Live migration is an essential feature of virtualization that allows transfer of virtual machine from one physical server to another without interrupting the services running in virtual machine. Live migration facilitates workload balancing, fault tolerance, online system maintenance, consolidation of virtual machines etc. Unfortunately the disclosed vulnerabilities with the live migration pose significant security risks. Because of these security risks the industry is hesitant to adapt the technology for sensitive applications. This paper is an investigation of attacks on live migration of virtual machine and discusses the key proposed and implemented approaches to secure live migration.

Server virtualization is an emerging technology that provides efficient resource utilization and cost-saving benefits. It consolidates many physical servers into a single physical server saving the hardware resources, physical space, power-consumption, air conditioning capacity and man power to manage the servers. Thus virtualization assists "Green Technology". Live migration is an essential feature of virtualization that allows transition of a running virtual machine from one system to another without halting the virtual machine. Live migration extends the list of benefits server virtualization provides. Almost all virtualization softwares now include support for live migration of virtual machine. Live migration is in its infant stage where security of live migration is yet to be analyzed. The usages of live migration and security exploits over it have both increased over time. The security concern of live migration is a major factor for its adoption by the IT industry. In this paper we discuss the attack model on the virtualization system and design and implement a security framework for secure live migration of virtual machines. The framework is an integrated security solution that addresses role based access policy, network intrusion, firewall protection and encryption for secure live migration process. Keywordslive migration, live migration security, live migration attack model, role based access control policy, reactive IDS, inter VM attacks.I.

Human feelings are mental conditions of sentiments that emerge immediately as opposed to cognitive exertion. Some of the basic feelings are happy, angry, neutral, sad and surprise. These internal feelings of a person are reflected on the face as Facial Expressions. This paper presents a novel methodology for Facial Expression Analysis which will aid to develop a facial expression recognition system. This system can be used in real time to classify five basic emotions. The recognition of facial expressions is important because of its applications in many domains such as artificial intelligence, security and robotics. Many different approaches can be used to overcome the problems of Facial Expression Recognition (FER) but the best suited technique for automated FER is Convolutional Neural Networks(CNN). Thus, a novel CNN architecture is proposed and a combination of multiple datasets such as FER2013, FER+, JAFFE and CK+ is used for training and testing. This helps to improve the accuracy and develop a robust real time system. The proposed methodology confers quite good results and the obtained accuracy may give encouragement and offer support to researchers to build better models for Automated Facial Expression Recognition systems.

<span lang="EN-US">The ever-increasing sale of vehicles and the steady increase in population density in metropolitan cities have raised many growing concerns, most importantly commute time, air and noise pollution levels. Traffic congestion can be alleviated by opting using adaptive traffic light systems, instead of fixed-time traffic signals. In this paper, a system is proposed which can detect, classify and count vehicles passing through any traffic junction using a single camera (as opposed to multi-sensor approaches). The detection and classification are done using SSD Neural Network object detection algorithm. The count of each class (2-wheelers, cars, trucks, buses etc.) is used to predict the signal green-time for the next cycle. The model self-adjusts every cycle by utilizing weighted moving averages. This system works well because the change in the density of traffic on any given road is gradual, spanning multiple traffic stops throughout the day.</span>

Live migration is an essential feature in virtualization technology where a running Virtual Machine (VM) from one physical host is migrated to another physical host without any service disruptions. Indisputably the benefits reaped from VM migration are high availability, load balancing, energy saving and disaster recovery which are the desired data centre attributes. The migration is initiated by the administrator and part of the migration procedure is to make an informed decision to isolate and identify an appropriate VM to be migrated, lest an impressive performance may not be achieved. The decision of selecting the right candidate VM to be migrated depends on parameters like total migration time, down time, total transferred data and page dirty rate. The nature of the application influences these parameters. The paper addresses the analysis of some of these parameters empirically, and based on these data a correct VM to be migrated is chosen. In addition to the above, this paper also discusses the dynamic resource allocation for RSA algorithm and JMeter.