Characterizing Service Level Objectives for Cloud Services: Motivation of Short-Term Cache Allocation Performance Modeling

Service level objectives (SLOs) stipulate performance goals for cloud applications, microservices, and infrastructure. SLOs are widely used, in part, because system managers can tailor goals to their products, companies, and workloads. Systems research intended to support strong SLOs should target realistic performance goals used by system managers in the field. Evaluations conducted with uncommon SLO goals may not translate to real systems. Some textbooks discuss the structure of SLOs but (1) they only sketch SLO goals and (2) they use outdated examples. We mined real SLOs published on the web, extracted their goals and characterized them. Many web documents discuss SLOs loosely but few provide details and reflect real settings. Systematic literature review (SLR) prunes results and reduces bias by (1) modeling expected SLO structure and (2) detecting and removing outliers. We collected 75 SLOs where response time, query percentile and reporting period were specified. We used these SLOs to confirm and refute common perceptions. For example, we found few SLOs with response time guarantees below 10 ms for 90% or more queries. This reality bolsters perceptions that single digit SLOs face fundamental research challenges.This work was funded by NSF Grants 1749501 and 1350941.No embargoAcademic Major: Computer Science and EngineeringAcademic Major: Financ

Visualizing the underlying trends of component latencies affecting service operation performance

This paper presents a technology agnostic method for extracting the underlying distinct patterns of variations in the overall performance of a service operation for changes to different application components supporting the service operation in a computer based service provider to consumer contract. This short paper advocates that visualizing these patterns would help in early projection of the operation's performance due to modification of the application components/processing catering to the operation, without the need of repetitive performance and load testing of the whole service. Lookup datasets against different component configurations are created to associate the variability of component processing impedances to the service operation's performance and best fit regression types are applied to enable trend extrapolation and interpolation

Developing an Online Student Accommodation Registration in UUM

The online reservation services allow persons to employ Information Technology without being tied to a single location; it provides greater flexibility than what is possible with desktop. This technology enables users to access the Internet at any time in any location. Different issues have been raised due to the limit of adopting technology to provide flexible techniques, such as UUM accommodation booking system. Hence, this study aimed to develop an online accommodation registration in UUM for managing and organizing UUM accommodation facilities for new postgraduate students. The study adopted the use of System Development Lifecycle methodology for designing and developing the proposed system. In addition, an evaluation was also conducted with 50 post graduate student from UUM to indicate their opinion about the proposed system. The result showed that online accommodation booking system was ease, useful, and satisfies the user's perspectives

Simulating complex systems with a low-detail model

In this paper we show how modeling and simulating a complex system such as a web-server can help to evaluate di erent metrics and proposals to improve the performance without necessity of using a real system. Many times the system is unavailable or requires spending time and resources to generate results. With simulation and concretely with a coarse-grain simulation as we propose can solve with great success this problem. In this article we have been able to simulate the metric and behaviour of a web server with SSL security, the ellapsed time required by the simulation on a desktop machine is only 1/10 of real time. We have also been able to measure, for example,the performance enhancements with 8 CPUs without having an available machine of similar features.Postprint (published version

自律的ネットワーク運用のための管理知識の利用手法に関する研究

Tohoku University木下哲男課

Modeling virtualized application performance from hypervisor counters

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 61-64).Managing a virtualized datacenter has grown more challenging, as each virtual machine's service level agreement (SLA) must be satisfied, when the service levels are generally inaccessible to the hypervisor. To aid in VM consolidation and service level assurance, we develop a modeling technique that generates accurate models of service level. Using only hypervisor counters as inputs, we train models to predict application response times and predict SLA violations. To collect training data, we conduct a simulation phase which stresses the application across many workloads levels, and collects each response time. Simultaneously, hypervisor performance counters are collected. Afterwards, the data is synchronized and used as training data in ensemble-based genetic programming for symbolic regression. This modeling technique is quite efficient at dealing with high-dimensional datasets, and it also generates interpretable models. After training models for web servers and virtual desktops, we test generalization across different content. In our experiments, we found that our technique could distill small subsets of important hypervisor counters from over 700 counters. This was tested for both Apache web servers and Windows-based virtual desktop infrastructures. For the web servers, we accurately modeled the breakdown points and also the service levels. Our models could predict service levels with 90.5% accuracy on a test set. On a untrained scenario with completely different contending content, our models predict service levels with 70% accuracy, but predict SLA violation with 92.7% accuracy. For the virtual desktops, on test scenarios similar to training scenarios, model accuracy was 97.6%. Our main contribution is demonstrating that a completely data-driven approach to application performance modeling can be successful. In contrast to many other works, our models do not use workload level or response times as inputs to the models, but nevertheless predicts service level accurately. Our approach also lets the models determine which inputs are important to a particular model's performance, rather than hand choosing a few inputs to train on.by Lawrence L. Chan.M.Eng

PseudoApp: Performance Prediction for Application Migration to Cloud

Abstract-To migrate an existing application to cloud, a user needs to estimate and compare the performance and resource consumption of the application running in different clouds, in order to select the best service provider and the right virtual machine size. However, it is prohibitively expensive to install a complex application in multiple new environments solely for the purpose of performance benchmarking. Performance modeling is more practical but the accuracy is limited by system factors that are hard to model. We propose a new technique called PseudoApp to address these challenges. Our solution creates a pseudo-application to mimic the resource consumption of a real application. A pseudo-application runs the same set of distributed components and executes the same sequence of system calls as those of the real application. By benchmarking a simple and easyto-install PseudoApp in different cloud environments, a user can accurately obtain the performance and resource consumption of the real application. We apply PseudoApp to Apache and TPC-W and find that PseudoApp accurately predicts their performance with 2-8% error in throughput

Provisioning multi-tier cloud applications using statistical bounds on sojourn time

In this paper we present a simple and effective approach for re-source provisioning to achieve a percentile bound on the end to end response time of a multi-tier application. We, at first, model the multi-tier application as an open tandem network of M/G/1-PS queues and develop a method that produces a near optimal appli-cation configuration, i.e, number of servers at each tier, to meet the percentile bound in a homogeneous server environment – using a single type of server. We then extend our solution to a K-server case and our technique demonstrates a good accuracy, independent of the variability of service-times. Our approach demonstrates a provisioning error of no more than 3 % compared to a 140 % worst case provisioning error obtained by techniques based on anM/M/1-FCFS queue model. In addition, we extend our approach to han-dle a heterogenous server environment, i.e., with multiple types of servers. We find that fewer high-capacity servers are preferable for high percentile provisioning. Finally, we extend our approach to account for the rental cost of each server-type and compute a cost efficient application configuration with savings of over 80%. We demonstrate the applicability of our approach in a real world sys-tem by employing it to provision the two tiers of the java implemen-tation of TPC-W – a multi-tier transactional web benchmark that represents an e-commerce web application, i.e. an online book-store