Incorporating cost of control into the design of a load balancing controller
Incorporating cost of control into the design of a load balancing controller
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将控制成本纳入负载平衡控制器的设计中
DOI：
10.1109/rttas.2004.1317284
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发表时间：
2004
期刊：
影响因子：
--
通讯作者：
Christian Garcia
中科院分区：
文献类型：
--
作者：
Y. Diao;J. Hellerstein;Adam J. Storm;M. Surendra;S. Lightstone;S. Parekh;Christian Garcia
负载平衡作为一种通过减少瓶颈利用来优化性能的方式被广泛用于计算系统中，例如调整缓冲池的大小以平衡数据库管理系统中的资源需求。负载平衡通常被视为一个约束优化问题，其中只考虑负载平衡的好处。然而，控制成本也很重要。在这里，我们研究的价值，包括在控制器的设计之间的权衡成本的瞬态不平衡的资源利用率和改变资源分配的成本。后者的一个例子是可以减少吞吐量的操作，例如缓存缓冲池。这是因为对内存立即减少的池中的数据的请求具有较长的访问时间，而对内存增加的池中的数据的请求必须在访问时间减少之前用来自磁盘的数据填充该内存。我们的框架，我们的研究控制成本的广泛使用的线性二次型调节器（LQR）。我们开发了一个成本模型，使我们能够指定的LQR Q和R矩阵的基础上改变资源分配和瞬态负载不平衡对系统性能的影响。我们的DB2通用数据库服务器使用基准测试的在线事务处理和决策支持工作负载的研究表明，将我们的成本模型到MIMO LQR控制器的结果在14%的性能提高超过实现动态分配的缓冲区的大小，而不适当地考虑控制的成本。
Load balancing is widely used in computing systems as a way to optimize performance by reducing bottleneck utilizations, such as adjusting the size of buffer pools to balance resource demands in a database management system. Load balancing is generally approached as a constrained optimization problem in which only the benefits of load balancing are considered. However, the costs of control are important as well. Herein, we study the value of including in controller design the trade-off between the cost of transient imbalances in resource utilizations and the cost of changing resource allocations. An example of the latter are actions such as resizing buffer pools that can reduce throughputs. This is because requests for data in pools whose memory is reduced immediately have longer access times whereas requests for data in pools whose memory is increased must fill this memory with data from disk before accessed times are reduced. We frame our study of control costs in terms of the widely used linear quadratic regulator (LQR). We develop a cost model that allows us to specify the LQR Q and R matrices based on the impact on system performance of changing resource allocations and transient load imbalances. Our studies of a DB2 universal database server using benchmarks for online transaction processing and decision support workloads show that incorporating our cost model into the MIMO LQR controller results in a 14% improvement in performance beyond that achieved by dynamically allocating the size of buffers without properly considering the cost of control.
DOI：
10.1109/cdc.2000.912056
发表时间：
2000-12
期刊：
Proceedings of the 39th IEEE Conference on Decision and Control (Cat. No.00CH37187)
影响因子：
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作者：
H. Kameda;S. F. El-Zoghdy;I. Ryu;Jie Li
通讯作者：
H. Kameda;S. F. El-Zoghdy;I. Ryu;Jie Li