Evgenia Smirni

A Regression-Based Analytic Model for Dynamic Resource Provisioning of Multi-Tier Applications

The multi-tier implementation has become the industry standard for developing scalable client-server enterprise applications. Since these applications are performance sensitive, effective models for dynamic resource provisioning and for delivering quality of service to these applications become critical. Workloads in such environments are characterized by client sessions of interdependent requests with changing transaction mix and load over time, making model adaptivity to the observed workload changes a critical requirement for model effectiveness. In this work, we apply a regression-based approximation of the CPU demand of client transactions on a given hardware. Then we use this approximation in an analytic model of a simple network of queues, each queue representing a tier, and show the approximations effectiveness for modeling diverse workloads with a changing transaction mix over time. Using the TPC- W benchmark and its three different transaction mixes we investigate factors that impact the efficiency and accuracy of the proposed performance prediction models. Experimental results show that this regression-based approach provides a simple and powerful solution for efficient capacity planning and resource provisioning of multi-tier applications under changing workload conditions.

Workload-aware load balancing for clustered Web servers

We focus on load balancing policies for homogeneous clustered Web servers that tune their parameters on-the-fly to adapt to changes in the arrival rates and service times of incoming requests. The proposed scheduling policy, ADAPTLOAD, monitors the incoming workload and self-adjusts its balancing parameters according to changes in the operational environment such as rapid fluctuations in the arrival rates or document popularity. Using actual traces from the 1998 World Cup Web site, we conduct a detailed characterization of the workload demands and demonstrate how online workload monitoring can play a significant part in meeting the performance challenges of robust policy design. We show that the proposed load, balancing policy based on statistical information derived from recent workload history provides similar performance benefits as locality-aware allocation schemes, without requiring locality data. Extensive experimentation indicates that ADAPTLOAD results in an effective scheme, even when servers must support both static and dynamic Web pages.

Anomaly? application change? or workload change? towards automated detection of application performance anomaly and change

Automated tools for understanding application behavior and its changes during the application life-cycle are essential for many performance analysis and debugging tasks. Application performance issues have an immediate impact on customer experience and satisfaction. A sudden slowdown of enterprise-wide application can effect a large population of customers, lead to delayed projects and ultimately can result in company financial loss. We believe that online performance modeling should be a part of routine application monitoring. Early, informative warnings on significant changes in application performance should help service providers to timely identify and prevent performance problems and their negative impact on the service. We propose a novel framework for automated anomaly detection and application change analysis. It is based on integration of two complementary techniques: i) a regression-based transaction model that reflects a resource consumption model of the application, and ii) an application performance signature that provides a compact model of run-time behavior of the application. The proposed integrated framework provides a simple and powerful solution for anomaly detection and analysis of essential performance changes in application behavior. An additional benefit of the proposed approach is its simplicity: it is not intrusive and is based on monitoring data that is typically available in enterprise production environments.

Multiple-queue backfilling scheduling with priorities and reservations for parallel systems

We describe a new, non-FCFS policy to schedule parallel jobs on systems that may be part of a computational grid. Our algorithm continuously monitors the system (i.e., intensity of incoming jobs and variability of their resource demands) and continuously adapts its scheduling parameters to sudden workload fluctuations. The proposed policy is based on backfilling which permits job rearrangement in the waiting queue. By exploiting otherwise idle processors, this rearrangement reduces fragmentation of system resources, thereby providing higher system utilization. We propose to maintain multiple job queues that effectively separate jobs according to their projected execution time. Our policy supports different job priority classes as well as job reservations, making it appropriate for scheduling jobs on parallel systems that are part of a computational grid. Detailed performance comparisons via simulation using traces from the Parallel Workload Archive indicate that the proposed policy consistently outperforms traditional scheduling approaches.

I/O requirements of scientific applications: an evolutionary view

The modest I/O configurations and file system limitations of many current high-performance systems preclude solution of problems with large I/O needs. I/O hardware and file system parallelism is the key to achieving high performance. We analyze the I/O behavior of several versions of two scientific applications on the Intel Paragon XP/S. The versions involve incremental application code enhancements across multiple releases of the operating system. Studying the evolution of I/O access patterns underscores the interplay between application access patterns and file system features. Our results show that both small and large request sizes are common, that at present, application developers must manually aggregate small requests to obtain high disk transfer rates, that concurrent file accesses are frequent, and that appropriate matching of the application access pattern and the file system access mode can significantly increase application I/O performance. Based on these results, we describe a set of file system design principles.

Workload Characterization of Input/Output Intensive Parallel Applications

The broadening disparity in the performance of input/output (I/O) devices and the performance of processors and communication links on parallel systems is a major obstacle to achieving high performance for a wide range of parallel applications. I/O hardware and file system parallelism are the keys to bridging this performance gap. A prerequisite to the development of efficient parallel file systems is detailed characterization of the I/O demands of parallel applications. In this paper, we present a comparative study of the I/O access patterns commonly found in I/O intensive parallel applications. Using the Pablo performance analysis environment and its I/O extensions we captured application I/O access patterns and analyzed their interactions with current parallel I/O systems. This analysis has proven instrumental in guiding the development of new application programming interfaces (APIs) for parallel file systems and in developing effective file system policies that can adaptively respond to complex application I/O requirements.

EQUILOAD: a load balancing policy for clustered web servers

Abstract We present a new strategy for the allocation of requests in clustered web servers, based on the size distribution of the requested documents. This strategy, EquiLoad , manages to achieve a balanced load to each of the back-end servers, and its parameters are obtained from the analysis of a trace’s past data. To study its performance, we use phase-type distribution fittings and solve the resulting models using a new solution method for M/PH/1 queues that only requires solution of linear systems. The results show that EquiLoad greatly outperforms random allocation, performs comparably or better than the Shortest Remaining Processing Time and Join Shortest Queue policies and maximizes cache hits at the back-end servers, therefore behaving similarly to a “locality-aware” allocation policy, but at a very low implementation cost.

Injecting realistic burstiness to a traditional client-server benchmark

The design of autonomic systems often relies on representative benchmarks for evaluating system performance and scalability. Despite the fact that experimental observations have established that burstiness is a common workload characteristic that has deleterious effects on user-perceived performance, existing client-server benchmarks do not provide mechanisms for injecting burstiness into the workload. In this paper, we introduce a new methodology for generating workloads that emulate the temporal surge phenomenon in a controllable way, thus provide a mechanism that enables testing and evaluation of client-server system performance under reproducible bursty workloads. This new methodology allows to inject different amounts of burstiness into the arrival stream using the index of dispersion, a single parameter that is as simple to use as a turnable knob. We exemplify the effectiveness of this new methodology by introducing a new module into the TPC-W, a benchmark that is routinely used for capacity planning of e-commerce systems. This new module injects burstiness into the arrival process of clients in a controllable manner, and hence, enables understanding system performance degradation due to burstiness. Detailed experimentation on a real system shows that this benchmark modification can stress the system under different degrees of burstiness, making a strong case for the usefulness of this modification for capacity planning of autonomic systems.

Lessons from characterizating the input/output behavior of parallel scientific applications

Abstract As both processor and interprocessor communication hardware is evolving rapidly with only moderate improvements to file system performance in parallel systems, it is becoming increasingly difficult to provide sufficient input/output (I/O) performance to parallel applications. I/O hardware and file system parallelism are the key to bridging this performance gap. Prerequisite to the development of efficient parallel file systems is the detailed characterization of the I/O demands of parallel applications. In the paper, we present a comparative study of parallel I/O access patterns, commonly found in I/O intensive scientific applications. The Pablo performance analysis tool and its I/O extensions is a valuable resource in capturing and analyzing the I/O access attributes and their interactions with extant parallel I/O systems. This analysis is instrumental in guiding the development of new application programming interfaces (APIs) for parallel file systems and effective file system policies that respond to complex application I/O requirements.

Automated anomaly detection and performance modeling of enterprise applications

Automated tools for understanding application behavior and its changes during the application lifecycle are essential for many performance analysis and debugging tasks. Application performance issues have an immediate impact on customer experience and satisfaction. A sudden slowdown of enterprise-wide application can effect a large population of customers, lead to delayed projects, and ultimately can result in company financial loss. Significantly shortened time between new software releases further exacerbates the problem of thoroughly evaluating the performance of an updated application. Our thesis is that online performance modeling should be a part of routine application monitoring. Early, informative warnings on significant changes in application performance should help service providers to timely identify and prevent performance problems and their negative impact on the service. We propose a novel framework for automated anomaly detection and application change analysis. It is based on integration of two complementary techniques: (i) a regression-based transaction model that reflects a resource consumption model of the application, and (ii) an application performance signature that provides a compact model of runtime behavior of the application. The proposed integrated framework provides a simple and powerful solution for anomaly detection and analysis of essential performance changes in application behavior. An additional benefit of the proposed approach is its simplicity: It is not intrusive and is based on monitoring data that is typically available in enterprise production environments. The introduced solution further enables the automation of capacity planning and resource provisioning tasks of multitier applications in rapidly evolving IT environments.