An increasing number of enterprises have adopted cloud computing to manage their important business applications in distributed green cloud &#x0028 DGC &#x0029 systems for low response time and high cost-effectiveness in recent years. Task scheduling and resource allocation in DGCs have gained more attention in both academia and industry as they are costly to manage because of high energy consumption. Many factors in DGCs, e.g., prices of power grid, and the amount of green energy express strong spatial variations. The dramatic increase of arriving tasks brings a big challenge to minimize the energy cost of a DGC provider in a market where above factors all possess spatial variations. This work adopts a G &#x002F G &#x002F 1 queuing system to analyze the performance of servers in DGCs. Based on it, a single-objective constrained optimization problem is formulated and solved by a proposed simulated-annealing-based bees algorithm &#x0028 SBA &#x0029 to find SBA can minimize the energy cost of a DGC provider by optimally allocating tasks of heterogeneous applications among multiple DGCs, and specifying the running speed of each server and the number of powered-on servers in each GC while strictly meeting response time limits of tasks of all applications. Realistic data-based experimental results prove that SBA achieves lower energy cost than several benchmark scheduling methods do.

Fine-grained resource provisioning and task scheduling for heterogeneous applications in distributed green clouds

Cloud computing concepts offer effective and efficient tools for addressing resource-hungry computational problems. While conventional methods, architectures, and processing techniques may limit cloud data center performance, software-defined cloud computing (SDCC) is an approach where virtualization services to all network resources in a dc are software-defined and where software-defined networking (SDN) and cloud computing go hand in hand. SDCC-related concepts change the previous state of affairs by promoting the centralized control of networking functions in a data center. A key objective of developing software-driven cloud infrastructure is that the networking hardware, software, storage, security, and network traffic management is open and interoperable. This facilitates easy installation and management of networking functions in the cloud infrastructure. Employing SDCC concepts to cloud data centers can improve resource administration challenges to a greater extent. This paper presents a survey on SDCC. We begin by introducing SDCC environments and explain its main architectural components. We identify the essential contributions of various developments to this field and discuss the implementation challenges and limitations faced in their adoption. We also explore the potential of SDCC in two domains, namely, resource orchestration and application development, as case studies of specific interest. In an attempt to anticipate the future evolution, we discuss the important research opportunities and challenges in this promising field.

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Software-Defined Cloud Computing: A Systematic Review on Latest Trends and Developments

Using social media web sites is among the most common activity of today's children and adolescents. Any web site that allows social interaction is considered a social media site, including social networking sites such as Facebook, MySpace, and Twitter; gaming sites and virtual worlds such as Club Penguin, Second Life, and the Sims; video sites such as YouTube; and blogs. Such sites offer today's youth a portal for entertainment and communication and have grown exponentially in recent years. For this reason, it is important that parents become aware of the nature of social media sites, given that not all of them are healthy environments for children and adolescents. Social networks have removed all the communication and interaction barriers, and now one can communicate his perception and thoughts over a variety of topics. Students and experts are able to share and communicate with like-minded people and can ask for the input and opinion on a particular topic. Another positive impact of social networking sites is to unite people on a huge platform for the achievement of some specific objective. This is very important to bring the positive change in society.

Effects of Social Media

A multi-objective approach for energy-efficient and reliable dynamic VM consolidation in cloud data centers

The ease and affordability offered by the cloud computing has attracted large number of customers towards it. Cloud service providers offer its services, to the cloud customers, usually in form of Virtual Machines (VMs). With the growth in the number of customers, cloud data centers encounter overwhelming number of VM requests. These requests need to be mapped on the real cloud hardware and therefore, VM placement has been an important research area in the cloud research community. Virtual machine placement, being an NP hard problem, is modelled as an optimization problem with the objective to optimize resource wastage. Dragonfly Algorithm (DA), a nature inspired technique, originates from static and dynamic swarming behavior of dragonfly and is well suited to solve VM placement problem. Therefore, in the proposed work, a modified dragonfly algorithm is applied for VM placement for better resource utilization at cloud data centers. The performance of the proposed model is analyzed through simulation and comparative study. Observations, obtained from the experiments, exhibit the superiority of the proposed model in solving VM placement problem.

Modified Dragonfly Algorithm for Optimal Virtual Machine Placement in Cloud Computing

Optimistic virtual machine placement in cloud data centers using queuing approach

An individual Human computer interference system is being introduced. In olden times, as an input device the mouse and keyboard were used by human computer interference system. Those people who are suffering from certain disease or illness cannot be able to operate computers. The idea of controlling the computers with the eyes will serve a great use for handicapped and disabled person. Also this type of control will eliminate the help required by other person to handle the computer. This measure will be the most useful for the person who is without hands through which they can operate with the help of their eye movements. The movement of the cursor is directly associated with the center of the pupil. Hence our first step would be detecting the center of point pupil. This process of pupil detection is implemented using the Raspberry Pi and OpenCV. The Raspberry Pi has a SD/MMC card slot which is used for placing the SD card. The SD card is boosted with the operating system that is required starting up of Raspberry Pi. The Raspberry PI will get executed once the application program is loaded into it.

Eyeball based Cursor Movement Control

This report shows the outcome by applying large scale data mining techniques on the Finnish roads. From the research study it is very difficult task to perform because the collected data have uncertainty, incomplete and error values. So the data exploration is a challenging task. The\n data used in the process have been collected from Finnish road administration data sets. The data used in the process have been collected from Finnish road administration data sets. The main target of our project is to look into practicability of Robust clustering, to find the associations\n and repeated item sets and applying apprehend methods for the analysis of road accidents. While the results display the selected mining techniques and methods were capable to the understandable patterns. To calculate the accident frequency count as a parameter /c-means algorithm is used to\n cluster the locations. To characterize the surface conditions association rule mining is used. data mining skills disclosed different environmental reasons associated with road accidents. Intersection on highways have been identified as a dangerous for fatal accidents.

Car Accident Detection and Notification System Using Smartphone

Social media monitoring has been growing day by day so analyzing of social data plays an important role in knowing customer behavior. So we are analyzing Social data such as Twitter Tweets using sentiment analysis which checks the attitude of User review on movies. This paper develops a combined dictionary based on social media keywords and online review and also find hidden relationship pattern from these keyword. In recent years, shopping online is becoming more and more popular. Online reviews play a very important role in today's ecommerce decision making business. A large part of the customer population i.e. customers read reviews of products or stores before deciding where to buy and where to buy. Since writing fake reviews / frauds comes with significant gains, there has been a huge increase in fake spam views on online review websites. Poor basic reviews or fake reviews or spam review reviews are not true. A good review of the target item can attract more customers and increase sales; Poor reviews of the target item may result in lower demand and decreased sales. This false / fraudulent review was deliberately written to mislead potential customers in order to induce / deceive or defile their prominence. Our work aims to identify whether the review is false or factual. Naïve Bayes Classifier, Eristic Regression and Support Vector Machines are the classifiers used in our work.

Detection of Fake Online Reviews Using Semi-Supervised and Supervised Learning

In the modern era, cancer is a major public health concern. Breast cancer is one of the leading causes of death among women. Breast cancer is becoming the top cause of death in women worldwide. Early identification of breast cancer allows patients to receive proper treatment, improving their chances of survival. The proposed Generative Adversarial Networks (GAN) approach is designed to aid in the detection and diagnosis of breast cancer. GANs are deep learning algorithms that generate new data instances that mimic the training data. GAN is made up of two parts: a generator that learns to generate false data and a discriminator that learns from this false data. Furthermore, the histogram of oriented gradients (HOG) is utilized as a feature descriptor in image processing and other computer vision techniques. Gradient orientation in the detection window and region of interest is determined by the histogram of oriented gradients descriptor approach. Using an image dataset and deep learning techniques, the proposed research (GAN-HOG) aims to improve the efficiency and performance of breast cancer diagnosis. The deep learning method is used here to analyze image data by segmenting and classifying the input photographs from the dataset. Unlike many existing nonlinear classification models, the proposed method employs a conditional distribution for the outputs. The proposed model GAN-HOG had an accuracy of 98.435%, a ResNet50 accuracy of 87.826%, a DCNN accuracy of 92.547%, a VGG16 accuracy of 89.453%, and an SVM accuracy of 95.546%.

Anitha Ponraj

Papers

Optimistic virtual machine placement in cloud data centers using queuing approach

Eyeball based Cursor Movement Control

Car Accident Detection and Notification System Using Smartphone

Detection of Fake Online Reviews Using Semi-Supervised and Supervised Learning

Deep Learning with Histogram of Oriented Gradients- based Computer-Aided Diagnosis for Breast Cancer Detection and Classification