Daniel A. Menascé

QoS issues in Web services

Quality of service (QoS) is a combination of several qualities or properties of a service, such as: availability is the percentage of time that a service is operating; security properties include the existence and type of authentication mechanisms the service offers, confidentiality and data integrity of messages exchanged, nonrepudiation of requests or messages, and resilience to denial-of-service attacks; response time is the time a service takes to respond to various types of requests; Response time is a function of load intensity, which can be measured in terms of arrival rates (such as requests per second) or number of concurrent requests. QoS takes into account not only the average response time, but also the percentile of the response time; and throughput is the rate at which a service can process requests. QoS measures can include the maximum throughput or a function that describes how throughput varies with load intensity. The QoS measure is observed by Web services users. These users are not human beings but programs that send requests for services to Web service providers. QoS issues in Web services have to be evaluated from the perspective of the providers of Web services and from the perspective of the users of these services.

Resource Allocation for Autonomic Data Centers using Analytic Performance Models

Large data centers host several application environments (AEs) that are subject to workloads whose intensity varies widely and unpredictably. Therefore, the servers of the data center may need to be dynamically redeployed among the various AEs in order to optimize some global utility function. Previous approaches to solving this problem suffer from scalability limitations and cannot easily address the fact that there may be multiple classes of workloads executing on the same AE. This paper presents a solution that addresses these limitations. This solution is based on the use of analytic queuing network models combined with combinatorial search techniques. The paper demonstrates the effectiveness of the approach through simulation experiments. Both online and batch workloads are considered

A methodology for workload characterization of E-commerce sites

Performance analysis and capacity planning for e-commerce sites poses an interesting problem: how to best characterize the workload of these sites. Tradition al workload characterization methods, based on hits/set, page views/set, or visits/set, are not appropriate for e-commerce sites. In these environments, customers interact with the site through a series of consecutive and related requests, called sessions. Different navigational patterns can be observed for different groups of customers. In this paper, we propose a methodology for characterizing and generating e-commerce workload models. First, we introduce a state transition graph called Customer Behavior Model Graph (CBMG), that is used to describe the behavior of groups of customers who exhibit similar navigational patterns. A set of useful metrics, analytically derived from the analysis of the CBMG, is presented. Next, we define a workload model and show the steps required to obtain its parameters. We then propose a clustering algorithm to characterize workloads of e-commerce sites in terms of CBMGs. Finally, we present and discuss experimental results of the use of proposed methodology.

Preserving QoS of e-commerce sites through self-tuning: a performance model approach

The Quality of Service (QoS) of e-commerce sites plays a crucial role in attracting and retaining customers. The workload experienced by these sites tends to vary in a very dynamic way. The complexity of the sites combined with the large short-terms variations of the workload calls for automated methods for site configuration. This paper describes a method for dynamically monitoring and tuning e-commerce sites so that desired QoS levels are attained. Our approach uses hill climbing techniques combined with analytic queuing models to guide the search for the best combination of configuration parameters. We validate our approach in an experimental setting by comparing the QoS levels of a TPC-W e-commerce site with and without control. We showed that under increasing loads, the controlled system meets its QoS goals, while the uncontrolled site fails to do so.

Business-oriented resource management policies for e-commerce servers

Abstract Quality of service of e-commerce sites has been usually managed by the allocation of resources such as processors, disks, and network bandwidth, and by tracking conventional performance metrics such as response time, throughput, and availability. However, the metrics that are of utmost importance to the management and shareholders of a Web store are revenue and profit. Thus, resource management schemes for e-commerce servers should be geared towards optimizing business metrics as opposed to conventional performance metrics. This paper uses a state transition graph called customer behavior model graph (CBMG) to describe a customer session. It then presents a family of priority based resource management policies for e-commerce servers. Priorities change dynamically as a function of the state a customer is in and as a function of the amount of money the customer has accumulated in his/her shopping cart. A detailed simulation model was developed to assess the gain of these dynamic policies with respect to policies that are oblivious to economic considerations. Simulation results show that the multilevel dynamic priority scheme suggested here can significantly improve the values of business-oriented metrics, such as revenue per second, during peak periods. E-commerce sites that use this approach will be able to improve revenue at peak times with the same server capacity.

TPC-W: a benchmark for e-commerce

When choosing an e-commerce sites hardware and software configuration, we need to know how a specific combination of Web servers, commerce servers, database servers, and supporting hardware will handle a desired load level. Benchmarks let us compare competing alternatives. Researchers have extensively studied workloads on Web sites that provide information and have characterized their performance at the level of HTTP requests. My colleagues and I have also conducted studies to understand e-commerce site workloads and to search for invariants that cut across more than one type of e-commerce site. However, there is currently only one available benchmark for e-commerce sites: TPC benchmark W, designed by the Transaction Processing Performance Council. I explore TPC-Ws main features, advantages, and limitations.

In search of invariants for e-business workloads

ABSTRACT Understanding the nature and hara teristi s of e-business workloads is a ru ial step to improve the quality of servi e o ered to ustomers in ele troni business environments. However, the variety and omplexity of the intera tions between ustomers and sites make the hara terization of ebusiness workloads a hallenging problem. Using a multilayer hierar hi al model, this paper presents a detailed hara terization of the workload of two a tual e-business sites: an online bookstore and an ele troni au tion site. Through the hara terization pro ess, we found the presen e of autonomous agents, or robots, in the workload and used the hierar hi al stru ture to determine their hara teristi s. We also found that sear h terms follow a Zipf distribution.

Autonomic Virtualized Environments

Virtualization was invented more than thirty years ago to allow large expensive mainframes to be easily shared among different application environments. As hardware prices went down, the need for virtualization faded away. Virtualization at all levels (system, storage, and network) became important again as a way to improve system security, reliability and availability, reduce costs, and provide greater flexibility. Virtualization is being used to support server consolidation efforts. In that case, many virtual machines running different application environments share the same hardware resources. This paper shows how autonomic computing techniques can be used to dynamically allocate processing resources to various virtual machines as the workload varies. The goal of the autonomic controller is to optimize a utility function for the virtualized environment. The paper considers dynamic CPU priority allocation and the allocation of CPU shares to the various virtual machines. Results obtained through simulation show that the autonomic controller is capable of achieving its goal

Static and Dynamic Processor Scheduling Disciplines in Heterogeneous Parallel Architectures

Most parallel jobs cannot be fully parallelized. In a homogeneous parallel machine-one in which all processors are identical-the serial fraction of the computation has to be executed at the speed of any of the identical processors, limiting the speedup that can be obtained due to parallelism. In a heterogeneous architecture, the sequential bottleneck can be greatly reduced by running the sequential part of the job or even the critical tasks in a faster processor. This paper uses Markov chain based models to analyze the performance of static and dynamic processor assignment policies for heterogeneous architectures. Parallel jobs are assumed to be described by acyclic directed task graphs. A new static processor assignment policy, called Largest Task First Minimum Finish Time (LTFMFT), is introduced. The analysis shows that this policy is very sensitive to the degree of heterogeneity of the architecture, and that it outperforms all other policies analyzed. Three dynamic assignment disciplines are compared and it is shown that, in heterogeneous environments, the disciplines that perform better are those that consider the structure of the task graph, and not only the service demands of the individual tasks. The performance of heterogeneous architectures is compared with cost-equivalent homogeneous ones taking into account different scheduling policies. Finally, static and dynamic processor assignment disciplines are compared in terms of performance.

Scaling for e-business

One of the challenges in designing and maintaining e-business sites is to ensure their scalability as the work-load increases. The article discusses a multi-layer reference model that can be used for capacity planning and analysis of e-business sites. It shows how to characterize the workload of e-commerce servers, taking into account customer behavior patterns. It further discusses how the various technologies used in e-commerce sites, such as authentication and payment protocols, affect their performance.