Journal of East China Normal University(Natural Science) ›› 2022, Vol. 2022 ›› Issue (5): 73-89.doi: 10.3969/j.issn.1000-5641.2022.05.007
• Evaluation Methods and Tools for Supply Chain Platform • Previous Articles Next Articles
Shuhong YOU1, Qian SU2, Rong ZHANG1,*()
Received:
2022-07-16
Accepted:
2022-07-16
Online:
2022-09-25
Published:
2022-09-26
Contact:
Rong ZHANG
E-mail:rzhang@dase.ecnu.edu.cn
CLC Number:
Shuhong YOU, Qian SU, Rong ZHANG. Dynamic simulation for cloud database runtime environment[J]. Journal of East China Normal University(Natural Science), 2022, 2022(5): 73-89.
Table 1
Summary of work related to runtime environment simulation"
相关工作 | 模拟通用环境 | 模拟极端环境 | 模拟范围 | 缺陷 |
混沌测试工具 | | | 硬件资源损坏 | 缺乏通用性、动态性 |
单一资源模拟工具 | | | 特定应用程序的工作负载 | 不适合用于模拟实际业务场景中的运行环境 |
文献[25] | | | 消耗CPU、内存、磁盘 | 缺乏全面性、动态性、准确性 |
文献[26] | | | 消耗CPU、内存、磁盘; 抢占网络带宽 | 缺乏全面性、准确性 |
Table 4
Dynamic environment simulation workload"
行号 | 负载 |
1 | CONCURRENT_EXEC; |
2 | CPU_OCCUPY[session;tikv-0;7,4,2;30,30,30];//分三阶段占用CPU, 每个占用周期是30 s |
3 | DISK_OCCUPY_IO[session;tikv-1;sequence_read;3000,1000;30,30];//分两阶段占用磁盘读I/O, 每个占用周期是30 s |
4 | THREAD_KILL[session;tikv-1;tikv-server];//终止该节点的tikv进程 |
5 | MID_CONCURRENT; |
6 | NET_LIMIT_BW[session;tidb-0;eth0;80,90,10;30,30,30];//分三阶段限制网络带宽, 每个限制周期是30 s |
7 | DISK_OCCUPY_IO[session;tikv-2;sequence_read;3000,1000;30,30]; |
8 | THREAD_KILL[session;tikv-2;tikv-server]; |
9 | END_ CONCURRENT; |
1 | BULAO J. How many companies use cloud computing in 2022? All you need to know [EB/OL]. (2022-07-08)[2022-07-10]. https://techjury.net/blog/how-many-companies-use-cloud-computing. |
2 | IBM Cloud Education. Containerization [EB/OL]. (2021-06-23)[2022-07-10]. https://www.ibm.com/cloud/learn/containerization. |
3 | The Kubernetes Authors. Kubernetes [EB/OL]. [2022-07-10]. https://kubernetes.io. |
4 | VERBITSKI A, GUPTA A, SAHA D, et al. Amazon aurora: Design considerations for high throughput cloud-native relational databases [C]// Proceedings of the 2017 ACM International Conference on Management of Data. 2017: 1041-1052. |
5 | VERBITSKI A, GUPTA A, SAHA D, et al. Amazon aurora: On avoiding distributed consensus for I/Os, commits, and membership changes [C]// Proceedings of the 2018 International Conference on Management of Data. 2018: 789-796. |
6 | CAO W, LIU Y, CHENG Z, et al. POLARDB Meets computational storage: Efficiently support analytical workloads in cloud-native relational database [C]// 18th USENIX Conference on File and Storage Technologies (FAST 20). 2020: 29-41. |
7 | ANTONOPOULOS P, BUDOVSKI A, DIACONU C, et al. Socrates: The new sql server in the cloud [C]// Proceedings of the 2019 International Conference on Management of Data. 2019: 1743-1756. |
8 | DEPOUTOVITCH A, CHEN C, CHEN J, et al. Taurus database: How to be fast, available, and frugal in the cloud [C]// Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data. 2020: 1463-1478. |
9 | MERENSTEIN A, TARASOV V, ANWAR A, et al. CNSBench: A cloud native storage benchmark [C]// 19th USENIX Conference on File and Storage Technologies (FAST 21). 2021: 263-276. |
10 | MA M, YIN Z, ZHANG S, et al. Diagnosing root causes of intermittent slow queries in cloud databases. Proceedings of the VLDB Endowment, 2020, 13 (8): 1176- 1189. |
11 | GitHub Inc. Woodpecker [EB/OL]. (2021-06-02)[2022-07-10]. https://github.com/DBHammer/Woodpecker. |
12 | GitHub Inc. Chaosblade [EB/OL]. (2021-07-18)[2022-07-10]. https://github.com/chaosblade-io/chaosblade. |
13 | GitHub Inc. Chaos-mesh [EB/OL]. [2022-07-10]. https://github.com/chaos-mesh. |
14 | MATTHEWS J N, HU W, HAPUARACHCHI M, et al. Quantifying the performance isolation properties of virtualization systems [C]// Proceedings of the 2007 Workshop on Experimental Computer Science. 2007: 6-15. |
15 | FELTER W, FERREIRA A, RAJAMONY R, et al. An updated performance comparison of virtual machines and linux containers [C]// International Symposium on Performance Analysis of Systems and Software (ISPASS). New York: IEEE, 2015: 171-172. |
16 | PANDEY A. Impact of memory intensive applications on performance of cloud virtual machine [C]// 2014 Recent Advances in Engineering and Computational Sciences (RAECS). New York: IEEE, 2014: 1-6. |
17 | BUSARI M, WILLIAMSON C. ProWGen: A synthetic workload generation tool for simulation evaluation of web proxy caches. Computer Networks, 2002, 38 (6): 779- 794. |
18 | JIN S, BESTAVROS A. Gismo: A generator of internet streaming media objects and workloads. ACM SIGMETRICS Performance Evaluation Review, 2001, 29 (3): 2- 10. |
19 | KAO W I, IYER R K. A user-oriented synthetic workload generator [C]// 1992 12th International Conference on Distributed Computing System. New York: IEEE, 1992: 270-277. |
20 | HILL J H. An architecture independent approach to emulating computation intensive workload for early integration testing of enterprise DRE systems [C]// OTM Confederated International Conferences. Berlin, Heidelberg: Springer, 2009: 744-759. |
21 | MILLER J E, KASTURE H, KURIAN G, et al. Graphite: A distributed parallel simulator for multicores [C]// HPCA-16 2010 The Sixteenth International Symposium on High-Performance Computer Architecture. New York: IEEE, 2010: 1-12. |
22 | CARLSON T E, HEIRMAN W, EECKHOUT L. Sniper: Exploring the level of abstraction for scalable and accurate parallel multi-core simulation [C]// Proceedings of 2011 International Conference for High Performance Computing, Networking, Storage and Analysis. 2011: 1-12. |
23 | SANCHEZ D, KOZYRAKIS C. ZSim: Fast and accurate microarchitectural simulation of thousand-core systems. ACM SIGARCH Computer Architecture News, 2013, 41 (3): 475- 486. |
24 | WANG J, BEU J, BHEDA R, et al. Manifold: A parallel simulation framework for multicore systems [C]// 2014 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS). New York: IEEE, 2014: 106-115. |
25 | JEONG H, LEE S. A workload generator for database system benchmarks [C]// International Conference on Information Integration and Web-based Applications & Services (iiWAS). 2005: 813-822. |
26 | ZHANG C, ZHANG R, SU Q, et al. Dynamic environment simulation for database performance evaluation [C]// Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint International Conference on Web and Big Data. Cham, Switzerland: Springer, 2021: 180-189. |
27 | GitHub Inc. Fio [EB/OL]. [2022-07-10]. https://github.com/axboe/fio. |
28 | HUANG D, LIU Q, CUI Q, et al. TiDB: A raft-based HTAP database. Proceedings of the VLDB Endowment, 2020, 13 (12): 3072- 3084. |
29 | GRIFFITHS N. nmon Performance: A free tool to analyze AIX and Linux performance [EB/OL]. (2006-02-27) [2022-07-10]. http://www.52testing.com/html/44/175444-87137.html. |
30 | TPC. TPC-C [EB/OL]. [2022-07-10]. http://www.tpc.org/tpcc/. |
[1] | Wei JIAN, Zirui HU, Rong ZHANG. Online analytical processing query cardinality estimation capability evaluation [J]. Journal of East China Normal University(Natural Science), 2024, 2024(5): 141-151. |
[2] | Xuechao LIAN, Wei LIU, Qingshuai WANG, Rong ZHANG. Design and optimization of high-contention transaction processing architecture [J]. Journal of East China Normal University(Natural Science), 2023, 2023(6): 28-38. |
[3] | Congcong WANG, Huiqi HU. Persistent memory- and shared cache architecture-based high-performance database [J]. Journal of East China Normal University(Natural Science), 2023, 2023(5): 1-10. |
[4] | Rong YU, Panfei YANG, Qingshuai WANG, Rong ZHANG. An HTAP database prototype with an adaptive data synchronization [J]. Journal of East China Normal University(Natural Science), 2023, 2023(5): 11-25. |
[5] | Danqi LIU, Peng CAI. Separate management strategies for Part metadata under the storage-computing separation architecture [J]. Journal of East China Normal University(Natural Science), 2023, 2023(5): 40-50. |
[6] | Xiyu LU, Wei LIU, Siyang WENG, Keqiang LI, Rong ZHANG. Generating diverse database isolation level test cases with fuzzy testing [J]. Journal of East China Normal University(Natural Science), 2023, 2023(5): 51-64. |
[7] | Ge GAO, Huiqi HU. FeaDB: In-memory based multi-version online feature store [J]. Journal of East China Normal University(Natural Science), 2023, 2023(5): 65-76. |
[8] | Xiansen CHEN, Chen XU. Acceleration technique for heterogeneous operators based on openGauss [J]. Journal of East China Normal University(Natural Science), 2023, 2023(5): 90-99. |
[9] | Shuai ZHANG, Huiqi HU, Yaoqiang XU, Xuan ZHOU. Time series database query optimization for anomaly detection [J]. Journal of East China Normal University(Natural Science), 2023, 2023(2): 119-131. |
[10] | Ting CHEN, Zhaokun XIANG, Jinkai XU, Rong ZHANG. Benchmarking join order selection of query optimizers [J]. Journal of East China Normal University(Natural Science), 2022, 2022(5): 48-60. |
[11] | Zhaokun XIANG, Ting CHEN, Qian SU, Rong ZHANG. A fuzzer for query processing functionality of OLAP databases [J]. Journal of East China Normal University(Natural Science), 2021, 2021(5): 74-83. |
[12] | Wenxin LIU, Peng CAI. Partition-based concurrency control in a multi-master database [J]. Journal of East China Normal University(Natural Science), 2021, 2021(5): 84-93. |
[13] | YANG Wencan, HU Huiqi, DUAN Huichao, HU Yaoyi, QIAN Weining. CedarAdvisor: A load-adaptive automatic indexing recommendation tool [J]. Journal of East China Normal University(Natural Science), 2020, 2020(6): 52-62. |
[14] | WEI Xiaoxian, LIU Wenxin, CAI Peng. Global transaction log of a multi-master cloud database [J]. Journal of East China Normal University(Natural Science), 2020, 2020(5): 10-20. |
[15] | LIU Zi-hao, HU Hui-qi, XU Rui, ZHOU Xuan. Implementation of LevelDB-based secondary index on two-dimensional data [J]. Journal of East China Normal University(Natural Sc, 2019, 2019(5): 159-167. |
Viewed | ||||||
Full text |
|
|||||
Abstract |
|
|||||