Dynamic Resource Partitioning for Heterogeneous Multi-Core-Based Cloud Computing in Smart Cities
As the smart cities emerged for more comfortable urban spaces, services, such as health, transportation, and so on, need to be promoted. In addition, the cloud computing provides flexible allocation, migration of services, and better security isolation; therefore, it is the infrastructure for the sm...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2016-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/7352309/ |
| _version_ | 1818641519955935232 |
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| author | Gangyong Jia Guangjie Han Jinfang Jiang Ning Sun Kun Wang |
| author_facet | Gangyong Jia Guangjie Han Jinfang Jiang Ning Sun Kun Wang |
| author_sort | Gangyong Jia |
| collection | DOAJ |
| description | As the smart cities emerged for more comfortable urban spaces, services, such as health, transportation, and so on, need to be promoted. In addition, the cloud computing provides flexible allocation, migration of services, and better security isolation; therefore, it is the infrastructure for the smart cities. Single instruction-set architecture (ISA) heterogeneous multi-core processors have higher performance per watt than their symmetric counterparts and are popular in current processors. In current cloud computing, which integrates a few fast out-of-order cores, coupled with a large number of simpler, slow cores, all cores expose the same ISA. The best way to leverage the effectiveness of these systems is to accelerate sequential CPU-bound threads using fast cores, and to improve the throughput of parallel memory-bound threads using slow cores. However, shared hardware resources, such as memory, respond to requests from all cores, which interfere with each other, leading to both low speed for fast cores and low throughput for slow cores. In this paper, we propose a dynamic resource partitioning (DRP) method for single-ISA heterogeneous multi-cores, which partitions the shared resources according to both threads' requirements for the shared resources and the performance of their running cores. The key principle is to profile both threads' resource characteristics at run-time and the performance of the cores that the threads are running on to estimate demands for resources. Then, we use the estimation to direct our resource partitioning. Moreover, we integrate our DRP with current memory scheduling policies to improve the system performance further for the two methods being orthogonal. |
| first_indexed | 2024-12-16T23:28:28Z |
| format | Article |
| id | doaj.art-bf27a34c97234553a2d1b27302140eea |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T23:28:28Z |
| publishDate | 2016-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-bf27a34c97234553a2d1b27302140eea2022-12-21T22:11:57ZengIEEEIEEE Access2169-35362016-01-01410811810.1109/ACCESS.2015.25075767352309Dynamic Resource Partitioning for Heterogeneous Multi-Core-Based Cloud Computing in Smart CitiesGangyong Jia0Guangjie Han1Jinfang Jiang2Ning Sun3Kun Wang4Department of Computer Science, Hangzhou Dianzi University, Hangzhou, ChinaDepartment of Information and Communication Systems, Hohai University, Changzhou, ChinaDepartment of Information and Communication Systems, Hohai University, Changzhou, ChinaDepartment of Information and Communication Systems, Hohai University, Changzhou, ChinaKey Laboratory of Broadband Wireless Communication and Sensor Network Technology, Ministry of Education, Nanjing University of Posts and Telecommunications, Nanjing, ChinaAs the smart cities emerged for more comfortable urban spaces, services, such as health, transportation, and so on, need to be promoted. In addition, the cloud computing provides flexible allocation, migration of services, and better security isolation; therefore, it is the infrastructure for the smart cities. Single instruction-set architecture (ISA) heterogeneous multi-core processors have higher performance per watt than their symmetric counterparts and are popular in current processors. In current cloud computing, which integrates a few fast out-of-order cores, coupled with a large number of simpler, slow cores, all cores expose the same ISA. The best way to leverage the effectiveness of these systems is to accelerate sequential CPU-bound threads using fast cores, and to improve the throughput of parallel memory-bound threads using slow cores. However, shared hardware resources, such as memory, respond to requests from all cores, which interfere with each other, leading to both low speed for fast cores and low throughput for slow cores. In this paper, we propose a dynamic resource partitioning (DRP) method for single-ISA heterogeneous multi-cores, which partitions the shared resources according to both threads' requirements for the shared resources and the performance of their running cores. The key principle is to profile both threads' resource characteristics at run-time and the performance of the cores that the threads are running on to estimate demands for resources. Then, we use the estimation to direct our resource partitioning. Moreover, we integrate our DRP with current memory scheduling policies to improve the system performance further for the two methods being orthogonal.https://ieeexplore.ieee.org/document/7352309/Single-ISA Heterogeneous Multi-corecloud computingperformance per wattdynamic resource partitioningmemory schedulingperformance |
| spellingShingle | Gangyong Jia Guangjie Han Jinfang Jiang Ning Sun Kun Wang Dynamic Resource Partitioning for Heterogeneous Multi-Core-Based Cloud Computing in Smart Cities IEEE Access Single-ISA Heterogeneous Multi-core cloud computing performance per watt dynamic resource partitioning memory scheduling performance |
| title | Dynamic Resource Partitioning for Heterogeneous Multi-Core-Based Cloud Computing in Smart Cities |
| title_full | Dynamic Resource Partitioning for Heterogeneous Multi-Core-Based Cloud Computing in Smart Cities |
| title_fullStr | Dynamic Resource Partitioning for Heterogeneous Multi-Core-Based Cloud Computing in Smart Cities |
| title_full_unstemmed | Dynamic Resource Partitioning for Heterogeneous Multi-Core-Based Cloud Computing in Smart Cities |
| title_short | Dynamic Resource Partitioning for Heterogeneous Multi-Core-Based Cloud Computing in Smart Cities |
| title_sort | dynamic resource partitioning for heterogeneous multi core based cloud computing in smart cities |
| topic | Single-ISA Heterogeneous Multi-core cloud computing performance per watt dynamic resource partitioning memory scheduling performance |
| url | https://ieeexplore.ieee.org/document/7352309/ |
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