Video content, of which YouTube is a major part, constitutes a large share of residential Internet traffic. In this paper, we analyse the user demand patterns for YouTube in two metropolitan access networks with more than 1 million requests over three consecutive weeks in the first network and more than 600,000 requests over four consecutive weeks in the second network.
In this letter, we propose a novel cooperative caching scheme for the next-generation Internet — content oriented networking (CON), trying to minimize the content access delay for mobile users. We formulate the caching problem as a mixed integer programming model and propose a heuristic solution based on Lagrangian relaxation. Simulation results show that this scheme can greatly reduce content access delay.
Software-Defined Networking (SDN) promises the vision of more flexible and manageable networks, but requires certain level of programmability in the data plane. Current industry insight holds
that programmable network processors are of lower performance
than their hard-coded counterparts, such as Ethernet chips. This
represents a roadblock to SDN adoption.
This paper presents Cloud Atlas, a SDN abstraction and API extending the Quantum virtual network into the WAN. Cloud Atlas is built on top of existing WAN network services (L1-, L2-, and L3VPNs) that do support QoS. Cloud Atlas makes these services available to OpenStack through a tight integration with Quantum. This paper discusses two prototypes we have built of Cloud Atlas, one based on command line scripts and one based on a network management system.
Distribution of media data over the Internet is increasing in popularity and volume. This poses challenges not only for network operators but also for service providers when it comes to serving the demand in a cost-efficient way. In this paper, we approach this problem by investigating the potential of co-operative approaches where locality in space (users in the same network) and locality in time (concurrent downloads) are exploited such that as many requests as possible may be handled inside the access and metro networks.
In an increasingly competitive environment it is more important than ever for operators to keep their end users satisfied. User satisfaction is often characterised in terms of Quality of Experience (QoE), a subjective metric with multiple dimensions such as expectations, content, terminal, environment, cost and performance. QoE is typically quantified as MOS, mean opinion score, which is obtained by averaging the ranks of a number of voluntary users for controlled combinations content/ terminals/performance etc. While this approach has many advantages, there are also a number of difficulties such as representativeness (the number of users as well as the number of objects and devices all have to be kept small); validity (the results may be biased by the situation, the setting, the remuneration and so on); and applicability (it is not clear how different numbers map to notions such as “acceptable” or “unacceptable” and operators alone cannot do very much about factors such as content).
Quantifying quality of experience for network applications is challenging as it is a subjective metric with multiple dimensions such as user expectation, satisfaction, and overall experience. Today, despite various techniques to support differentiated Quality of Service (QoS), the operators still lack of automated methods to translate QoS to QoE, especially for general web applications.
As mobile networks are witnessing huge growth in the volumes of data traffic from smartphone and tablet. Mobile operators start to look into CDN and caching in order to offload increasingly overloaded mobile networks, reduce network and peering cost, and improve mobile user's quality of experience.
Energy efficiency features are being integrated in network protocols and management systems. Simulations can provide input on how a particular algorithm would perform in different network conditions. However, building an environment that is able to comprehensively account for interactions between different network functions is difficult.
High availability is a key non-functional requirement that software and telecom service providers strive to achieve. With the on-going shift to Cloud computing, the challenges of satisfying the high availability requirement become more arduous, as the Cloud introduces additional features, such as on-demand access, scalability, virtualization, etc. that add to the complexity of the high availability solution.