Abstract not found

In this paper we revisit the problem of achieving multi-class workload response time goals by automatically adjusting the buffer memory allocations of each workload class. We discuss the virtues and limitations of previous work with respect to a set of criteria we lay out for judging the success of any goal-oriented resource allocation algorithm. We then introduce the concept of hit rate concavity and develop a new goal-oriented buffer allocation algorithm, called Class Fencing, that is based on this concept. Exploiting the notion of hit rate concavity results in an algorithm that not only is as accurate and stable as our previous work, but also more responsive, more robust, and simpler to implement.

Administration tasks increasingly dominate the total cost of ownership of database management systems. A key task, and a very difficult one for an administrator, is to justify upgrades of CPU, memory and storage resources with quantitative predictions of the expected improvement in workload performance. Current database systems are not designed with such prediction in mind and hence offer only limited help to the administrator. This paper proposes changes to database system design that enable a Resource Advisor to answer “whatif” questions about resource upgrades. A prototype Resource Advisor built to work with a commercial DBMS shows the efficacy of our approach in predicting the effect of upgrading a key resource — buffer pool size — on OLTP workloads in a highly concurrent system. 1

Two contributions are made in this paper. First, we show that system level characterization of file block references is inadequate for maximizing buffer cache performance. We show that a finer-grained characterization approach is needed. Though application level characterization methods have been proposed, this is the first attempt, to the best of our knowledge, to consider file level characterizations. We propose an Application/File-level Characterization (AFC) scheme where we detect on-line the reference characteristics at the application level and then at the file level, if necessary. The results of this characterization are used to employ appropriate replacement policies in the buffer cache to maximize performance. The second contribution is in proposing an efficient and fair buffer allocation scheme. Application or file level resource management is infeasible unless there exists an allocation scheme that is efficient and fair. We propose the \DeltaH IT allocation scheme that tak...

In this paper, we propose a new adaptive buffer management scheme called DEAR (DEtection based Adaptive Replacement) that automatically detects the block reference patterns of applications and applies different replacement policies to different applications based on the detected reference pattern. The proposed DEAR scheme uses a periodic process. Detection is made by associating block attribute values such as backward distance and frequency gathered at the (i \Gamma 1)-th invocation with forward distances of blocks referenced between the (i \Gamma 1)-th and i-th invocations. We implemented the DEAR scheme in FreeBSD 2.2.5 and measured its performance using several real applications. The results show that compared with the LRU buffer management scheme, the proposed scheme reduces the number of disk I/Os by up to 51% (with an average of 23%) and the response time by up to 35% (with an average of 12%) in the case of single application executions. For multiple applications, the proposed sc...

The type of the workload on a database management system (DBMS) is a key consideration in tuning the system. Allocations for resources such as main memory can be very different depending on whether the workload type is Online Transaction Processing (OLTP) or Decision Support System (DSS). A DBMS also typically experiences changes in the type of workload it handles during its normal processing cycle. Database administrators must, therefore, recognize the significant shifts of workload type that demand reconfiguring the system in order to maintain acceptable levels of performance. We envision autonomous, selftuning DBMSs that have the capability to manage their own performance by automatically recognizing the workload type and then reconfiguring their resources accordingly. In this paper, we present an approach to automatically identifying a DBMS workload as either OLTP or DSS. We build a classification model based on the most significant workload characteristics that differenti ate OLTP from DSS and then use the model to identify any change in the workload type. We construct and compare classifiers built from two different sets of industry-standard workloads, namely the TPC-C and TPC-H benchmarks, and the Browsing and Ordering profiles from the TPC-W benchmark. We conduct various sets of experiments that show that our workload classifiers are reliable, and have high accuracy in recognizing the type of the workload mix and in estimating the degree of its concentration.

Of the many tuning parameters available in a database management system (DBMS), one of the most crucial to performance is the buffer pool size. Choosing an appropriate size, however, can be a difficult task. In this paper we present an analytical modeling approach to predicting the buffer pool hit rate that can be used to simplify the process of buffer pool sizing. Since the buffer replacement algorithm determines the buffer hit rate, we model the replacement algorithm which, in the case of DB2/UDB, is a variation of the GCLOCK algorithm. A Markov Chain model of GCLOCK is used to estimate the hit rate for a buffer pool. We evaluate the accuracy of the model's estimates with experiments carried out on DB2/UDB with the TPC-C benchmark. The model is validated for both single and multiple buffer pool cases.

The performance of any system cannot be determined without knowing the workload, that is, the set of requests presented to the system. Workload characterization is the process by which we produce models that are capable of describing and reproducing the behavior of a workload. Such models are imperative to any performance related studies such as capacity planning, workload balancing, performance prediction and system tuning. In this paper, we survey workload characterization techniques used for several types of computer systems. We identify significant issues and concerns encountered during the characterization process and propose an augmented methodology for workload characterization as a framework.

Recent research has shown that near optimal performance can be achieved by adaptive block replacement policies that use user-level hints regarding the block reference pattern. However, obtaining user-level hints requires considerable effort from users making it difficult to apply adaptive replacement policies to diverse kinds of applications. We propose a new adaptive block management scheme that we call DEAR (DEtection based Adaptive Replacement) which makes on-line detections of block reference patterns of applications using Decision Trees without user intervention. Based on the detected reference pattern, DEAR applies an appropriate replacement policy to each application. This scheme is suitable for buffer management in systems such as multimedia servers where data reference patterns of applications may be diverse. Results from trace driven simulations show that the DEAR scheme can detect the reference patterns of applications and reduce the miss ratio up to 15 percentage points com...

In order to meet the individual performance goals of each class in a complex multiclass database workload, today's database management systems require the adjustment of a number of low-level performance "knobs," such as buffer pool sizes, multiprogramming levels, data placement, dispatching priorities, etc. As the complexity of database systems is increasing, while their cost is declining at the same time, manually adjusting low-level DBMS performance knobs will become increasingly impractical. Ideally, the DBMS should simply accept per-class performance goals as inputs, and it should adjust its own low-level knobs in order to achieve them; this self-tuning capability is called goal-oriented resource allocation. This thesis makes three contributions in the area of goal-oriented resource allocation for database management systems. First, it defines an overall architecture for goal-oriented resource allocation that includes techniques to insure a stable and responsive system and to accu...