Empirical examination of a collaborative web application

Stewart, Christopher; Leventi, M.; Shen, Kai

doi:10.1109/iiswc.2008.4636094

Cited by 15 publications

(7 citation statements)

References 10 publications

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“…• WeBWorK [7,33] is a web-based application that allows teachers to post math or physics problems for their students to solve online. In particular, teacher-supplied WeBWorK problems are interpreted by the application server as contentgenerating scripts.…”

Section: Empirical Setup and Statistics Collection Methodologymentioning

confidence: 99%

Request behavior variations

ShenKai

2010

SIGPLAN Not.

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A large number of user requests execute (often concurrently) within a server system. A single request may exhibit fluctuating hardware characteristics (such as instruction completion rate and on-chip resource usage) over the course of its execution, due to inherent variations in application execution semantics as well as dynamic resource competition on resource-sharing processors like multicores. Understanding such behavior variations can assist fine-grained request modeling and adaptive resource management.This paper presents operating system management to track request behavior variations online. In addition to metric sample collection during periodic interrupts, we exploit the frequent system calls in server applications to perform low-cost in-kernel sampling. We utilize identified behavior variations to support or enhance request modeling in request classification, anomaly analysis, and online request signature construction. A foundation of our request modeling is the ability to quantify the difference between two requests' time series behaviors. We evaluate several differencing measures and enhance the classic dynamic time warping technique with additional penalties for asynchronous warp steps. Finally, motivated by fluctuating request resource usage and the resulting contention, we implement contention-easing CPU scheduling on multicore platforms and demonstrate its effectiveness in improving the worst-case request performance.Experiments in this paper are based on five server applicationsApache web server, TPCC, TPCH, RUBiS online auction benchmark, and a user-content-driven online teaching application called WeBWorK.

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Section: Empirical Setup and Statistics Collection Methodologymentioning

confidence: 99%

Request behavior variations

ShenKai

2010

SIGPLAN Not.

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“…In [4] Benevenuto et al characterizes user behavior in Online Social Networks and Duarte et al in [9] characterizes traffic in Web blogs. Previous work on the characterization of collaborative web applications was conducted in [23]. Although both the blogosphere and the OTA application used in our work are similar in the sense that they are user-oriented, user behavior is different in these scenarios.…”

Section: Related Workmentioning

confidence: 97%

Characterization of workload and resource consumption for an online travel and booking site

Poggi

Carrera

Gavaldà

et al. 2010

IEEE International Symposium on Workload Characterization (IISWC'10)

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Online travel and ticket booking is one of the top ECommerce industries. As they present a mix of products: flights, hotels, tickets, restaurants, activities and vacational packages, they rely on a wide range of technologies to support them: Javascript, AJAX, XML, B2B Web services, Caching, Search Algorithms and Affiliation; resulting in a very rich and heterogeneous workload. Moreover, visits to travel sites present a great variability depending on time of the day, season, promotions, events, and linking; creating bursty traffic, making capacity planning a challenge. It is therefore of great importance to understand how users and crawlers interact on travel sites and their effect on server resources, for devising cost effective infrastructures and improving the Quality of Service for users.In this paper we present a detailed workload and resource consumption characterization of the web site of a top national Online Travel Agency. Characterization is performed on server logs, including both HTTP data and resource consumption of the requests, as well as the server load status during the execution. From the dataset we characterize user sessions, their patterns and how response time is affected as load on Web servers increases. We provide a fine grain analysis by performing experiments differentiating: types of request, time of the day, products, and resource requirements for each. Results show that the workload is bursty, as expected, that exhibit different properties between day and night traffic in terms of request type mix, that user session length cover a wide range of durations, which response time grows proportionally to server load, and that response time of external data providers also increase on peak hours, amongst other results. Such results can be useful for optimizing infrastructure costs, improving QoS for users, and development of realistic workload generators for similar applications.

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“…RUBiS implements the functionality of an auctioning Web site. Both applications have already been used in evaluating previous cloud computing research [17,18]. We can have 9 possible deployment variations with each of the data-tier and the application tier being (i) on the private premise, (ii) on the public cloud, or (iii) partitioned for hybrid deployment.…”

Section: Discussionmentioning

confidence: 99%

Partitioning of web applications for hybrid cloud deployment

Kaviani

Wohlstadter

Lea

2014

J Internet Serv Appl

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Hybrid cloud deployment offers flexibility in trade-offs between the cost-savings/scalability of the public cloud and control over data resources provided at a private premise. However, this flexibility comes at the expense of complexity in distributing a system over these two locations. For multi-tier web applications, this challenge manifests itself primarily in the partitioning of application-and database-tiers. While there is existing research that focuses on either application-tier or data-tier partitioning, we show that optimized partitioning of web applications benefits from both tiers being considered simultaneously. We present our research on a new cross-tier partitioning approach to help developers make effective trade-offs between performance and cost in a hybrid cloud deployment. The general approach primarily benefits from two technical improvements to integer-programming based application partitioning. First, an asymmetric cost-model for optimizing data transfer in environments where ingress and egress data-transfer have differing costs, such as in many infrastructure as a service platforms. Second, a new encoding of database query plans as integer programs, to enable simultaneous optimization of code and data placement in a hybrid cloud environment. In two case studies the approach results in up to 54% reduction in monetary costs compared to a premise only deployment and 56% improvement in response time compared to a naive partitioning where the application-tier is deployed in the public cloud and the data-tier is on private infrastructure.

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Empirical examination of a collaborative web application

Abstract: Online instructional applications, social networking sites, Wiki-based

Cited by 15 publications

References 10 publications

Request behavior variations

Request behavior variations

Characterization of workload and resource consumption for an online travel and booking site

Partitioning of web applications for hybrid cloud deployment

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