Offers in cloud computing & network


The Synchromedia Lab (http://www.synchromedia.ca) at the École de Technologie Supérieure (Université du Québec)  is seeking for talent MSc and PhD candidates with a specialty in various fields of cloud computing, data analysis, data center networking, software-defined network, environment, etc.

In addition to the projects listed below, we are particularly interested in candidates with skills in networking, cloud computing and data analysis in general. Please send us an email with your profile and research purpose. Applications shall include a CV, transcripts, and a cover letter. Selected applicants will be contacted for a screening view.

Contact: knguyen@synchromedia.ca

 

  

Environmental-aware traffic engineering in software-defined infrastructure

Today, network operators are increasingly managing their pools of network elements and long-haul connections with the help of WAN controllers, like OSCARS, OpenDRAC, Argia, and v-WAN. With an increase in traffic demand and heterogeneity of network architecture, these WAN controllers are facing challenges of interoperation & analytics. Therefore, analytical models adding to such inter-datacenter tools are required to help carriers planning ahead how to deploy their on-demand circuit without excessive over-provisioning.

This project will be dedicated to virtual topology optimization and energy-aware routing. Based on prior work in the GreenStar Network project, the candidate will build an optimization model for a set of nodes and links in network networks, and devise optimal routing schemes for each user request. He/she will then infer contextual relationships between resource requirements and power consumption and proposes heuristic algorithms to dynamically detect patterns which will be used to optimize paths in the virtual network. Software-defined networking technique will be used to implement the proposed solution, both in inter- data network and a living lab environment.

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Software-defined networking for IoT services in smart community

The introduction of massively new Internet of Things (IoT) applications, in particular those with low-latency requirements, has resulted in localvore demand. Edge computing is an emerging approach dealing with such increasing demand. It enables computing tasks to be performed at the last-mile close to end users, instead of a remote cloud. To fully achieve edge computing, traditional Central Office (CO) may be transformed into a small-scale data center which is responsible for both network provisioning and computing tasks. Such computing paradigm offers many features necessary to enable M2M services in the future embedded Internet. Home networks are also rapidly developing to include a large diversity of devices, including mobile phones, personal computers, laptops, TVs, speakers, lights, and electronic appliances.

This project will be investigating software-defined solutions for converging optical and wireless access networks in smart community network, supporting the development of next-generation IoT applications. The candidate will evaluate of existing architecture, and then design a scalable, robust and reliable software-defined home and community network. He/she will propose approaches and develop algorithm to virtualize network, and provide QoS for IoT services based on bandwidth sharing and optimization, as well as to define a fault-tolerant architecture for virtual network and virtual CPE. The project also involves research on resource discovery, network QoS, resource allocation, and virtual resource grouping and sharing.

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Big data processing and management in smart community

In a smart home and smart community, networked M2M devices generate various traffic patterns, including periodic, event-driven, generated amounts, and multimedia streaming patterns, depending on their applications, which is a valuable source to make decisions on home automation and smart community management. Big data management aims to improve not only the control of communications network, but also to monitor user and system behaviour all the time and store the data, getting several parameters through different devices to always ensure the well-being and the home control. A cloud based framework for statistical data processing is required based on the parallel processing paradigms. This will be leveraged regarding new sources of information from smart homes, as well as energy data management standards recently defined by IETF.

This project will be dedicated to big data modelling and management for smart services in a networked community. The candidate will investigate and develop data collection methods to perform extensive experiments to gather data on consumption, emissions, and behaviours of actors involved in the smart home and cloud. A platform for big data storage and management will be built, as well as patterns and models characterizing real-time operational status of smart home appliances, cloud resources and users over time. He/She will then propose state-of-the-art algorithms for processing query big databases, analyzing behaviors, and optimizing operations on knowledge bases.

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Life-cycle analysis (LCA) and environmental optimization in virtual environment

Internet services have undergone multiple revolutions over the past 50 years, from snail-mail to household ultra-broadband utility. Today, the tremendous demands of the growing human society are pushing the evolution of Internet-based automation technologies, particularly for knowledge dissemination and intensive information processing. Data centres, regarded as the “brain” of the Internet, are required to host increasingly critical applications, thereby consuming a huge amount of energy. This raises new questions, in terms of scalability, resiliency and operational cost-effectiveness, and environmental concerns in particular.

The Canada Research Chair Tier 1 project at the Synchromedia Lab is addressing issues related to a sustainable smart cloud platform, which is a virtual and analytical system capable of deep, complex computation and intelligent behaviours performed in an energy-efficient and eco-friendly way. By means of smart meters, data collectors, and analytical gears that acquire knowledge about the ecosystem and all the actors involved, including end-users, eco-cloud services are able to react immediately and “just in time”, to establish automated control processes.This project is aimed at developing a framework to analyze and optimize environmental emissions of the cloud ecosystem. It includes models and techniques to calculate and/or estimate environmental impacts of virtual resources, components, services, and systems in real-time and long term utilization, at low granularity levels. The candidate is required to apply modeling techniques to represent the virtual environment of cloud in a complete, accurate, and understandable fashion. Mathematical and optimization models will then be used to optimize cloud operation process to minimize the overall environmental impacts while meeting quality of service requirements.

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Statistical models for traffic in virtual WAN

Today, network operators are increasingly managing their pools of network elements and long-haul connections with the help of WAN controllers, like OSCARS, OpenDRAC, Argia, and v-WAN. With an increase in traffic demand and heterogeneity of network architecture, these WAN controllers are facing challenges of interoperation & analytics. Therefore, analytical models adding to such inter-datacenter tools are required to help carriers planning ahead how to deploy their on-demand circuit without excessive over-provisioning.

This project will address traffic models for various types of applications in inter- data center network environment, especially with respect to Telco and multimedia applications. These models will be used to develop algorithms and methodologies to optimize resource consumption and environmental impacts of the core network, which is key element of an inter- data center orchestrator.

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Interoperable interfaces for inter-cloud environment

Today's data centres, regarded as the “brain” of the Internet, are required to host increasingly critical applications, thereby consuming a huge amount of energy. This raises new questions, in terms of scalability, resiliency and operational cost-effectiveness, and environmental concerns in particular. The Canada Research Chair Tier 1 project at the Synchromedia Lab is addressing issues related to a sustainable smart cloud platform, which is a virtual and analytical system capable of deep, complex computation and intelligent behaviours performed in an energy-efficient and eco-friendly way.

A key issue when building a cloud-based ecosystem spanning multiple infrastructures is interoperability. Existing cloud computing solutions have not been built with interoperability in mind. They usually lock customers into a single cloud infrastructure, platform or service, preventing the portability of data or software created by them.This project is therefore, defined to build interfaces to import/export operational data between different cloud infrastructures, like different OpenStack domains, or between an OpenStack and Amazon AWS domain. The interfaces will also provide additional control functions to manage virtual resources between different cloud infrastructures.

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Privacy communications for IoT services

Recently, the centralized smart home control have resulted in a new class of applications which require individuals to contribute their private data in order to amass, store, manipulate and analyze information for decision-making purposes. This is enabled thanks to networked smart objects that monitor, and report various types of data, such as energy consumption, temperature, quality of air, etc. On one hand, this fine grained information enables trending, forecasting and fault detection analysis, which leads to a more efficient and robust control and management system; on the other hand, this information reveals important privacy information of human actors.

Recently, a strong notion of privacy, namely, differential privacy has emerged, which provides some privacy guarantees against adversaries with arbitrary side information. Differential privacy aims at limiting the risk enhancement to one’s privacy when he contributes his data to a statistical database by guaranteeing that, even if the sender removes his data from the data set, the released results would not likely become significantly more or less. However, strong privacy guarantees may have negative impacts on application performance.

In the same time, the software-defined networking (SDN) paradigm which separates the control plane from the data plane has emerged as potential solution dealing with the complexity of IoT networks. In particular, the ability of implementing new algorithms to dynamically handle packets in OpenFlow-enabled network elements to achieve specific administrative goals makes it possible to develop new real-time security and privacy mechanisms from in-network perspectives.

This research is aimed at building a new framework for real-time differential privacy from signal processing perspective, and implementing a novel method for providing real-time privacy preserving using SDN technology which flexibly and optimally adds noises to network flows. They are substantial for the development of carrier-grade smart home, Machine-to-Machine (M2M), IoT, and Telco Cloud technologies.

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Software-Defined Data Center Fabrics

This project will build on Software Defined Networking (SDN) technology to allow data center network fabrics to be specified using target independent domain specific programming languages. The project will investigate innovative approaches that enable advances in data-plane programming and network-wide policy specification in next-generation data centers.

Recently, a new concept of Programming Protocol-Independent Packet Processors has emerged in a wide-range of applications to a variety of networking devices. In particular, domain specific languages such as P4 have the ability to transform the networking industry by allowing network operators to deploy new network features across a number for network devices in a multi-vendor environment. There is still a wide gap between the concept of a generalized language for packet processing and the ability to deploy arbitrary programs across programmable packet processing targets such as general purpose CPUS, Network Processors (NPUs), FPGAs and programmable ASICs. This project will develop new models and algorithms to address a next-generation programmable forwarding plane of network elements, based on P4 programming language, with respect to new virtualized and physical network functions.

In addition, while the ability to deliver SDN is rooted in the ability to program individual devices, network configuration remains a complex process. This project will therefore develop new techniques that optimize individual device function based on overall network configuration. In reality, Network Function Virtualization (NFV) enables feature deployment agility by moving the data plane from hardware appliances to virtual machines. Virtualization techniques enable server consolidation and the ability to elastically scale capability through the use of commercial off-the shelf hardware (COTS). The outcome of this project will help expand the capability of carrier grade servers to respond to the ever-increasing volume of network traffic.

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Fog-and-cloud service model for IoT

Today, IoT is becoming a global system of connected sensors, actuators, networks, machines and devices. IoT and Cloud integration will enable development of large-scale IoT applications, such as smart cities, energy, health, etc. Moreover, due to requirements such as mobility support, location-awareness and low latency, the cloud has been recently extended to the edge of the network—Fog Computing. Developing large-scale IoT applications using cloud and fog computing resources is challenging because it requires a service abstraction model that matches highly dynamic and heterogeneous resources at different levels of the network hierarchy from IoT devices to fog devices and the cloud. This project is dedicated to to develop knowledge, software service design concepts and mechanisms for scalable and dynamic integration of IoT devices and their services into fog and cloud platforms. It will devise solutions for:

  • Modeling, developing, and integrating IoT services in dynamic Fog-Cloud computing systems
  • Managing and adapting these services with respect to dynamicity of IoT devices and the dynamic availability of fog resources

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Optimized architecture for IoT applications

Applications for the Internet of Things (IoT) are interesting and their design is challenging since they have to fulfil several requirements. These requirements are tightly connected with each other. Of course, applications need to be functionally correct. In addition, they need to use computing resources efficiently, since there are often parts of the application that run on battery-driven sensor nodes. At the same time, applications need to be secure, and for instance protect the privacy of user data. The communication in the network needs to be scalable, and its architecture should be robust. There may also be challenges regarding user-friendliness, because of the constrained user interfaces that these systems often have. Altogether, this requires a robust, modular, optimized, and highly available architecture, and often hinders the creation of such systems in a cost-effective way. Approaches to reduce complexity, such as the provision of frameworks or middleware, as applied to other areas, may not be possible due to the resource constraints or due to the tight dependencies of technologies that also evolve quickly.

The research thus focuses on the overall challenges when developing IoT applications, satisfying several requirements. The work also covers architectures for IoT systems, identifying where specific functionality should be execute and how different architectures influences security and robustness of the system. After a study of the problem domain and ongoing research, the work should identify and advance viable techniques, strategies and architectures that ease application development for IoT systems. This can include formal techniques, model-driven approaches like code generation, and other analytical methods that can assist IoT application developers.

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