Landing AI on Networks: An equipment vendor viewpoint on Autonomous Driving Networks

The tremendous achievements of Artificial Intelligence (AI) in computer vision, natural language processing, games and robotics, has extended the reach of the AI hype to other fields: in telecommunication networks, the long term vision is to let AI fully manage, and autonomously drive, all aspects of network operation. In this industry vision paper, we discuss challenges and opportunities of Autonomous Driving Network (ADN) driven by AI technologies. To understand how AI can be successfully landed in current and future networks, we start by outlining challenges that are specific to the networking domain, putting them in perspective with advances that AI has achieved in other fields. We then present a system view, clarifying how AI can be fitted in the network architecture. We finally discuss current achievements as well as future promises of AI in networks, mentioning a roadmap to avoid bumps in the road that leads to true large-scale deployment of AI technologies in networks

Development of a virtualization systems architecture course for the information sciences and technologies department at the Rochester Institute of Technology (RIT)

Virtualization is a revolutionary technology that has changed the way computing is performed in data centers. By converting traditionally siloed computing assets to shared pools of resources, virtualization provides a considerable number of advantages such as more efficient use of physical server resources, more efficient use of datacenter space, reduced energy consumption, simplified system administration, simplified backup and disaster recovery, and a host of other advantages. Due to the considerable number of advantages, companies and organizations of various sizes have either migrated their workloads to virtualized environments or are considering virtualization of their workloads. As per Gartner Magic Quadrant for x86 Server Virtualization Infrastructure 2013 , roughly two-third of x86 server workloads are virtualized [1]. The need for virtualization solutions by companies and organizations has increased the demand for qualified virtualization professionals for planning, designing, implementing, and maintaining virtualized infrastructure of different scales. Although universities are the main source for educating IT professionals, the field of information technology is so dynamic and changing so rapidly that not all universities can keep pace with the change. As a result, providing the latest technology that is being used in the information technology industry in the curriculums of universities is a big advantage for information technology universities. Taking into consideration the trend toward virtualization in computing environments and the great demand for virtualization professionals in the industry, the faculty of Information Sciences and Technologies department at RIT decided to prepare a graduate course in the master\u27s program in Networking and System Administration entitled Virtualization Systems Architecture , which better prepares students to a find a career in the field of enterprise computing. This research is composed of five chapters. It starts by briefly going through the history of computer virtualization and exploring when and why it came into existence and how it evolved. The second chapter of the research goes through the challenges in virtualization of the x86 platform architecture and the solutions used to overcome the challenges. In the third chapter, various types of hypervisors are discussed and the advantages and disadvantages of each one are discussed. In the fourth chapter, the architecture and features of the two leading virtualization solutions are explored. Then in the final chapter, the research goes through the contents of the Virtualization Systems Architecture course

Scalable architectures for platform-as-a-service clouds: performance and cost analysis

Scalability is a significant feature of cloud computing, which ad-dresses to increase or decrease the capacities of allocated virtual resources at application, platform, database and infrastructure level on demand. We investigate scalable architecture solutions for cloud PaaS that allow services to utilize the resources dynamically and effectively without directly affecting users. We have implemented scalable architectures with different session state management solutions, deploying an online shopping cart application in a PaaS solution, and measuring the performance and cost under three server-side session state providers: Caching, SQL database and NoSQL database. A commercial solution with its supporting state management components has been used. Particularly when re-architecting software for the cloud, the trade-off between performance, scalability and cost implications needs to be discussed