

被引:6
|
作者
Luo, Haoyu [1 ]
Liu, Xiao [2 ]
Liu, Jin [1 ]
Yang, Yun [3 ]
机构
[1] Wuhan Univ, Comp Sch, State Key Lab Software Engn, Wuhan, Hubei, Peoples R China
[2] Deakin Univ, Sch Informat Technol, Melbourne, Vic, Australia
[3] Swinburne Univ Technol, Sch Software & Elect Engn, Melbourne, Vic, Australia
基金
中国国家自然科学基金; 美国国家科学基金会;
关键词
parallel workflows; temporal violation; time delay propagation; quality of service; cloud computing;
D O I
10.1109/ICWS.2017.22
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Massive parallel business workflows running in the cloud are prone to temporal violations (namely intermediate runtime delays) due to various reasons such as service performance fluctuation and resource conflicts. To deliver satisfactory on-time completion, cloud workflow temporal verification is employed to accurately detect time delays of workflow activities and timely handle temporal violations before final deadline is violated. While most of the existing works only monitor the time delays of individual workflow activities or workflow instances, the effect of time delay propagation (similar to "Butterfly Effect") in cloud workflow systems has been overlooked, which has significant impact on the accuracy of temporal verification. In this paper, we present a propagation-aware temporal verification strategy for parallel business cloud workflows. Specifically, we first analyze the effect of time delay propagation in cloud workflow systems. Then, we present the novel temporal verification strategy based on a new propagation-aware throughput consistency model which includes the propagation effect. Experimental results demonstrate that compared with the traditional strategy, our propagation-aware strategy has higher success rate in achieving target on-time completion rate for massive parallel business cloud workflows.
引用
收藏
页码:106 / 113
页数:8
相关论文
共 50 条
  • [31] Graph-Based Interpretability for Fake News Detection through Topic- and Propagation-Aware Visualization
    Soga, Kayato
    Yoshida, Soh
    Muneyasu, Mitsuji
    COMPUTATION, 2024, 12 (04)
  • [32] Energy Efficient Task Scheduling for Parallel Workflows in Cloud Environment
    Kumar, Mallari Harish
    Peddoju, Sateesh K.
    2014 INTERNATIONAL CONFERENCE ON CONTROL, INSTRUMENTATION, COMMUNICATION AND COMPUTATIONAL TECHNOLOGIES (ICCICCT), 2014, : 1298 - 1303
  • [33] Adaptive Spot-Instances Aware Autoscaling for Scientific Workflows on the Cloud
    Monge, David A.
    Garcia Garino, Carlos
    HIGH PERFORMANCE COMPUTING, CARLA 2014, 2014, 485 : 13 - 27
  • [34] Power-Aware Mechanism for Scheduling Scientific Workflows in Cloud Environment
    Kataraki, Kirankumar, V
    Maradithaya, Sumana
    INTERNATIONAL JOURNAL OF INFORMATION SYSTEM MODELING AND DESIGN, 2021, 12 (01) : 22 - 38
  • [35] FRL-MFPG: Propagation-aware fault root cause location for microservice intelligent operation and maintenance
    Chen, Yuhua
    Xu, Dongqi
    Chen, Ningjiang
    Wu, Xu
    INFORMATION AND SOFTWARE TECHNOLOGY, 2023, 153
  • [36] Formal Verification of Temporal Properties for Reduced Overhead in Grid Scientific Workflows
    Jun-Wei Cao
    Fan Zhang
    Ke Xu
    Lian-Chen Liu
    Cheng Wu
    Journal of Computer Science and Technology, 2011, 26 : 1017 - 1030
  • [37] Formal Verification of Temporal Properties for Reduced Overhead in Grid Scientific Workflows
    Cao, Jun-Wei
    Zhang, Fan
    Xu, Ke
    Liu, Lian-Chen
    Wu, Cheng
    JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY, 2011, 26 (06) : 1017 - 1030
  • [38] Formal Verification of Temporal Properties for Reduced Overhead in Grid Scientific Workflows
    曹军威
    张帆
    许可
    刘连臣
    吴澄
    Journal of Computer Science & Technology, 2011, 26 (06) : 1017 - 1030
  • [39] A Preliminary Study on the VANET Topology Characteristics from Propagation-Aware Traffic Flows Extracted from Measured Data
    Cheng, Lin
    Verbeek, Erkin
    Skardal, Per Sebastian
    2020 IEEE INTERNATIONAL SYMPOSIUM ON ANTENNAS AND PROPAGATION AND NORTH AMERICAN RADIO SCIENCE MEETING, 2020, : 1161 - 1162
  • [40] An adaptive parallel execution strategy for cloud-based scientific workflows
    de Oliveira, Daniel
    Ogasawara, Eduardo
    Ocana, Kary
    Baiao, Fernanda
    Mattoso, Marta
    CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE, 2012, 24 (13): : 1531 - 1550