Skip to main content

Asp.Net Performance considerations while Architecting

Performance considerations while architecting Asp.Net
  1. Use Session State only if you need to
  2. Use View State only if you need to
  3. Avoid Auto event wireup
  4. Remove unnecessary Http Modules
  5. Encode request and response using ASCII instead of UTF
  6. Use optimal Authentication procedure. in order of increasing cost: None, Windows, Forms, Passpost. Make sure you use the cheapest one that best fits your needs.
  7. Aggressive Caching will improve performance. OutputCaching of many types
    • Duration—Time item exists in the cache
    • VaryByParam—Varies cache entries by Get/Post params
    • VaryByHeader—Varies cache entries by Http header
    • VaryByCustom—Varies cache entries by browser
    • Override to vary by whatever you want:
      • Fragment Caching—When it is not possible to store an entire page (privacy, personalization, dynamic content), you can use fragment caching to store parts of it for quicker retrieval later.
      •  VaryByControl—Varies the cached items by values of a control
    • Cache API—Provides extremely fine granularity for caching by keeping a hashtable of cached objects in memory (System.web.UI.caching). It also:
      • Includes Dependencies (key, file, time)
      • Automatically expires unused items
      • Supports Callbacks
Caching intelligently increases performance

Comments

Post a Comment

Popular posts from this blog

ABOD and its PyOD python module

Angle based detection By  Hans-Peter Kriegel, Matthias Schubert, Arthur Zimek  Ludwig-Maximilians-Universität München  Oettingenstr. 67, 80538 München, Germany Ref Link PyOD By  Yue Zhao   Zain Nasrullah   Department of Computer Science, University of Toronto, Toronto, ON M5S 2E4, Canada  Zheng Li jk  Northeastern University Toronto, Toronto, ON M5X 1E2, Canada I am combining two papers to summarize Anomaly detection. First one is Angle Based Outlier Detection (ABOD) and other one is python module that  uses ABOD along with over 20 other apis (PyOD) . This is third part in the series of Anomaly detection. First article exhibits survey that covered length and breadth of subject, Second article highlighted on data preparation and pre-processing.  Angle Based Outlier Detection. Angles are more stable than distances in high dimensional spaces for example the popularity of cosine-based similarity measures for text data. Object o is an out
Post a Comment
Read more

TableSense: Spreadsheet Table Detection with Convolutional Neural Networks

 - By Haoyu Dong, Shijie Liu, Shi Han, Zhouyu Fu, Dongmei Zhang Microsoft Research, Beijing 100080, China. Beihang University, Beijing 100191, China Paper Link Abstract Spreadsheet table detection is the task of detecting all tables on a given sheet and locating their respective ranges. Automatic table detection is a key enabling technique and an initial step in spreadsheet data intelligence. However, the detection task is challenged by the diversity of table structures and table layouts on the spreadsheet. Considering the analogy between a cell matrix as spreadsheet and a pixel matrix as image, and encouraged by the successful application of Convolutional Neural Networks (CNN) in computer vision, we have developed TableSense, a novel end-to-end framework for spreadsheet table detection. First, we devise an effective cell featurization scheme to better leverage the rich information in each cell; second, we develop an enhanced convolutional neural network model for tab
2 comments
Read more

DEEP LEARNING FOR ANOMALY DETECTION: A SURVEY

-By  Raghavendra Chalapathy  University of Sydney,  Capital Markets Co-operative Research Centre (CMCRC)  Sanjay Chawla  Qatar Computing Research Institute (QCRI),  HBKU  Paper Link Anomaly detection also known as outlier detection is the identification of rare items, events or observations which raise suspicions by differing significantly from the majority of the data. Typically the anomalous items will translate to some kind of problem such as bank fraud, a structural defect, medical problems or errors in a text. Anomalies are also referred to as outliers, novelties, noise, deviations and exceptions Hawkins defines an outlier as an observation that deviates so significantly from other observations as to arouse suspicion that it was generated by a different mechanism. Aim of this paper is two-fold, First is a structured and comprehensive overview of research methods in deep learning-based anomaly detection. Furthermore the adoption of these methods
1 comment
Read more