基于预取的磁盘存储系统节能技术研究
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摘要
磁盘占据数据中心数据存储的统治性地位,磁盘的节能控制对降低数据中心总运营成本和节能减排都具有重要意义。然而,磁盘的节能控制在实际系统的运用中却存在诸多困难和挑战。磁盘能耗状态的转换过程需要耗费较长的时间和较多的电能,容易造成系统读写服务的响应时间延迟,并且会影响磁盘的使用寿命,以往基于缓冲区预取的磁盘节能方法,大多未就系统性能和磁盘寿命进行综合考虑。另一方面,对单个磁盘的节能控制会影响数据中心存储系统的整体性能。研究保证磁盘可靠性和系统总体性能的能耗感知预取技术以及磁盘存储系统的自组织节能行为,是一项重要和紧迫的研究课题。
围绕基于预取的磁盘存储系统节能技术,从理论分析、系统设计和实验测试得出了以下一些研究成果。
现有的能耗感知贪婪式预取方法PGP (Power-aware Greedy Prefetching),通过将请求数据提前读入内存增大磁盘空闲时间间隔,是一种启发式的、具有一定实际效果的磁盘节能方法。在对PGP的预取机制分析中,发现PGP未对预取的启动时间和预取长度进行分析。进一步研究发现磁盘的空闲时间序列可能因预取启动时间和预取长度变化而变化,从而导致磁盘的总节电量减少和能耗状态转换次数增加。综合磁盘本身的属性、任务序列整体性能要求、磁盘的节能以及磁盘可靠性,建立了针对大规模数据中心磁盘存储系统的能耗感知预取优化框架。
建立了针对单磁盘单数据流能耗感知预取优化模型DiscPOP,磁盘的节能目标函数和约束条件被证明为0-1整型线性规划问题。磁盘空闲时间序列过长,导致求解DiscPOP最优解的复杂度提高。贪婪分割算法是一种离线的、分而治之的策略,过滤掉磁盘空闲时间序列中的连续无效序列,将总的空闲时间序列分割成较短的子序列,通过线性规划解决器得到各个子序列的能耗感知预取最优解。提出了基于延缓开始机制的能耗感知预取在线算法,通过简单的控制条件,使得系统智能地选定一个启动点进行能耗感知预取,达到节能优化和磁盘能耗周期转换次数减少的目标。利用基于库存论的供应链管理模型,提出应用于多数据流的单磁盘能耗感知预取方法。通过对磁盘进行分组,利用单磁盘能耗感知预取最优解,提出一种2-竞争性的多磁盘能耗感知预取优化方法,并扩展至多组磁盘或者具有镜像磁盘的结构中。经实验验证,DiscPOP及其扩展方案降低了磁盘能耗并减少了磁盘能耗转换周期次数。
研究固态盘和DRAM (Dynamic Random Access Memory.动态随机存储内存)组成混合缓存结构的能耗感知预取方法。通过对多顺序流的异步预取分析,发现混合缓存中的固态盘不仅会产生严重的写放大问题,还会产生严重的交织随机读写负载。提出了三个针对混合缓存的能耗感知预取优化规则,分别是通过对顺序流进行分类动态调整预取长度和触发距离、将不同到达速率的顺序流数据分别缓存在固态盘和DRAM上以及将固态盘上同一个顺序流的部分异步预取数据缓存于DRAM中消除交织读写情况。基于这三个预取规则提出了一种启发式的、面向混合缓存的协同式自适应能耗感知预取算法CAP,并重新设计缓存设备固态盘上的页面管理机制,降低固态盘作缓存时产生的碎片程度。经实验验证,CAP提高了系统的吞吐量,减少了固态盘写入速度,并优化了磁盘上的空闲空间序列,减少了磁盘的耗电量。
提出了一种针对数据中心大规模磁盘存储系统的理想化能耗优化数据布局方法。依照数据访问的频度筛选出热点数据,并将其多个副本按照分组分别存储在各个磁盘组上,为大规模磁盘存储系统提供与能耗成比例的服务,需要打开的磁盘个数与需要提供的数据访问吞吐量成正比。利用动力学方法建立了一个针对大规模磁盘存储系统节能分析的二维元胞自动机模型。分析数据中心大规模磁盘存储系统的自组织性和自我调节能力,通过局部数据节点的能耗感知预取和数据迁移等行为,利用简单的状态转换规则,模拟和分析局部磁盘节能行为对系统整体性能和能耗的影响。实验结果表明,整个系统性能和节点状态随着局部磁盘的调控,元胞状态呈现出复杂的时空演化现象,副本个数随着负载的增加而增多并趋于稳定。在负载到达速度较低的情况下,各个磁盘的等待队列长度熵出现近似的幂律分布,整个系统的节能行为表现出一定的自组织特性。
Since disk serves as the dominant storage device in data centers, conserving disk energy plays a significant role in data center operating fee reduction, and energy-saving and emission-reduction. However, implementing disk energy conserving technique is still facing many challenges currently. On the one hand, spinning up a disk consumes much power and time. On the other hand, disk has limited power cycles. Most previous work on energy-aware prefetching ignores the relationship between system performance and disk lifetime. Besides, the change of the state of a single disk may affect the performance of the whole storage system. Therefore, to address these critical issues, efforts must be paid to study disk energy conservation considering disk reliability and system performance, and the self-organizing behaviors in disk storage system.
Power-aware Greedy Prefetching (PGP) increases disk idle intervals by greedily preloading much data to buffer. However, this might result in less energy conservation and more disk power cycles without exploiting the relationship between I/O access pattern and application pattern. Combining the tradeoff among disk power consumption, performance guarantee and disk reliability together, a power-aware prefetching framework for massive disk storage system, is proposed.
To solve single disk and single stream case, a Disk characteristic based Power-Optimal Prefetching (DiscPOP) model, is formulated as an optimization problem. DiscPOP is proved to be solved via a 0-1 Integer Linear Programming (ILP) technique. For offline cases, a Greedy Partition algorithm (GP) is proposed to divide the problem into several small ones and solve them separately via the proposed ILP algorithm. For online cases, two heuristic algorithms are proposed based on Lazy Start Power-Optimal Prefetching (LSPOP) technique. Both of them use simple threshold controlled algorithms to select a prefetching start judiciously and cautiously. A Supple Chain Management based model is borrowed to solve online multi-stream power-aware prefetching. For multi-disk case, disks are divided into n groups to achieve n-competitive power-optimal prefetching. The results show both GP and online algorithms outperforms PGP and other traditional aggressive prefetching algorithms by more disk energy conservation and less power cycles.
Conventional asynchronous prefetching incurs serious mixed random write and read load in SSD (Solid State Disk). To apply the mixed cache with SSD and DRAM (Dynamic Random Access Memory), three power-aware prefetching rules, are proposed. The prefetching degree and trigger distance of different streams are adjusted dynamically. SSD and DRAM serve different arrival ratio streams. When asynchronous prefetching is performed on SSD, some of the fetched data are placed in DRAM to eliminate mixed read and write load. Based on these three proposed rules, a Coordinated and Adaptive Prefetching (CAP) algorithm is proposed to improve multiple sequential prefetching in such hybrid caches. A page management mechanism is re-designed to reduce defragmentation in SSD. The results show CAP improve system throughput and reduce write allocations in SSD.
An ideal power-aware data placement method for massive disk storage systems is proposed based on data access frequency. The replicas are grouped to reside in different disk groups to provide power-proportional storage service. Based on the ideal data placement, a 2-D cellular automata model, named MDSCA, is proposed to analyze and emulate the dynamical behavior rules in massive disk storage systems. The simulation results show that complex temporal and spatial phenomena evolve from the adjustment of local cells by energy-aware prefetchng and data migration. The total number of replicas increases when the load becomes heavier, and it tends to a stable sate eventually. Moreover, when the load is low, it is shown that there is an approximate power law distribution of the entropy of request queue length of each disk. To a certain extent, the whole system exhibits self-organization.
围绕基于预取的磁盘存储系统节能技术,从理论分析、系统设计和实验测试得出了以下一些研究成果。
现有的能耗感知贪婪式预取方法PGP (Power-aware Greedy Prefetching),通过将请求数据提前读入内存增大磁盘空闲时间间隔,是一种启发式的、具有一定实际效果的磁盘节能方法。在对PGP的预取机制分析中,发现PGP未对预取的启动时间和预取长度进行分析。进一步研究发现磁盘的空闲时间序列可能因预取启动时间和预取长度变化而变化,从而导致磁盘的总节电量减少和能耗状态转换次数增加。综合磁盘本身的属性、任务序列整体性能要求、磁盘的节能以及磁盘可靠性,建立了针对大规模数据中心磁盘存储系统的能耗感知预取优化框架。
建立了针对单磁盘单数据流能耗感知预取优化模型DiscPOP,磁盘的节能目标函数和约束条件被证明为0-1整型线性规划问题。磁盘空闲时间序列过长,导致求解DiscPOP最优解的复杂度提高。贪婪分割算法是一种离线的、分而治之的策略,过滤掉磁盘空闲时间序列中的连续无效序列,将总的空闲时间序列分割成较短的子序列,通过线性规划解决器得到各个子序列的能耗感知预取最优解。提出了基于延缓开始机制的能耗感知预取在线算法,通过简单的控制条件,使得系统智能地选定一个启动点进行能耗感知预取,达到节能优化和磁盘能耗周期转换次数减少的目标。利用基于库存论的供应链管理模型,提出应用于多数据流的单磁盘能耗感知预取方法。通过对磁盘进行分组,利用单磁盘能耗感知预取最优解,提出一种2-竞争性的多磁盘能耗感知预取优化方法,并扩展至多组磁盘或者具有镜像磁盘的结构中。经实验验证,DiscPOP及其扩展方案降低了磁盘能耗并减少了磁盘能耗转换周期次数。
研究固态盘和DRAM (Dynamic Random Access Memory.动态随机存储内存)组成混合缓存结构的能耗感知预取方法。通过对多顺序流的异步预取分析,发现混合缓存中的固态盘不仅会产生严重的写放大问题,还会产生严重的交织随机读写负载。提出了三个针对混合缓存的能耗感知预取优化规则,分别是通过对顺序流进行分类动态调整预取长度和触发距离、将不同到达速率的顺序流数据分别缓存在固态盘和DRAM上以及将固态盘上同一个顺序流的部分异步预取数据缓存于DRAM中消除交织读写情况。基于这三个预取规则提出了一种启发式的、面向混合缓存的协同式自适应能耗感知预取算法CAP,并重新设计缓存设备固态盘上的页面管理机制,降低固态盘作缓存时产生的碎片程度。经实验验证,CAP提高了系统的吞吐量,减少了固态盘写入速度,并优化了磁盘上的空闲空间序列,减少了磁盘的耗电量。
提出了一种针对数据中心大规模磁盘存储系统的理想化能耗优化数据布局方法。依照数据访问的频度筛选出热点数据,并将其多个副本按照分组分别存储在各个磁盘组上,为大规模磁盘存储系统提供与能耗成比例的服务,需要打开的磁盘个数与需要提供的数据访问吞吐量成正比。利用动力学方法建立了一个针对大规模磁盘存储系统节能分析的二维元胞自动机模型。分析数据中心大规模磁盘存储系统的自组织性和自我调节能力,通过局部数据节点的能耗感知预取和数据迁移等行为,利用简单的状态转换规则,模拟和分析局部磁盘节能行为对系统整体性能和能耗的影响。实验结果表明,整个系统性能和节点状态随着局部磁盘的调控,元胞状态呈现出复杂的时空演化现象,副本个数随着负载的增加而增多并趋于稳定。在负载到达速度较低的情况下,各个磁盘的等待队列长度熵出现近似的幂律分布,整个系统的节能行为表现出一定的自组织特性。
Since disk serves as the dominant storage device in data centers, conserving disk energy plays a significant role in data center operating fee reduction, and energy-saving and emission-reduction. However, implementing disk energy conserving technique is still facing many challenges currently. On the one hand, spinning up a disk consumes much power and time. On the other hand, disk has limited power cycles. Most previous work on energy-aware prefetching ignores the relationship between system performance and disk lifetime. Besides, the change of the state of a single disk may affect the performance of the whole storage system. Therefore, to address these critical issues, efforts must be paid to study disk energy conservation considering disk reliability and system performance, and the self-organizing behaviors in disk storage system.
Power-aware Greedy Prefetching (PGP) increases disk idle intervals by greedily preloading much data to buffer. However, this might result in less energy conservation and more disk power cycles without exploiting the relationship between I/O access pattern and application pattern. Combining the tradeoff among disk power consumption, performance guarantee and disk reliability together, a power-aware prefetching framework for massive disk storage system, is proposed.
To solve single disk and single stream case, a Disk characteristic based Power-Optimal Prefetching (DiscPOP) model, is formulated as an optimization problem. DiscPOP is proved to be solved via a 0-1 Integer Linear Programming (ILP) technique. For offline cases, a Greedy Partition algorithm (GP) is proposed to divide the problem into several small ones and solve them separately via the proposed ILP algorithm. For online cases, two heuristic algorithms are proposed based on Lazy Start Power-Optimal Prefetching (LSPOP) technique. Both of them use simple threshold controlled algorithms to select a prefetching start judiciously and cautiously. A Supple Chain Management based model is borrowed to solve online multi-stream power-aware prefetching. For multi-disk case, disks are divided into n groups to achieve n-competitive power-optimal prefetching. The results show both GP and online algorithms outperforms PGP and other traditional aggressive prefetching algorithms by more disk energy conservation and less power cycles.
Conventional asynchronous prefetching incurs serious mixed random write and read load in SSD (Solid State Disk). To apply the mixed cache with SSD and DRAM (Dynamic Random Access Memory), three power-aware prefetching rules, are proposed. The prefetching degree and trigger distance of different streams are adjusted dynamically. SSD and DRAM serve different arrival ratio streams. When asynchronous prefetching is performed on SSD, some of the fetched data are placed in DRAM to eliminate mixed read and write load. Based on these three proposed rules, a Coordinated and Adaptive Prefetching (CAP) algorithm is proposed to improve multiple sequential prefetching in such hybrid caches. A page management mechanism is re-designed to reduce defragmentation in SSD. The results show CAP improve system throughput and reduce write allocations in SSD.
An ideal power-aware data placement method for massive disk storage systems is proposed based on data access frequency. The replicas are grouped to reside in different disk groups to provide power-proportional storage service. Based on the ideal data placement, a 2-D cellular automata model, named MDSCA, is proposed to analyze and emulate the dynamical behavior rules in massive disk storage systems. The simulation results show that complex temporal and spatial phenomena evolve from the adjustment of local cells by energy-aware prefetchng and data migration. The total number of replicas increases when the load becomes heavier, and it tends to a stable sate eventually. Moreover, when the load is low, it is shown that there is an approximate power law distribution of the entropy of request queue length of each disk. To a certain extent, the whole system exhibits self-organization.
引文
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