Efficient Dynamic Deployment of Simulation Tasks in Collaborative Cloud and Edge Environments
Cloud computing has been studied and used extensively in many scenarios for its nearly unlimited resources and X as a service model. To reduce the latency for accessing the remote cloud data centers, small data centers or cloudlets are deployed near end-users, which is also called edge computing. In...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-02-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/12/3/1646 |
| _version_ | 1797489021229727744 |
|---|---|
| author | Miao Zhang Peng Jiao Yong Peng Quanjun Yin |
| author_facet | Miao Zhang Peng Jiao Yong Peng Quanjun Yin |
| author_sort | Miao Zhang |
| collection | DOAJ |
| description | Cloud computing has been studied and used extensively in many scenarios for its nearly unlimited resources and X as a service model. To reduce the latency for accessing the remote cloud data centers, small data centers or cloudlets are deployed near end-users, which is also called edge computing. In this paper, we mainly focus on the efficient scheduling of distributed simulation tasks in collaborative cloud and edge environments. Since simulation tasks are usually tightly coupled with each other by sending many messages and the status of tasks and hosts may also change frequently, it is essentially a dynamic bin-packing problem. Unfortunately, popular methods, such as meta-heuristics, and accurate algorithms are time-consuming and cannot deal with the dynamic changes of tasks and hosts efficiently. In this paper, we present Pool, an incremental flow-based scheduler, to minimize the overall communication cost of all tasks in a reasonable time span with the consideration of migration cost of task. After formulating such a scheduling problem as a min-cost max-flow (MCMF) problem, incremental MCMF algorithms are adopted to accelerate the procedure of calculating an optimal flow and heuristic scheduling algorithm, with the awareness of task migration cost, designed to assign tasks. Simulation experiments on Alibaba cluster trace show that Pool can schedule all of the tasks efficiently and is almost 5.8 times faster than the baseline method when few tasks and hosts change in the small problem scale. |
| first_indexed | 2024-03-10T00:10:35Z |
| format | Article |
| id | doaj.art-de7dbf45c6304c0c96c5b3a7df21e38c |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T00:10:35Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-de7dbf45c6304c0c96c5b3a7df21e38c2023-11-23T16:01:05ZengMDPI AGApplied Sciences2076-34172022-02-01123164610.3390/app12031646Efficient Dynamic Deployment of Simulation Tasks in Collaborative Cloud and Edge EnvironmentsMiao Zhang0Peng Jiao1Yong Peng2Quanjun Yin3College of Systems Engineering, National University of Defense Technology, Changsha 410073, ChinaCollege of Systems Engineering, National University of Defense Technology, Changsha 410073, ChinaCollege of Systems Engineering, National University of Defense Technology, Changsha 410073, ChinaCollege of Systems Engineering, National University of Defense Technology, Changsha 410073, ChinaCloud computing has been studied and used extensively in many scenarios for its nearly unlimited resources and X as a service model. To reduce the latency for accessing the remote cloud data centers, small data centers or cloudlets are deployed near end-users, which is also called edge computing. In this paper, we mainly focus on the efficient scheduling of distributed simulation tasks in collaborative cloud and edge environments. Since simulation tasks are usually tightly coupled with each other by sending many messages and the status of tasks and hosts may also change frequently, it is essentially a dynamic bin-packing problem. Unfortunately, popular methods, such as meta-heuristics, and accurate algorithms are time-consuming and cannot deal with the dynamic changes of tasks and hosts efficiently. In this paper, we present Pool, an incremental flow-based scheduler, to minimize the overall communication cost of all tasks in a reasonable time span with the consideration of migration cost of task. After formulating such a scheduling problem as a min-cost max-flow (MCMF) problem, incremental MCMF algorithms are adopted to accelerate the procedure of calculating an optimal flow and heuristic scheduling algorithm, with the awareness of task migration cost, designed to assign tasks. Simulation experiments on Alibaba cluster trace show that Pool can schedule all of the tasks efficiently and is almost 5.8 times faster than the baseline method when few tasks and hosts change in the small problem scale.https://www.mdpi.com/2076-3417/12/3/1646distributed simulationtask schedulingminimum cost maximum flowincremental scheduling |
| spellingShingle | Miao Zhang Peng Jiao Yong Peng Quanjun Yin Efficient Dynamic Deployment of Simulation Tasks in Collaborative Cloud and Edge Environments Applied Sciences distributed simulation task scheduling minimum cost maximum flow incremental scheduling |
| title | Efficient Dynamic Deployment of Simulation Tasks in Collaborative Cloud and Edge Environments |
| title_full | Efficient Dynamic Deployment of Simulation Tasks in Collaborative Cloud and Edge Environments |
| title_fullStr | Efficient Dynamic Deployment of Simulation Tasks in Collaborative Cloud and Edge Environments |
| title_full_unstemmed | Efficient Dynamic Deployment of Simulation Tasks in Collaborative Cloud and Edge Environments |
| title_short | Efficient Dynamic Deployment of Simulation Tasks in Collaborative Cloud and Edge Environments |
| title_sort | efficient dynamic deployment of simulation tasks in collaborative cloud and edge environments |
| topic | distributed simulation task scheduling minimum cost maximum flow incremental scheduling |
| url | https://www.mdpi.com/2076-3417/12/3/1646 |
| work_keys_str_mv | AT miaozhang efficientdynamicdeploymentofsimulationtasksincollaborativecloudandedgeenvironments AT pengjiao efficientdynamicdeploymentofsimulationtasksincollaborativecloudandedgeenvironments AT yongpeng efficientdynamicdeploymentofsimulationtasksincollaborativecloudandedgeenvironments AT quanjunyin efficientdynamicdeploymentofsimulationtasksincollaborativecloudandedgeenvironments |