Penalized Graph Partitioning Based Allocation Strategy for Database-as-a-Service Systems

Penalized Graph Partitioning Based Allocation Strategy for Database-as-a-Service Systems
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数据库即服务系统基于惩罚图分区的分配策略

DOI:
10.1145/3006299.3006300
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发表时间:
2016
期刊:
2016 IEEE/ACM 3rd International Conference on Big Data Computing Applications and Technologies (BDCAT)
影响因子:
--
通讯作者:
Wolfgang Lehner
Wolfgang Lehner
中科院分区:
--
文献类型:
--
作者:
Tim Kiefer;Dirk Habich;Wolfgang Lehner

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数据库即服务(DBaaS)将云计算的优势转移到数据管理系统中,这对于大数据时代至关重要。DBaaS系统中的分配,即从数据库到基础设施节点的映射,会影响系统的性能、利用率和成本效益。将数据库和底层基础设施建模为加权图,并使用图分区和映射算法产生分配策略。然而,图分区假设单个顶点的权重(线性)相加为分区权重。实际上,由于硬件、操作系统资源或DBMS组件上的争用,性能通常不会随着工作量的增加而线性扩展。为了克服这一问题,本文提出了一种基于惩罚图划分的分配策略。我们展示了如何对具有非线性划分权重的图修改现有算法,即顶点权重不与划分权重线性相加。我们在一个32个节点上有1000个数据库的DBaaS系统中实验地评估了我们的分配策略。
Databases as a service (DBaaS) transfer the advantages of cloud computing to data management systems, which is important for the big data era. The allocation in a DBaaS system, i.e., the mapping from databases to nodes of the infrastructure, influences performance, utilization, and cost-effectiveness of the system. Modeling databases and the underlying infrastructure as weighted graphs and using graph partitioning and mapping algorithms yields an allocation strategy. However, graph partitioning assumes that individual vertex weights add up (linearly) to partition weights. In reality, performance does usually not scale linearly with the amount of work due to contention on the hardware, on operating system resources, or on DBMS components. To overcome this issue, we propose an allocation strategy based on penalized graph partitioning in this paper. We show how existing algorithms can be modified for graphs with non-linear partition weights, i.e., vertex weights that do not sum up linearly to partition weights. We experimentally evaluate our allocation strategy in a DBaaS system with 1,000 databases on 32 nodes.
使用终端传播增强数据局部性
DOI: --
发表时间: 1996
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