Insights from Analysis of Video Streaming Data to Improve Resource Management

Today a large portion of Internet traffic is video. Over The Top (OTT) service providers offer video streaming services by creating a large distributed cloud network on top of a physical infrastructure owned by multiple entities. Our study explores insights from video streaming activity by analyzing data collected from Korea's largest OTT service provider. Our analysis of nationwide data shows interesting characteristics of video streaming such as correlation between user profile information (e.g., age, sex) and viewing habits, viewing habits of users (when do the users watch? using which devices?), viewing patterns (early leaving viewer vs. steady viewer), etc. Video on Demand (VoD) streaming involves costly (and often limited) compute, storage, and network resources. Findings from our study will be beneficial for OTTs, Content Delivery Networks (CDNs), Internet Service Providers (ISPs), and Carrier Network Operators, to improve their resource allocation and management techniques.Comment: This is a preprint electronic version of the article accepted to IEEE CloudNet 201

Auto-Scaling Network Resources using Machine Learning to Improve QoS and Reduce Cost

Virtualization of network functions (as virtual routers, virtual firewalls, etc.) enables network owners to efficiently respond to the increasing dynamicity of network services. Virtual Network Functions (VNFs) are easy to deploy, update, monitor, and manage. The number of VNF instances, similar to generic computing resources in cloud, can be easily scaled based on load. Hence, auto-scaling (of resources without human intervention) has been receiving attention. Prior studies on auto-scaling use measured network traffic load to dynamically react to traffic changes. In this study, we propose a proactive Machine Learning (ML) based approach to perform auto-scaling of VNFs in response to dynamic traffic changes. Our proposed ML classifier learns from past VNF scaling decisions and seasonal/spatial behavior of network traffic load to generate scaling decisions ahead of time. Compared to existing approaches for ML-based auto-scaling, our study explores how the properties (e.g., start-up time) of underlying virtualization technology impacts Quality of Service (QoS) and cost savings. We consider four different virtualization technologies: Xen and KVM, based on hypervisor virtualization, and Docker and LXC, based on container virtualization. Our results show promising accuracy of the ML classifier using real data collected from a private ISP. We report in-depth analysis of the learning process (learning-curve analysis), feature ranking (feature selection, Principal Component Analysis (PCA), etc.), impact of different sets of features, training time, and testing time. Our results show how the proposed methods improve QoS and reduce operational cost for network owners. We also demonstrate a practical use-case example (Software-Defined Wide Area Network (SD-WAN) with VNFs and backbone network) to show that our ML methods save significant cost for network service leasers

Internet of things mobility forensics.

Proceedings of the 2016 Information Security Research and Education (INSuRE) Conference (INSuRECon-16). The inaugural INSuREcon Conference was held on September 30, 2016. The conference was held virtually using Cisco Webex online meeting and video conferencing software. Five papers were accepted out of 8 submissions. During the conference, each paper was presented by an author at the conference and the audience was provided the opportunity to ask questions of the presenter

Dynamic workload migration over optical backbone network to minimize data center electricity cost

As more organizations rapidly adopt cloud services, energy consumption in data centers (DCs) is increasing such that today Information and Communication Technology (ICT) has become a major consumer of energy. A large portion of ICT energy consumption is used to power servers running in DCs and the network they use to communicate. In this study, we consider that, often, energy cost at a particular DC is related to the electricity price regulated by Independent System Operators / Regional Transmission Organizations (ISOs/RTOs). As these prices vary in time and depend on the geographical locations of the DCs, recent studies have shown that the spatio-temporal variations of electricity price can be exploited to reduce electricity cost. While most prior works consider a quasi-static scenario with known workload patterns, our study proposes a dynamic workload-aware algorithm that exploits the spatio-temporal variations of electricity costs with the goal to minimize the energy cost in ICT. Our algorithm uses dynamic request rerouting and live virtual machine (VM) migration to move workloads to DCs with lower electricity cost. We consider VM migration cost (including electricity cost at optical backbone network nodes), bandwidth constraints for migration, VM consolidation, constraints from Service Level Agreement (SLA), and administrative overhead of VM migration. Our simulation studies show that the proposed algorithm reduces operational cost and improves energy efficiency of data centers significantly

Auto-Scaling VNFs Using Machine Learning to Improve QoS and Reduce Cost

Virtualization of network functions (as virtual routers, virtual firewalls, etc.) enables network owners to efficiently respond to the increasing dynamicity of network services. Virtual Network Functions (VNFs) are easy to deploy, update, monitor, and manage. The number of VNF instances, similar to generic computing resources in cloud, can be easily scaled based on load. Auto-scaling (of resources without human intervention) has been investigated in academia and industry. Prior studies on auto-scaling use measured network traffic load to dynamically react to traffic changes. In this study, we propose a proactive Machine Learning (ML) based approach to perform auto-scaling of VNFs in response to dynamic traffic changes. Our proposed ML classifier learns from past VNF scaling decisions and seasonal/spatial behavior of network traffic load to generate scaling decisions ahead of time. Compared to existing approaches for ML-based auto- scaling, our study explores how the properties (e.g., start-up time) of underlying virtualization technology impacts QoS and cost savings. We consider four different virtualization technologies: Xen and KVM, based on hypervisor virtualization, and Docker and LXC, based on container virtualization. Our results show promising accuracy of the ML classifier. We also demonstrate using realistic traffic load traces and optical backbone network that our ML method improves QoS and saves significant cost for network owners as well as leasers