Workload-aware storage policies for cloud object storage

被引:0
作者
Chen, Yu [1 ]
Tong, Wei [1 ]
Feng, Dan [1 ]
Wang, Zike [1 ]
机构
[1] Huazhong Univ Sci & Technol, Engn Res Ctr Data Storage Syst & Technol, Sch Comp Sci & Technol, Wuhan Natl Lab Optoelect,Key Lab Informat Storage, Wuhan, Hubei, Peoples R China
关键词
Cloud object storage; Software-defined storage; Middleware; Multi-tenancy; Resource management;
D O I
10.1016/j.jpdc.2022.01.026
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Different applications have different access characteristics and various performance requirements. Thus, the shared cloud object store entails providing tenant-specific policies. However, the limited configurability of existing storage policies makes it difficult to provide efficient and flexible policies to meet tenants' evolving needs. First, existing policies that only control request forwarding cannot provide sufficient optimizations for workload performance. Second, those policies lack the flexibility to adapt to the possible workload changes during runtime. In this paper, we propose Mass, a programmable framework to provide the enhanced storage policies for diverse workloads based on their access characteristics. We also design its enhancements, C-Mass, extending Mass's capabilities through container-based policy deployment to efficiently handle workload changes. Compared with existing storage policies, the latency and throughput of workloads under Mass are improved by up to 81.6% and 231.5%, respectively. Further, the workload performance under C-Mass is optimized by up to 40%.& nbsp;(c) 2022 Elsevier Inc. All rights reserved.
引用
收藏
页码:232 / 247
页数:16
相关论文
共 59 条
[31]  
Merkel D., 2014, Linux Journal, V2014
[32]   Too Big to Eat: Boosting Analytics Data Ingestion from Object Stores with Scoop [J].
Moatti, Yosef ;
Rom, Eran ;
Gracia-Tinedo, Raul ;
Naor, Dalit ;
Chen, Doron ;
Sampe, Josep ;
Sanchez-Artigas, Marc ;
Garcia-Lopez, Pedro ;
Gluszak, Filip ;
Deschodt, Eric ;
Pace, Francesco ;
Venzano, Daniele ;
Michiardi, Pietro .
2017 IEEE 33RD INTERNATIONAL CONFERENCE ON DATA ENGINEERING (ICDE 2017), 2017, :309-320
[33]   Ambry: LinkedIn's Scalable Geo-Distributed Object Store Ambry: LinkedIn's Scalable Geo-Distributed Object Store [J].
Noghabi, Shadi A. ;
Subramanian, Sriram ;
Narayanan, Priyesh ;
Narayanan, Sivabalan ;
Holla, Gopalakrishna ;
Zadeh, Mammad ;
Li, Tianwei ;
Gupta, Indranil ;
Campbell, Roy H. .
SIGMOD'16: PROCEEDINGS OF THE 2016 INTERNATIONAL CONFERENCE ON MANAGEMENT OF DATA, 2016, :253-265
[34]   Wiera: Policy-Driven Multi-Tiered Geo-Distributed Cloud Storage System [J].
Oh, Kwangsung ;
Qin, Nan ;
Chandra, Abhishek ;
Weissman, Jon .
IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, 2020, 31 (02) :294-305
[35]  
Qing Zheng, 2012, 2012 IEEE 5th International Conference on Cloud Computing (CLOUD), P998, DOI 10.1109/CLOUD.2012.52
[36]  
Quamar Abdul, 2013, PROC 16 INT C EXTEND, P430, DOI DOI 10.1145/2452376.2452427
[37]  
Rafiq Arif, 2017, CHINA PAKISTAN EC CO, P1
[38]   SCOPE: self-adaptive and policy-based data management middleware for federated clouds [J].
Rafique, Ansar ;
Van Landuyt, Dimitri ;
Truyen, Eddy ;
Reniers, Vincent ;
Joosen, Wouter .
JOURNAL OF INTERNET SERVICES AND APPLICATIONS, 2019, 10 (01)
[39]   Tiera: Towards Flexible Multi-Tiered Cloud Storage Instances [J].
Raghavan, Ajaykrishna ;
Chandra, Abhishek ;
Weissman, Jon B. .
ACM/IFIP/USENIX MIDDLEWARE 2014, 2014, :1-12
[40]   IndexFS: Scaling File System Metadata Performance with Stateless Caching and Bulk Insertion [J].
Ren, Kai ;
Zheng, Qing ;
Patil, Swapnil ;
Gibson, Garth .
SC14: INTERNATIONAL CONFERENCE FOR HIGH PERFORMANCE COMPUTING, NETWORKING, STORAGE AND ANALYSIS, 2014, :237-248