An OptiAssign-PSO Based Optimisation for Multi-Objective Multi-Level Multi-Task Scheduling in Cloud Computing Environment
Advanced Analytics for Disease Tracking & Remote Intravenous Injection Monitoring
DevKraft - Fueling Collaboration in Coding Challenges
Comparative Security and Compliance Analysis of Serverless Computing Platforms: AWS Lambda, Azure Functions, and Google Cloud Functions
Blockchain Healthcare Management using Patients
A Comprehensive Review of Security Issues in Cloud Computing
An Extended Min-Min Scheduling Algorithm in Cloud Computing
Data Quality Evaluation Framework for Big Data
An Architectural Framework for Ant Lion Optimization-based Feature Selection Technique for Cloud Intrusion Detection System using Bayesian Classifier
Be Mindful of the Move: A Swot Analysis of Cloud Computing Towards the Democratization of Technology
GridSim Installation and Implementation Process
A Survey on Energy Aware Job Scheduling Algorithms in Cloud Environment
Genetic Algorithm Using MapReduce - A Critical Review
Clustering based Cost Optimized Resource Scheduling Technique in Cloud Computing
Encroachment of Cloud Education for the Present Educational Institutions
Emerging trends such as cloud computing and big data have altered the requirements of future internet, for which low latency, extraordinary bandwidth and dynamic management are very significant. In order to adapt to the new needs, Software-Defined Networking (SDN) has been considered as one of the most favorable solutions. In SDN approach, centralized entities called "controllers" manages and controls the network via well-defined APIs (Application Program Interface). The forwarding layer has a set of clear and definite rules. Traffic passing through these switches is compared with these rules and a match-action method is applied to this traffic. However, with the ever growing demand of traffic, the need of more sophisticated, secure and high performance controllers has increased. Therefore, in this paper, the authors have presents performance (in latency, throughput perspective) and security evaluation for some of the most well-known controllers: Maestro, Floodlight, NOX, OpenMul, Beacon, OpenIRIS. The survey shows that OpenIRIS controller has the lowest latency, and the OpenMul controller shows the highest throughput. Whereas, security wise OpenIRIS is the least vulnerable controller.
Cloud Computing has revolutionized the Information and Communication Technology (ICT) industry by enabling ondemand provisioning of elastic computing resources on a pay-as-you-go basis. Resource Scheduling is a way of determining schedule on which activities should be performed. Resource scheduling is a complicated task in a Cloud environment because of heterogeneity of the computing resources. To allocate the best resource to a Cloud job is a tedious task and the problem of finding the best resource – job pair according to Cloud consumer application requirements is an optimization problem. The main goal of the Cloud scheduler is to schedule the resources effectively and efficiently. Dispersion, heterogeneity and uncertainty of resources bring challenges to resource allocation, which cannot be satisfied with traditional resource allocation policies in Cloud circumstances. In this research paper, the clustering based cost optimized resource scheduling technique has been proposed. In clustering based resource scheduling, classification of these workloads is done through k-means clustering algorithm by assigning the weights to the different quality attributes. The experimental results gathered through Cloud environment clearly demonstrate that the proposed technique has better performance for cost as compared to the existing resource scheduling technique.
Mobile devices are widely deployed in the world, and many people use them to download and upload media such as videos and pictures. Traditional security approaches were proposed to secure the data exchange between users and the cloud. Information hiding techniques have been recently emerged in many different application areas. In a digital image watermarking system, watermark is embedded in an object and the object may be an image or audio or video. Image object have been used in this paper. But nowadays, files are uploaded and maintained in cloud servers, where malicious users access these watermarked images from cloud servers and remove the watermarks from the images and make use of them without any copyright from the data owner. To overcome this problem, this paper introduces the new method of image chunk. Image chunk will split the image into four parts of sections and upload them in a four different sections to the cloud server respectively. Therefore, users cannot access the image and also cannot remove the watermarks from the file. Also, the authors have proposed a file compression technique to optimize the memory size and quality for storage space in the server. It will reduce the bandwidth usage for file upload and downloads.
Keyword search in relational database is a method that has higher relevance in the current world. One very important thing is extracting data from a large database. So it reduces the personal and time consuming works. Data extraction from a large number of sets of database using the relevant keyword based on the users are needed. It is a very interactive and user-friendly. Keyword search enables the user to get information without knowing any database schemas or complex query languages like SQL (Structured Querry Language). Using a keyword in relational database, data extraction will be easier. The user doesn't want to know the query language for searching on the database. Study of keyword search using algorithms such as BANKS, DBXPLORE, DISCOVER, DEINIX in detailed explanation are given for further process. This process continues for future work which selects the one which is best based on the analyses. The main objective of this study about keyword searching in large database is to reduce the memory space and making an efficient retrieval of information; also it reduces the time of the user for retrieving the required data.
Educational data mining is one of the fields where there is lot of scope for research, which helps educational institutions to analyse the learning capability of the students. And also gives scope to the educational institutions to make modifications in the curriculum and also to change the teaching methodologies based upon the learning capability of a student. Here, this paper concentrates on the learning capability of the students in higher educational institutions. For that, a dataset of 300 records was collected with various socio-economical and graduate attribute factors. Various classification algorithms was performed on the dataset using Weka, an open source tool. Random forest classification algorithm was found as the best performing algorithm on the dataset. This algorithm was used to design an user interface which is used to predict the future state of a student.
