Steven E. Froehlich

Generic on-line discovery of quantitative models for service level management

Quantitative models are needed for a variety of management tasks, including (a) identification of critical variables to use for health monitoring, (b) anticipating service level violations by using predictive models, and (c) on-going optimization of configurations. Unfortunately, constructing quantitative models requires specialized skills that are in short supply. Even worse, rapid changes in provider configurations and the evolution of business demands mean that quantitative models must be updated on an on-going basis. This paper describes an architecture and algorithms for on-line discovery of quantitative models without prior knowledge of the managed elements. The architecture makes use of an element schema that describes managed elements using the common information model (CIM). Algorithms are presented for selecting a subset of the element metrics to use as explanatory variables in a quantitative model and for constructing the quantitative model itself. We further describe a prototype system based on this architecture that incorporates these algorithms. We apply the prototype to on-line estimation of response times for DB2 Universal Database under a TPC-W workload. Of the approximately 500 metrics available from the DB2 performance monitor, our system chooses 3 to construct a model that explains 72% of the variability of response time.

Generic Online Optimization of Multiple Configuration Parameters with Application to a Database Server

Optimizing configuration parameters is time-consuming and skills-intensive. This paper proposes a generic approach to automating this task. By generic, we mean that the approach is relatively independent of the target system for which the optimization is done. Our approach uses online adjustment of configuration parameters to discover the system’s performance characteristics. Doing so creates two challenges: (1) handling interdependencies between configuration parameters and (2) minimizing the deleterious effects on production workload while the optimization is underway. Our approach addresses (1) by including in the architecture a rule-based component that handles interdependencies between configuration parameters. For (2), we use a feedback mechanism for online optimization that searches the parameter space in a way that generally avoids poor performance at intermediate steps. Our studies of a DB2 Universal Database Server under an e-commerce workload indicate that our approach can be effective in practice.

Achieving precise coordinated cluster time in a cluster environment

A time-keeping mechanism is proposed for providing microsecond-level consistent time across a cluster of computers. The proposed mechanism is based on a new clock steering algorithm that uses piecewise linear mapping to align a local clock to an external reference clock in a smooth manner. We present two realizations of the algorithm: one is based on pulse-per-second (PPS) and the other is based on low-latency timing message exchange. The time derived by the proposed mechanism is called CCT (coordinated cluster time). It has a well-defined interface such that it can be used by applications with little overhead. Moreover, the interface deals completely with leap second issues. We implemented CCT on IBM BladeCenters and compared it to NTP. Experimental results demonstrate that the proposed mechanism achieves one microsecond precision.

Generic On-Line Discovery of Quantitative Models

Quantitative models are needed for a variety of management tasks, including identification of critical variables to use for health monitoring, anticipating service-level violations by using predictive models, and ongoing optimization of configurations. Unfortunately, constructing quantitative models requires specialized skills that are in short supply. Even worse, rapid changes in provider configurations and the evolution of business demands mean that quantitative models must be updated on an ongoing basis. This paper describes an architecture and algorithms for online discovery of quantitative models without prior knowledge of the managed elements. The architecture makes use of an element schema that describes managed elements using the Common Information Model (CIM). Algorithms are presented for selecting a subset of the element metrics to use as explanatory variables in a quantitative model and for constructing the quantitative model itself. We further describe a prototype system based onthis architecture that incorporates these algorithms. We apply the prototype to online estimation of response times for DB2 Universal Database under a TPC-W workload. Of the approximately 500 metrics available from the DB2 performance monitor, our system chooses three to construct a model that explains 72 percent of the variability of response time.

Service level management: A dynamic discovery and optimization approach

Optimizing configuration parameters for achieving service level objectives is time-consuming and skills-intensive. This paper proposes a generic approach to automating this task. By generic, we mean that the approach is relatively independent of the target system for which the optimization is done. Our approach uses online adjustment of configuration parameters to discover the systems performance characteristics. Doing so creates two challenges: (1) handling interdependencies between configuration parameters and (2) minimizing the deleterious effects on production workload while the optimization is underway. Our approach addresses (1) by including in the architecture a rule-based component that handles interdependencies between configuration parameters. For (2), we use a feedback mechanism for online optimization that searches the parameter space in a way that generally avoids poor performance at intermediate steps. Our studies of a DB2 Universal Database Server under an e-commerce workload indicate that our approach is effective in practice.