Distributed computation in computer networks

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Web Application Performance Testing

Web application performance testing is an emerging and important field of software engineering. As web applications become more commonplace and complex, the need for performance testing will only increase. This paper discusses common concepts, practices and tools that lie at the heart of web application performance testing. A pragmatic, hands-on approach is assumed where applicable; real-life examples of test tooling, execution and analysis are presented right next to the underpinning theory. At the client-side, web application performance is primarily driven by the amount of data transmitted over the wire. At the server-side, selection of programming language and platform, implementation complexity and configuration are the primary contributors to web application performance. Web application performance testing is an activity that requires delicate coordination between project stakeholders, developers, system administrators and testers in order to produce reliable and useful results. Proper test definition, execution, reporting and repeatable test results are of utmost importance. Open-source performance analysis tools such as Apache JMeter, Firebug and YSlow can be used to realise effective web application performance tests. A sample case study using these tools is presented in this paper. The sample application was found to perform poorly even under the moderate load incurred by the sample tests.Siirretty Doriast

A methodology for full-system power modeling in heterogeneous data centers

The need for energy-awareness in current data centers has encouraged the use of power modeling to estimate their power consumption. However, existing models present noticeable limitations, which make them application-dependent, platform-dependent, inaccurate, or computationally complex. In this paper, we propose a platform-and application-agnostic methodology for full-system power modeling in heterogeneous data centers that overcomes those limitations. It derives a single model per platform, which works with high accuracy for heterogeneous applications with different patterns of resource usage and energy consumption, by systematically selecting a minimum set of resource usage indicators and extracting complex relations among them that capture the impact on energy consumption of all the resources in the system. We demonstrate our methodology by generating power models for heterogeneous platforms with very different power consumption profiles. Our validation experiments with real Cloud applications show that such models provide high accuracy (around 5% of average estimation error).This work is supported by the Spanish Ministry of Economy and Competitiveness under contract TIN2015-65316-P, by the Gener- alitat de Catalunya under contract 2014-SGR-1051, and by the European Commission under FP7-SMARTCITIES-2013 contract 608679 (RenewIT) and FP7-ICT-2013-10 contracts 610874 (AS- CETiC) and 610456 (EuroServer).Peer ReviewedPostprint (author's final draft

Cloud Ready Desktop Virtualization Solution

Cloud computing is a relatively new set of technologies that can allow businesses to easily scale their computing resources and improve responsiveness to customer needs. This has held true for application, server, and desktop virtualization. Desktop virtualization in particular provides a means to solve many of the traditional challenges associated with deploying and maintaining large business workstation environments, including centralized data management, rapid deployment of workstations, and centralized updating. VMware, a longtime leader in the virtualization sector, offers a desktop virtualization platform that is widely considered to be the best in class. This paper explores the process of building a desktop virtualization solution using VMware View, a cloud-ready desktop virtualization solution from VMware

Database machines in support of very large databases

Software database management systems were developed in response to the needs of early data processing applications. Database machine research developed as a result of certain performance deficiencies of these software systems. This thesis discusses the history of database machines designed to improve the performance of database processing and focuses primarily on the Teradata DBC/1012, the only successfully marketed database machine that supports very large databases today. Also reviewed is the response of IBM to the performance needs of its database customers; this response has been in terms of improvements in both software and hardware support for database processing. In conclusion, an analysis is made of the future of database machines, in particular the DBC/1012, in light of recent IBM enhancements and its immense customer base

Extensible Performance-Aware Runtime Integrity Measurement

Today\u27s interconnected world consists of a broad set of online activities including banking, shopping, managing health records, and social media while relying heavily on servers to manage extensive sets of data. However, stealthy rootkit attacks on this infrastructure have placed these servers at risk. Security researchers have proposed using an existing x86 CPU mode called System Management Mode (SMM) to search for rootkits from a hardware-protected, isolated, and privileged location. SMM has broad visibility into operating system resources including memory regions and CPU registers. However, the use of SMM for runtime integrity measurement mechanisms (SMM-RIMMs) would significantly expand the amount of CPU time spent away from operating system and hypervisor (host software) control, resulting in potentially serious system impacts. To be a candidate for production use, SMM RIMMs would need to be resilient, performant and extensible. We developed the EPA-RIMM architecture guided by the principles of extensibility, performance awareness, and effectiveness. EPA-RIMM incorporates a security check description mechanism that allows dynamic changes to the set of resources to be monitored. It minimizes system performance impacts by decomposing security checks into shorter tasks that can be independently scheduled over time. We present a performance methodology for SMM to quantify system impacts, as well as a simulator that allows for the evaluation of different methods of scheduling security inspections. Our SMM-based EPA-RIMM prototype leverages insights from the performance methodology to detect host software rootkits at reduced system impacts. EPA-RIMM demonstrates that SMM-based rootkit detection can be made performance-efficient and effective, providing a new tool for defense

Performance issues in mid-sized relational database machines

Relational database systems have provided end users and application programmers with an improved working environment over older hierarchial and networked database systems. End users now use interactive query languages to inspect and manage their data. And application programs are easier to write and maintain due to the separation of physical data storage information from the application program itself. These and other benefits do not come without a price however. System resource consumption has long been the perceived problem with relational systems. The additional resource demands usually force computing sites to upgrade existing systems or add additional facilities. One method of protecting the current investment in systems is to use specialized hardware designed specifically for relational database processing. \u27Database Machines\u27 provide that alternative. Since the commercial introduction of database machines in the early 1980\u27s, both software and hardware vendors of relational database systems have claimed superior performance over competing products. Without a STANDARD performance measurement technique, the database user community has been flooded with benchmarks and claims from vendors which are immediately discarded by some competitors as being biased towards a particular system design. This thesis discusses the issues of relational database performance measurement with an emphasis on database machines, however; these performance issues are applicable to both hardware and software systems. A discussion of hardware design, performance metrics, software and database design is included. Also provided are recommended guidelines to use in evaluating relational database systems in lieu of a standard benchmark methodology