Research on intelligent service scheduling model based on service level optimization
- X Cheng, W.H. Li
- Corresponding Author
- X Cheng
Available Online November 2015.
- https://doi.org/10.2991/itms-15.2015.418How to use a DOI?
- service scheduling; mesh refinement; node searching; transmission path
- Parallel services efficiently mining frequent itemsets is the core issue of dynamic service scheduling. For the feature that service nodes split the task into reorganization service under the data transmission environment, how to arrange the services sequence of matched nodes in accordance with specific calculation sequence to ensure the best computing and communications performance and to avoid explosion in the service delivery process is a puzzle. Through in-depth study of Apriori algorithm, a mining algorithm based on Partial Depth Priority (PDP) was proposed. The algorithm has a variety of features like various service nodes distributed in the AOV network to meet the characteristics of the DAG chart, short services communication time, dynamic allocation of service priorities. Experiments prove that: the model by studying the Apriori algorithm proposed "preferential attachment-merge pruning algorithm for mining" partial depth based on MapReduce, using the "density grid attunements connected region algorithm" deep into the MapReduce technology, so as to better complete the service node partition and data merge.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - X Cheng AU - W.H. Li PY - 2015/11 DA - 2015/11 TI - Research on intelligent service scheduling model based on service level optimization BT - 2015 International Conference on Industrial Technology and Management Science PB - Atlantis Press SN - 2352-538X UR - https://doi.org/10.2991/itms-15.2015.418 DO - https://doi.org/10.2991/itms-15.2015.418 ID - Cheng2015/11 ER -