Endpoint-transparent Multipath Transport with Software-defined Networks

Multipath forwarding consists of using multiple paths simultaneously to transport data over the network. While most such techniques require endpoint modifications, we investigate how multipath forwarding can be done inside the network, transparently to endpoint hosts. With such a network-centric approach, packet reordering becomes a critical issue as it may cause critical performance degradation. We present a Software Defined Network architecture which automatically sets up multipath forwarding, including solutions for reordering and performance improvement, both at the sending side through multipath scheduling algorithms, and the receiver side, by resequencing out-of-order packets in a dedicated in-network buffer. We implemented a prototype with commonly available technology and evaluated it in both emulated and real networks. Our results show consistent throughput improvements, thanks to the use of aggregated path capacity. We give comparisons to Multipath TCP, where we show our approach can achieve a similar performance while offering the advantage of endpoint transparency

Fog Computing: A Taxonomy, Survey and Future Directions

In recent years, the number of Internet of Things (IoT) devices/sensors has increased to a great extent. To support the computational demand of real-time latency-sensitive applications of largely geo-distributed IoT devices/sensors, a new computing paradigm named "Fog computing" has been introduced. Generally, Fog computing resides closer to the IoT devices/sensors and extends the Cloud-based computing, storage and networking facilities. In this chapter, we comprehensively analyse the challenges in Fogs acting as an intermediate layer between IoT devices/ sensors and Cloud datacentres and review the current developments in this field. We present a taxonomy of Fog computing according to the identified challenges and its key features.We also map the existing works to the taxonomy in order to identify current research gaps in the area of Fog computing. Moreover, based on the observations, we propose future directions for research

A Case for Data Centre Traffic Management on Software Programmable Ethernet Switches

Virtualisation first and cloud computing later has led to a consolidation of workload in data centres that also comprises latency-sensitive application domains such as High Performance Computing and telecommunication. These types of applications require strict latency guarantees to maintain their Quality of Service. In virtualised environments with their churn, this demands for adaptability and flexibility to satisfy. At the same time, the mere scale of the infrastructures favours commodity (Ethernet) over specialised (Infiniband) hardware. For that purpose, this paper introduces a novel traffic management algorithm that combines Rate-limited Strict Priority and Deficit round-robin for latency-aware and fair scheduling respectively. In addition, we present an implementation of this algorithm on the bmv2 P4 software switch by evaluating it against standard priority-based and best-effort scheduling.Comment: 8th IEEE International Conference on Cloud Networking (IEEE CloudNet 2019

Single-Board-Computer Clusters for Cloudlet Computing in Internet of Things

The number of connected sensors and devices is expected to increase to billions in the near future. However, centralised cloud-computing data centres present various challenges to meet the requirements inherent to Internet of Things (IoT) workloads, such as low latency, high throughput and bandwidth constraints. Edge computing is becoming the standard computing paradigm for latency-sensitive real-time IoT workloads, since it addresses the aforementioned limitations related to centralised cloud-computing models. Such a paradigm relies on bringing computation close to the source of data, which presents serious operational challenges for large-scale cloud-computing providers. In this work, we present an architecture composed of low-cost Single-Board-Computer clusters near to data sources, and centralised cloud-computing data centres. The proposed cost-efficient model may be employed as an alternative to fog computing to meet real-time IoT workload requirements while keeping scalability. We include an extensive empirical analysis to assess the suitability of single-board-computer clusters as cost-effective edge-computing micro data centres. Additionally, we compare the proposed architecture with traditional cloudlet and cloud architectures, and evaluate them through extensive simulation. We finally show that acquisition costs can be drastically reduced while keeping performance levels in data-intensive IoT use cases.Ministerio de Economía y Competitividad TIN2017-82113-C2-1-RMinisterio de Economía y Competitividad RTI2018-098062-A-I00European Union’s Horizon 2020 No. 754489Science Foundation Ireland grant 13/RC/209

Intra- Datacenter Challenges; System Perspective

Invited presentation at ECOC Sunday workshop with title: Data Center Networks: Meeting the emerging requirements for capacity, cost, energy consumption and reac

Challenges in real-time virtualization and predictable cloud computing

Cloud computing and virtualization technology have revolutionized general-purpose computing applications in the past decade. The cloud paradigm offers advantages through reduction of operation costs, server consolidation, flexible system configuration and elastic resource provisioning. However, despite the success of cloud computing for general-purpose computing, existing cloud computing and virtualization technology face tremendous challenges in supporting emerging soft real-time applications such as online video streaming, cloud-based gaming, and telecommunication management. These applications demand real-time performance in open, shared and virtualized computing environments. This paper identifies the technical challenges in supporting real-time applications in the cloud, surveys recent advancement in real-time virtualization and cloud computing technology, and offers research directions to enable cloud-based real-time applications in the future