The Glasgow raspberry pi cloud: a scale model for cloud computing infrastructures

Data Centers (DC) used to support Cloud services often consist of tens of thousands of networked machines under a single roof. The significant capital outlay required to replicate such infrastructures constitutes a major obstacle to practical implementation and evaluation of research in this domain. Currently, most research into Cloud computing relies on either limited software simulation, or the use of a testbed environments with a handful of machines. The recent introduction of the Raspberry Pi, a low-cost, low-power single-board computer, has made the construction of a miniature Cloud DCs more affordable. In this paper, we present the Glasgow Raspberry Pi Cloud (PiCloud), a scale model of a DC composed of clusters of Raspberry Pi devices. The PiCloud emulates every layer of a Cloud stack, ranging from resource virtualisation to network behaviour, providing a full-featured Cloud Computing research and educational environment

Improving data centre network utilisation using near-optimal traffic engineering

Equal Cost Multiple Path (ECMP) forwarding is the most prevalent multipath routing used in Data Centre (DC) networks today. However, it fails to exploit increased path diversity that can be provided by traffic engineering techniques through the assignment of non-uniform link weights to optimise network resource usage. To this extent, constructing a routing algorithm that provides path diversity over non-uniform link weights (i.e., unequal cost links), simplicity in path discovery and optimality in minimising Maximum Link Utilisation (MLU) is non-trivial. In this paper, we have implemented and evaluated the Penalizing Exponential Flow-spliTing (PEFT) algorithm in a cloud DC environment based on two dominant topologies, canonical and fattree. In addition, we have proposed a new cloud DC topology which, with only a marginal modification of the current canonical tree DC architecture, can further reduce MLU and increase overall network capacity utilisation through PEFT routing

In-Line Service Measurements: Exploiting IPv6 Extension Headers

The ability to measure, monitor and control the service quality experienced by network traffic is becoming increasingly important as multiple traffic types are aggregated onto IP networks. This paper introduces a novel measurement technique for assessing performance metrics (e.g. one-way packet loss, delay, delay variation, and ‘goodput’) of IPv6 network flows. By exploiting native IPv6 extension headers, measurement triggers and measurement data are carried in the same packets as the payload data itself, providing a high level of probability that the behaviour of the real user traffic flows is being observed. A prototype implementation of this technique has been constructed and used to measure numerous properties of different application flows, over both wireline and wireless IPv6 environments. End-to-end, oneway delay and delay variation of real-time video streams have been measured, as well as the goodput of services operating on top of reliable transport. This measurement technique can be the basis for low-overhead, scalable, transparent and reliable measurement of individual and aggregate network flows that can be dynamically deployed where and when required in a multi-service IP environment

In-Line Service Measurements: Exploiting IPv6 Extension Headers

The ability to measure, monitor and control the service quality experienced by network traffic is becoming increasingly important as multiple traffic types are aggregated onto IP networks. This paper introduces a novel measurement technique for assessing performance metrics (e.g. one-way packet loss, delay, delay variation, and ‘goodput’) of IPv6 network flows. By exploiting native IPv6 extension headers, measurement triggers and measurement data are carried in the same packets as the payload data itself, providing a high level of probability that the behaviour of the real user traffic flows is being observed. A prototype implementation of this technique has been constructed and used to measure numerous properties of different application flows, over both wireline and wireless IPv6 environments. End-to-end, oneway delay and delay variation of real-time video streams have been measured, as well as the goodput of services operating on top of reliable transport. This measurement technique can be the basis for low-overhead, scalable, transparent and reliable measurement of individual and aggregate network flows that can be dynamically deployed where and when required in a multi-service IP environment

Longer is better: exploiting path diversity in data center networks

Data Center (DC) networks exhibit much more centralized characteristics than the legacy Internet, yet they are operated by similar distributed routing and control algorithms that fail to exploit topological redundancy to deliver better and more sustainable performance. Multipath protocols, for example, use node-local and heuristic information to only exploit path diversity between shortest paths. In this paper, we use a measurementbased approach to schedule flows over both shortest and nonshortest paths based on temporal network-wide utilization. We present the Baatdaat1 flow scheduling algorithm which uses spare DC network capacity to mitigate the performance degradation of heavily utilized links. Results show that Baatdaat achieves close to optimal Traffic Engineering by reducing network-wide maximum link utilization by up to 18% over Equal-Cost MultiPath (ECMP) routing, while at the same time improving flow completion time by 41% - 95%

Uncertainty-driven ensemble forecasting of QoS in Software Defined Networks

Software Defined Networking (SDN) is the key technology for combining networking and Cloud solutions to provide novel applications. SDN offers a number of advantages as the existing resources can be virtualized and orchestrated to provide new services to the end users. Such a technology should be accompanied by powerful mechanisms that ensure the end-to-end quality of service at high levels, thus, enabling support for complex applications that satisfy end users needs. In this paper, we propose an intelligent mechanism that agglomerates the benefits of SDNs with real-time 'Big Data' forecasting analytics. The proposed mechanism, as part of the SDN controller, supports predictive intelligence by monitoring a set of network performance parameters, forecasting their future values, and deriving indications on potential service quality violations. By treating the performance measurements as time-series, our mechanism employs a novel ensemble forecasting methodology to estimate their future values. Such predictions are fed to a Type-2 Fuzzy Logic system to deliver, in real-time, decisions related to service quality violations. Such decisions proactively assist the SDN controller for providing the best possible orchestration of the virtualized resources. We evaluate the proposed mechanism w.r.t. precision and recall metrics over synthetic data. © 2017 IEEE