Evaluation of the Impact of Packet Drops due to AQM over Capacity Limited Paths

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Performance Evaluation of Triple Play Services Delivery with E2E QoS Provisioning

The creation and wide use of new high quality demanding services (VoIP, High Quality Video Streaming) and the delivery of them over already saturated core and access network infrastructures have created the necessity for E2E QoS provisioning. Network Providers use at their infrastructures several kinds of mechanisms and techniques for providing QoS. Most known and widely used technologies are MPLS and DiffServ. The IEEE 802.16-2004 standard (WiMAX) refers to a promising wireless broadband technology with enhanced QoS support algorithms. This document presents an experimental network infrastructure providing E2E QoS, using a combination of MPLS and DiffServ technologies in the core network and WiMAX technology as the wireless access medium for high priority services (VoIP, High Quality Video Streaming) transmission. The main scope is to map the traffic prioritization and classification attributes of the core network to the access network in a way which does not affect the E2E QoS provisioning. The performance evaluation will be done by introducing different kinds of traffic scenarios in a saturated and overloaded network environment. The evaluation will prove that this combination made feasible the E2E QoS provisioning while keeping the initial constrains as well as the services delivered over a wireless network

Virtualizing the access network via open APIs

Residential broadband consumption is growing rapidly, in-creasing the gap between ISP costs and revenues. Mean-while, proliferation of Internet-enabled devices is congesting access networks, frustrating end-users and content providers. We propose that ISPs virtualize access infrastructure, using open APIs supported through SDN, to enable dynamic and controlled sharing amongst user streams. Content providers can programmatically provision capacity to user devices to ensure quality of experience, users can match the degree of virtualization to their usage pattern, and ISPs can real-ize per-stream revenues by slicing their network resources. Using video streaming and bulk transfers as examples, we develop an architecture that specifies the interfaces between the ISP, content provider, and user. We propose an algo-rithm for optimally allocating network resources, leveraging bulk transfer time elasticity and access path space diver-sity. Simulations using real traces show that virtualization can reduce video degradation by over 50%, for little extra bulk transfer delay. Lastly, we prototype our system and validate it in a test-bed with real video streaming and file transfers. Our proposal is a first step towards the long-term goal of realizing open and agile access network service quality management that is acceptable to users, ISPs and content providers alike

Congestion-Aware Scalable Video Streaming

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LoLa: Low-Latency Realtime Video Conferencing over Multiple Cellular Carriers

LoLa is a novel multi-path system for video conferencing applications over cellular networks. It provides significant gains over single link solutions when the link quality over different cellular networks fluctuate dramatically and independently over time, or when aggregating the throughput across different cellular links improves the perceived video quality. LoLa achieves this by continuously estimating the quality of available cellular links to decide how to strip video packets across them without inducing delays or packet drops. It is also tightly coupled with state-of-the-art video codec to dynamically adapt video frame size to respond quickly to changing network conditions. Using multiple traces collected over 4 different cellular operators in a large metropolitan city, we demonstrate that LoLa provides significant gains in terms of throughput and delays compared to state-of-the-art real-time video conferencing solution.Comment: 9 pages, 9 figure

Auto-tuning Distributed Stream Processing Systems using Reinforcement Learning

Fine tuning distributed systems is considered to be a craftsmanship, relying on intuition and experience. This becomes even more challenging when the systems need to react in near real time, as streaming engines have to do to maintain pre-agreed service quality metrics. In this article, we present an automated approach that builds on a combination of supervised and reinforcement learning methods to recommend the most appropriate lever configurations based on previous load. With this, streaming engines can be automatically tuned without requiring a human to determine the right way and proper time to deploy them. This opens the door to new configurations that are not being applied today since the complexity of managing these systems has surpassed the abilities of human experts. We show how reinforcement learning systems can find substantially better configurations in less time than their human counterparts and adapt to changing workloads