z-TORCH: An Automated NFV Orchestration and Monitoring Solution

Autonomous management and orchestration (MANO) of virtualized resources and services, especially in large-scale NFV environments, is a big challenge owing to the stringent delay and performance requirements expected of a variety of network services. The quality of decision (QoD) of a MANO system depends on the quality and timeliness of the information that it receives from the underlying monitoring system. The data generated by monitoring systems is a significant contributor to the network and processing load of MANO systems, impacting thus their performance. This raises a unique challenge: how to jointly optimize the QoD of MANO systems while at the same minimizing their monitoring loads at runtime? This is the main focus of this paper. In this context we propose a novel automated NFV orchestration solution called z-TORCH (zero Touch Orchestration) that jointly optimizes the orchestration and monitoring processes by exploiting machine learning techniques. The objective is to enhance the QoD of MANO systems achieving a near-optimal placement of VNFs at minimum monitoring costs.This work has received funding from the European Unions Horizon 2020 research and innovation programme under grant agreement No 761536 (5G-Transformer project

Datacenter Traffic Control: Understanding Techniques and Trade-offs

Datacenters provide cost-effective and flexible access to scalable compute and storage resources necessary for today's cloud computing needs. A typical datacenter is made up of thousands of servers connected with a large network and usually managed by one operator. To provide quality access to the variety of applications and services hosted on datacenters and maximize performance, it deems necessary to use datacenter networks effectively and efficiently. Datacenter traffic is often a mix of several classes with different priorities and requirements. This includes user-generated interactive traffic, traffic with deadlines, and long-running traffic. To this end, custom transport protocols and traffic management techniques have been developed to improve datacenter network performance. In this tutorial paper, we review the general architecture of datacenter networks, various topologies proposed for them, their traffic properties, general traffic control challenges in datacenters and general traffic control objectives. The purpose of this paper is to bring out the important characteristics of traffic control in datacenters and not to survey all existing solutions (as it is virtually impossible due to massive body of existing research). We hope to provide readers with a wide range of options and factors while considering a variety of traffic control mechanisms. We discuss various characteristics of datacenter traffic control including management schemes, transmission control, traffic shaping, prioritization, load balancing, multipathing, and traffic scheduling. Next, we point to several open challenges as well as new and interesting networking paradigms. At the end of this paper, we briefly review inter-datacenter networks that connect geographically dispersed datacenters which have been receiving increasing attention recently and pose interesting and novel research problems.Comment: Accepted for Publication in IEEE Communications Surveys and Tutorial

On the Necessity of Accounting for Resiliency in SFC

International audienceWhen deploying network service function chains the focus is usually given on metrics such as the cost, the latency, or the energy and it is assumed that the underlying cloud infrastructure provides resiliency mechanisms to handle with the disruptions occurring in the physical infrastructure. In this position paper, we advocate that while usual performance metrics are essential to decide on the deployment of network service function chains, the notion of resiliency should not be neglected as the choice of virtual-to-physical placement may dramatically improve the ability of the service chains to handle with failures of the infrastructure without requiring complex resiliency mechanisms

Towards efficient and cost-effective live migrations of virtual machines

As cloud computing and the use of virtual machines (VMs) have become a widespread phenomenon, a wide variety of optimization techniques have been invented for this field. One of them is live migration, which enables relocation of VMs between physical hosts without shutting them down. Since this feature has been implemented and simplified in the majority of popular virtualization platforms, IT administrators have begun migrating VMs regularly. There are many reasons for this, including load balancing, server consolidation and disaster recovery. This thesis have used a machine learning based algorithm to partition mi- gration marked VMs into migration groups, with the goals of minimizing network load and lower the time consumption. A new algorithm, proposed by this thesis, is used to provide additional cost-optimization