Adaptive client side object replication for response time improvement in pervasive environments

Improving Ruby on Rails-Based Web Application Performance

Information ◽

10.3390/info12080319 ◽

2021 ◽

Vol 12 (8) ◽

pp. 319

Author(s):

Denys Klochkov ◽

Jan Mulawka

Keyword(s):

Response Time ◽

User Experience ◽

Web Application ◽

Performance Metrics ◽

Response Times ◽

Web Development ◽

Performance Measurements ◽

Application Performance ◽

Client Side ◽

Ruby On Rails

The evolution of web development and web applications has resulted in creation of numerous tools and frameworks that facilitate the development process. Even though those frameworks make web development faster and more efficient, there are certain downsides to using them. A decrease in application performance when using an “off the shelf” framework might be a crucial disadvantage, especially given the vital role web application response time plays in user experience. This contribution focuses on a particular framework—Ruby on Rails. Once the most popular framework, it has now lost its leading position, partially due to slow performance metrics and response times, especially in larger applications. Improving and expanding upon the previous work in this field, an attempt to improve the response time of a specially developed benchmark application is made. This is achieved by performing optimizations that can be roughly divided into two groups. The first group concerns the frontend improvements, which include: adopting the client-side rendering, JavaScript Document Object Model (DOM) manipulation and asynchronous requests. Another group can be described as the backend improvements, which include implementing intelligent, granular caching, disabling redundant modules, as well as profiling and optimizing database requests and reducing database access inefficiencies. Those improvements resulted in overall up to 74% decreased page loading times, with perceived application performance being improved above this mark due to the adoption of a client-side rendering strategy. Using the different metrics of application performance measurements, each of the improvement steps is evaluated with regards to its effect on different aspects of overall performance. In conclusion, this work presents a way to significantly decrease the response time of a particular Ruby on Rails application and simultaneously provide a better user experience. Even though the majority of this process is specific to Rails, similar steps can be taken to improve applications implemented with the use of other similar frameworks. As the result of the work, a groundwork is laid for the development of the tool that could assist the developers in improving their applications as well.

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Improving SQL Query Response Time thru Client Side Processing in Client-Server Environment

Proceedings of 2019 the 9th International Workshop on Computer Science and Engineering ◽

10.18178/wcse.2019.06.103 ◽

2019 ◽

Keyword(s):

Response Time ◽

Client Server ◽

Query Response Time ◽

Client Side ◽

Sql Query

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Temporal Prediction Errors Affect Short-Term Memory Scanning Response Time

Experimental Psychology (formerly Zeitschrift für Experimentelle Psychologie) ◽

10.1027/1618-3169/a000339 ◽

2016 ◽

Vol 63 (6) ◽

pp. 333-342

Author(s):

Roberto Limongi ◽

Angélica M. Silva

Keyword(s):

Response Time ◽

Short Term Memory ◽

Estimation Error ◽

Predictive Coding ◽

Time Estimation ◽

Prediction Errors ◽

Memory Scanning ◽

Short Term ◽

Term Memory ◽

Temporal Prediction

Abstract. The Sternberg short-term memory scanning task has been used to unveil cognitive operations involved in time perception. Participants produce time intervals during the task, and the researcher explores how task performance affects interval production – where time estimation error is the dependent variable of interest. The perspective of predictive behavior regards time estimation error as a temporal prediction error (PE), an independent variable that controls cognition, behavior, and learning. Based on this perspective, we investigated whether temporal PEs affect short-term memory scanning. Participants performed temporal predictions while they maintained information in memory. Model inference revealed that PEs affected memory scanning response time independently of the memory-set size effect. We discuss the results within the context of formal and mechanistic models of short-term memory scanning and predictive coding, a Bayes-based theory of brain function. We state the hypothesis that our finding could be associated with weak frontostriatal connections and weak striatal activity.

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