Abstract — 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 approximation’s 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. I.

this paper, we describe an approach in which analytic performance models are combined with combinatorial search techniques to design controllers that run periodically (e.g., every few minutes) to determine the best possible configuration for the system given its workload. We first illustrate and motivate the ideas using a simulated multithreaded server. Then, we provide experimental results, obtained by using the techniques described here, to an actual Web server subject to a workload generated by SURGE

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Network attached storage devices improve I/O performance by separating control and data paths and eliminating host intervention during data transfer. Devices are attached to a high speed network for data transfer and to a slower network for control messages. Hierarchical mass storage systems use disks to cache the most recently used files and tapes (robotic and manually mounted) to store the bulk of the files in the file system. This paper shows how queuing network models can be used to assess the performance of hierarchical mass storage systems that use network attached storage devices. The analytic model validated through simulation was used to analyze many different scenarios. 1 Introduction Most current mass storage systems at national laboratories and supercomputer centers are based on the server attached storage model in which all storage devices are attached to a single machine using high speed busses and I/O channels. A request for access to a storage object arrives at the ser...

Mean value analysis (MVA) is a well-known solution technique for separable closed queueing networks used in performance modeling of computer and communication systems. In many cases, like for sensitivity analysis or with inaccurate model input parameters, intervals are more appropriate as model inputs than single values. This paper presents a version of the MVA algorithm for separable closed queueing networks with one customer class consisting of load-independent queueing centers as well as delay devices, which accepts both single values and intervals as input parameters in arbitrary combination. Monotonicity of the model outputs with respect to all input parameters is proved and these monotonicity properties are used to construct a low cost intervalversion of the MVA algorithm providing exact output intervals as results. Thus, dependency problems commonly arising with the interval evaluation of arithmetic expressions are avoided without significant increase in computation costs. Addit...

Being able to model contention for software resources (e.g., a critical section or database lock) is paramount to building performance models that capture all aspects of the delay encountered by a process as it executes. Several methods have been offered for dealing with software contention and with message blocking in client-server systems. This paper presents a general, straightforward, easy to understand and implement, approach to modeling software contention using queuing networks. The approach, called SQNHQN, consists of a two-level iterative process. Two queuing networks are considered: one represents software resources (SQN) and the other hardware resources (HQN). Multiclass models are allowed and any solution technique---exact or approximate---can be used at any of the levels. This technique falls in the general category of fixed-point approximate models and is similar in nature to other approaches. The main difference lies in its simplicity. The process converges very fast in the examples examined. The results were validated against global balance equation solutions and are very accurate.

Computer systems are becoming extremely complex due to the large number and heterogeneity of their hardware and software components, the multi-layered architecture used in their design, and the unpredictable nature of their workloads. Thus, performance management becomes difficult and expensive when carried out by human beings. A new approach, called self-managing computer systems, is to build into the systems the mechanisms required to self-adjust configuration parameters so that the Quality of Service requirements of the system are constantly met. In this paper, we evaluate the robustness of such methods when the workload exhibits high variability in terms of the inter-arrival time and service times of requests. Another contribution of this paper is the assessment of the use of workload forecasting techniques in the design of QoS controllers.

When evaluating the performance of computer systems, often uncertainties or variabilities in service demands may be observed. Applying well known mean value analysis (MVA) for single- or multiclass queueing network models of such systems is inappropriate and ineffective, because these models fail to represent variations within a class. This paper proposes to use histograms for characterizing model parameters that are associated with uncertainty or variability and presents an adaptation of the single class MVA algorithm, which traditionally accepts single (mean) values for service demands, so that one or more input parameters can be specified as a histogram. The adapted algorithm generates a histogram output for the performance measures, thus providing a more detailed information (e.g. percentile values) than the mean values obtained from conventional MVA. The proposed technique is demonstrated on selected examples in different problem domains. It is shown, that the computational comple...

Mass storage systems are finding greater use in scientific computing research environments for retrieving and archiving the large volumes of data generated and manipulated by scientific computations. This paper presents a queuing network model that can be used to carry out capacity planning studies of hierarchical mass storage systems. Measurements taken on a Unitree mass storage system and a detailed workload characterization provided the workload intensity and resource demand parameters for the various types of read and write requests. The performance model developed here is based on approximations to multiclass Mean Value Analysis of queuing networks. The approximations were validated through the use of discrete event simulation and the complete model was validated and calibrated through measurements. The resulting model was used to analyze three different scenarios: effect of workload intensity increase, use of file compression at the server and client, and use of file abstractions.

As the complexity of IT systems increases, performance management and capacity planning become the largest and most difficult expenses to control. New methodologies and modeling techniques that explain large-system behavior and help predict their future performance are now needed to effectively tackle the emerging performance issues. With the multi-tier architecture paradigm becoming an industry standard for developing scalable client-server applications, it is important to design effective and accurate performance prediction models of multi-tier applications under an enterprise production environment and a real workload mix. To accurately answer performance questions for an existing production system with a real workload mix, we design and implement a new capacity planning and anomaly detection tool, called R-Capriccio, that is based on the following three components: i) a Workload Profiler that exploits locality in existing enterprise web workloads and extracts a small set of most popular, core client transactions responsible for the majority of client requests in the system; ii) a Regression-based Solver that is used for deriving the CPU demand of each core transaction on a given hardware; and iii) an Analytical Model that is based on a network of queues that models a multi-tier system. To validate R-Capriccio, we conduct a detailed case study using the access logs from two heterogeneous production servers that represent customized client accesses to a popular and actively used HP Open View Service Desk application.