期刊文献+

一种多租户云数据存储缓存管理机制 被引量:7

A Multi-Tenant Memory Management Mechanism for Cloud Data Storage
下载PDF
导出
摘要 随着云计算的普及,软件即服务(software as a service,SaaS)逐渐成为云计算的一种重要表现形式.云中数据节点的缓存是提高多租户应用数据访问性能的一种重要资源,缓存资源的共享和分配受到SaaS提供商的关注.对SaaS提供商而言,如何在多租户间有效地分配数据节点上的缓存资源,从而满足租户的服务水平协议(service level agreement,SLA),获得更高的收益已成为一项挑战.为此,提出了多租户云数据存储缓存管理机制,以实现服务提供商收益最大化的目标,结合SLA收益模型,评估不同缓存策略下服务提供商获取的收益值,将全局缓存管理问题定义为目标优化问题,并结合缓存分配特点,采用优化的遗传算法解决该问题.通过实验比较,该方法能保证SaaS服务提供商在多租户间有效利用缓存资源获取高收益. With the popularization of cloud computing, software as a service (SaaS) has become an important form of cloud computing. Memory resource owned by each data node in the cloud is a key resource to improve data access performance of multi-tenant applications. Therefore, memory resource share and provisioning have received a lot of attention from SaaS providers. For the service providers, how to reasonably allocate memory resource in each data node in order to obtain higher profits while guaranteeing tenants' service level agreement (SLA) has become a challenge. Addressing the challenge, we propose a framework of multi-tenant memory management (MTMM) for cloud data storage and corresponding memory allocation method. The method takes the maximum profits service provider can obtain as a target. Combined with tenants' SLA profit models, the global memory allocation problem is analyzed and modeled as an objective optimal problem. Corresponding the profits service provider can get under different memory allocation strategies are predicted through it. Considering the characteristics of multi-tenant memory allocation, we solve the problem by optimized genetic algorithm in order to improve the performance of the method. Compared with the traditional LRU method and multi-tenant memory allocation method employed in single node, the mechanism proposed in this paper can effectively manage memory and provide higher profits for service providers.
作者 史玉良 王捷
出处 《计算机研究与发展》 EI CSCD 北大核心 2014年第11期2528-2537,共10页 Journal of Computer Research and Development
基金 国家自然科学基金项目(61272241 61303085) 山东大学自主创新基金项目(2012TS074)
关键词 软件即服务 多租户 缓存管理 云数据存储 服务水平协议 software as a service (SaaS) multi-tenancy memory management cloud data storage service level agreement (SLA)
  • 相关文献

参考文献17

  • 1Aulbach S, Jacobs D, Kemper A, et al. A comparison of flexible schemas for software as a service [C] //Proc of the 2009 ACM SIGMOD Int Conf on Management of Data. New York: ACM, 2009:881-888.
  • 2Aulbach S, Grust T, Jacobs D, etal. Multi-tenant databases for software as a service: Schema-mapping techniques [C] // Proc of the 2008 ACM SIGMOD Int Conf on Management of Data. New York: ACM, 2008:1195-1206.
  • 3姚金成,张世栋,史玉良,李庆忠.基于Chunk Folding的多租户数据库缓存管理机制[J].计算机学报,2011,34(12):2319-2331. 被引量:11
  • 4Thiebaut D, Stone H S, Wolf J L. Improving disk cache hit ratios through cache partitioning [J]. IEEE Trans on Computers, 1992, 41(6) : 665-676.
  • 5Storm A J, Garcia-Arellano C, Lightstone S S, et al. Adaptive self-tuning memory in DB2 [C] //Proc of the 32nd Int Conf on Very Large Data Bases. New York: A(2M, 2006:1081-1092.
  • 6Goyal P, Jadav D, Modaha D S, et al. CacheCOW: QoS for storage system caches [C] //Proc of the llth Int Conf on Quality of Service. Berlin: Springer, 2003:498-515.
  • 7Suh G E, Rudolph L, Devadas S. Dynamic partitioning of shared cache memory [J]. Journal of Supercomputing, 2004, 28(1) : 7-26.
  • 8Patrick C M, Garg R, Son S W, et al. Improving I/O performance using soft-QoS-based dynamic storage cache partitioning [C] //Proc of IEEE Int Conf on Cluster Computing and Workshops (CLUSTER'09). Piscataway, NJ: IEEE, 2009:1-10.
  • 9Chaudhuri S. What next? A half-dozen data management research goals for big data and the cloud [C] //Proe of the 31st Symp on Principles of Database Systems (PODS'12). New York: ACM, 2012:1-4.
  • 10林海略,韩燕波.多租户应用的性能管理关键问题研究[J].计算机学报,2010,33(10):1881-1895. 被引量:45

二级参考文献34

  • 1GuoCJ, SunW, Huang Y, Wang Z H, Gao B. A framework for native multi-tenancy application development and management//Proceedings of the 9th IEEE International Conference on E-Commerce Technology and the 4th IEEE International Conference on Enterprise Computing, E-Commerce and E Services(CEC-EEE'07). 2007: 551-558.
  • 2Weissman C D, Bobrowski S. The design of the force, com multitenant internet application envelopment platform//Proceedings of the 35th SIGMOD International Conference on Management of Data (SIGMOD' 09). Providence, Rhode Island, USA, 2009:889- 896.
  • 3Aulbach S, Grust T, Jacobs D, Kemper A, Rittinger J. Multi-tenant databases for software as a service: Schemamapping techniques//Proceedings of the 34th SIGMOD In- ternational Conference on Management of Data (SIGMOD' 08). Vancouver, BC, Canada, 2008: 1195-1206.
  • 4Amza C, Ch A, Cox A L, Elnikety S, Gil R, Rajamani K, Zwaencpoel W. Specification and implementation of dynamic Web site benchmarks//Proceedings of the 5th IEEE Work shop on Workload Characterization (WWC' 02). Austin, Texas, USA, 2002. 147- 156.
  • 5http: //www. humanbenehmark, com/tests/reactiontime/in dex. php, link retrieved on 2010-06-24.
  • 6Soundararajan G, Amza C. Online data migration for auto nomic provisioning of databases in dynamic content Web serw ers//Proceedings of the 15th Annual International Conference on Computer Science and Software Engineering (CAS CON'05). Richmond Hill, ON, Canada, 2005: 268-282.
  • 7Ibaraki T, Katoh N. Resource Allocation Problems: Algorithmic Approaches. Cambridge, MA USA: MIT Press, 1988.
  • 8Arlitt M, Jin T. Workload characterization of the 1998 world cup web site. HP Laboratories Palo Alto, 1999.
  • 9yon Behren J R, Condit J, Brewer E A. Why events are a bad idea (for High Concurrency Servers)//Proceedings of the 9th Workshop on Hot Topics in Operating Systems (HotOS' 03). Lihue, Hawaii, USA, 2003:19 24.
  • 10Ousterhout J K. Why threads are a bad idea(for Most Purposes)//Proceedings of the Keynotes of the USENIX Winter Technical Conference(USENIX'96). San Diego, CA, USA, 1996.

共引文献53

同被引文献54

引证文献7

二级引证文献15

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部