Automatic Parameter Configuration for Cloud Infrastructures by Design Pattern Extraction

2013 ◽  
Author(s):  
Tetsuya Uchiumi ◽  
Shinya Kitajima ◽  
Shinji Kikuchi ◽  
Yasuhide Matsumoto
Keyword(s):  
Design Pattern ◽  
Pattern Extraction ◽  
Parameter Configuration ◽  
Cloud Infrastructures

Cloud-Based Multimedia Content Protection System

2020 ◽  
pp. 164-169
Author(s):  
B. Aparna ◽  
S. Madhavi ◽  
G. Mounika ◽  
P. Avinash ◽  
S. Chakravarthi
Keyword(s):  
Large Scale ◽  
Protection System ◽  
Multimedia Content ◽  
Computationally Efficient ◽  
Content Protection ◽  
Protection Systems ◽  
Multimedia Content Protection ◽  
High Scalability ◽  
Cloud Infrastructures ◽  
Rapid Deployment

We propose a new design for large-scale multimedia content protection systems. Our design leverages cloud infrastructures to provide cost efficiency, rapid deployment, scalability, and elasticity to accommodate varying workloads. The proposed system can be used to protect different multimedia content types, including videos, images, audio clips, songs, and music clips. The system can be deployed on private and/or public clouds. Our system has two novel components: (i) method to create signatures of videos, and (ii) distributed matching engine for multimedia objects. The signature method creates robust and representative signatures of videos that capture the depth signals in these videos and it is computationally efficient to compute and compare as well as it requires small storage. The distributed matching engine achieves high scalability and it is designed to support different multimedia objects. We implemented the proposed system and deployed it on two clouds: Amazon cloud and our private cloud. Our experiments with more than 11,000 videos and 1 million images show the high accuracy and scalability of the proposed system. In addition, we compared our system to the protection system used by YouTube and our results show that the YouTube protection system fails to detect most copies of videos, while our system detects more than 98% of them.

ANALYZING AND IMPROVING WEB APPLICATION QUALITY USING DESIGN PATTERNS

2012 ◽  
Vol 2 (2) ◽  
pp. 112-116
Author(s):  
Shikha Bhatia ◽  
Mr. Harshpreet Singh
Keyword(s):  
Web Application ◽  
Design Pattern ◽  
Design Patterns ◽  
Web Applications ◽  
Past Research ◽  
Software Systems ◽  
Interactive Software ◽  
Web Based ◽  
Software Application ◽  
Execution Speed

With the mounting demand of web applications, a number of issues allied to its quality have came in existence. In the meadow of web applications, it is very thorny to develop high quality web applications. A design pattern is a general repeatable solution to a generally stirring problem in software design. It should be noted that design pattern is not a finished product that can be directly transformed into source code. Rather design pattern is a depiction or template that describes how to find solution of a problem that can be used in many different situations. Past research has shown that design patterns greatly improved the execution speed of a software application. Design pattern are classified as creational design patterns, structural design pattern, behavioral design pattern, etc. MVC design pattern is very productive for architecting interactive software systems and web applications. This design pattern is partition-independent, because it is expressed in terms of an interactive application running in a single address space. We will design and analyze an algorithm by using MVC approach to improve the performance of web based application. The objective of our study will be to reduce one of the major object oriented features i.e. coupling between model and view segments of web based application. The implementation for the same will be done in by using .NET framework.

scholarly journals A Self-Adaptive Cuckoo Search Algorithm Using a Machine Learning Technique

Mathematics ◽  
2021 ◽  
Vol 9 (16) ◽  
pp. 1840
Author(s):  
Nicolás Caselli ◽  
Ricardo Soto ◽  
Broderick Crawford ◽  
Sergio Valdivia ◽  
Rodrigo Olivares
Keyword(s):  
Machine Learning ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Set Covering ◽  
Covering Problem ◽  
Parameter Configuration ◽  
Self Tuning ◽  
Problem Solvers

Metaheuristics are intelligent problem-solvers that have been very efficient in solving huge optimization problems for more than two decades. However, the main drawback of these solvers is the need for problem-dependent and complex parameter setting in order to reach good results. This paper presents a new cuckoo search algorithm able to self-adapt its configuration, particularly its population and the abandon probability. The self-tuning process is governed by using machine learning, where cluster analysis is employed to autonomously and properly compute the number of agents needed at each step of the solving process. The goal is to efficiently explore the space of possible solutions while alleviating human effort in parameter configuration. We illustrate interesting experimental results on the well-known set covering problem, where the proposed approach is able to compete against various state-of-the-art algorithms, achieving better results in one single run versus 20 different configurations. In addition, the result obtained is compared with similar hybrid bio-inspired algorithms illustrating interesting results for this proposal.

scholarly journals GPU-Enabled Serverless Workflows for Efficient Multimedia Processing

2021 ◽  
Vol 11 (4) ◽  
pp. 1438
Author(s):  
Sebastián Risco ◽  
Germán Moltó
Keyword(s):  
Video Processing ◽  
Cost Effective ◽  
Multimedia Processing ◽  
Computing Services ◽  
Video Frames ◽  
Open Source Framework ◽  
Cloud Infrastructures ◽  
Aws Lambda ◽  
Runtime Environments

Serverless computing has introduced scalable event-driven processing in Cloud infrastructures. However, it is not trivial for multimedia processing to benefit from the elastic capabilities featured by serverless applications. To this aim, this paper introduces the evolution of a framework to support the execution of customized runtime environments in AWS Lambda in order to accommodate workloads that do not satisfy its strict computational requirements: increased execution times and the ability to use GPU-based resources. This has been achieved through the integration of AWS Batch, a managed service to deploy virtual elastic clusters for the execution of containerized jobs. In addition, a Functions Definition Language (FDL) is introduced for the description of data-driven workflows of functions. These workflows can simultaneously leverage both AWS Lambda for the highly-scalable execution of short jobs and AWS Batch, for the execution of compute-intensive jobs that can profit from GPU-based computing. To assess the developed open-source framework, we executed a case study for efficient serverless video processing. The workflow automatically generates subtitles based on the audio and applies GPU-based object recognition to the video frames, thus simultaneously harnessing different computing services. This allows for the creation of cost-effective highly-parallel scale-to-zero serverless workflows in AWS.

scholarly journals Cost-Performance Evaluation of a Recognition Service of Livestock Activity Using Aerial Images

2021 ◽  
Vol 13 (12) ◽  
pp. 2318
Author(s):  
Darío G. Lema ◽  
Oscar D. Pedrayes ◽  
Rubén Usamentiaga ◽  
Daniel F. García ◽  
Ángela Alonso
Keyword(s):  
Performance Evaluation ◽  
Local Solution ◽  
Aerial Images ◽  
Cost Ratio ◽  
Activity Detection ◽  
Average Precision ◽  
Cost Performance ◽  
Detection Algorithms ◽  
Cloud Infrastructures ◽  
Animal Detection

The recognition of livestock activity is essential to be eligible for subsides, to automatically supervise critical activities and to locate stray animals. In recent decades, research has been carried out into animal detection, but this paper also analyzes the detection of other key elements that can be used to verify the presence of livestock activity in a given terrain: manure piles, feeders, silage balls, silage storage areas, and slurry pits. In recent years, the trend is to apply Convolutional Neuronal Networks (CNN) as they offer significantly better results than those obtained by traditional techniques. To implement a livestock activity detection service, the following object detection algorithms have been evaluated: YOLOv2, YOLOv4, YOLOv5, SSD, and Azure Custom Vision. Since YOLOv5 offers the best results, producing a mean average precision (mAP) of 0.94, this detector is selected for the creation of a livestock activity recognition service. In order to deploy the service in the best infrastructure, the performance/cost ratio of various Azure cloud infrastructures are analyzed and compared with a local solution. The result is an efficient and accurate service that can help to identify the presence of livestock activity in a specified terrain.

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