Research Projects Current
A Framework for Democratized Edge Computing and Intelligence
Edge computing (EC) has been proven to have a significant impact on offloading traffic/computations/Edge Intelligence (EI) tasks from backhaul links and improving users’ Quality of Service (QoS). The vast majority of developed EC/EI models and implemented platforms consist of dedicated infrastructure-based edge nodes (e.g., edge servers, base stations, and smart access points) that are solely controlled by cloud service providers and network operators. To break this monopoly and enable more players to construct and control their own edge cloud, we design and implement a complete democratized edge framework (DEF) that provides seamless non-monopolized EC and EI services to businesses and enterprises.
DEF revolutionarily transforms the already existing yet latent computing resources into on-demand clusters of distributed EC/EI workers. Examples of such resources include user-owned smartphones, laptops, tablets, IoT devices, smart appliances, connected vehicles, as well as computing infrastructures of various public, private, and educational institutions (e.g., servers, workstations, and computer labs during low/non-working hours). We establish innovative multi-objective protocol designs and cross-layer algorithmic procedures for worker recruitment/benchmarking, system/network resource allocation, and task placement/scheduling/migration/replication that collectively enable the deployment of distributed EC and EI jobs, with service guarantees, on large-scale systems of non-proprietary, geographically-dispersed, extremely-heterogeneous and/or resource-constrained wired and wireless workers.
The ground-breaking materialization of this complete framework for large-scale democratized EC and EI will not only reduce monopolies but will also enable: 1) Affordable access to edge computing resources for more users. 2) An open market for businesses, institutions, and even individuals to opt in and monetize their unused/underused computing resources. 3) Easy development and provisioning of EC and EI services by non-tech businesses. 4) Ground-breaking physical and logical procedures that empower decentralized EC and EI, which can play an instrumental role in materializing smart city solutions. Thus, democratizing EC and EI opens an entirely new tech market that is people-owned, democratically governed, and accessible/profitable to all.
Edge computing (EC) has been proven to have a significant impact on offloading traffic/computations/Edge Intelligence (EI) tasks from backhaul links and improving users’ Quality of Service (QoS). The vast majority of developed EC/EI models and implemented platforms consist of dedicated infrastructure-based edge nodes (e.g., edge servers, base stations, and smart access points) that are solely controlled by cloud service providers and network operators. To break this monopoly and enable more players to construct and control their own edge cloud, we design and implement a complete democratized edge framework (DEF) that provides seamless non-monopolized EC and EI services to businesses and enterprises.
DEF revolutionarily transforms the already existing yet latent computing resources into on-demand clusters of distributed EC/EI workers. Examples of such resources include user-owned smartphones, laptops, tablets, IoT devices, smart appliances, connected vehicles, as well as computing infrastructures of various public, private, and educational institutions (e.g., servers, workstations, and computer labs during low/non-working hours). We establish innovative multi-objective protocol designs and cross-layer algorithmic procedures for worker recruitment/benchmarking, system/network resource allocation, and task placement/scheduling/migration/replication that collectively enable the deployment of distributed EC and EI jobs, with service guarantees, on large-scale systems of non-proprietary, geographically-dispersed, extremely-heterogeneous and/or resource-constrained wired and wireless workers.
The ground-breaking materialization of this complete framework for large-scale democratized EC and EI will not only reduce monopolies but will also enable: 1) Affordable access to edge computing resources for more users. 2) An open market for businesses, institutions, and even individuals to opt in and monetize their unused/underused computing resources. 3) Easy development and provisioning of EC and EI services by non-tech businesses. 4) Ground-breaking physical and logical procedures that empower decentralized EC and EI, which can play an instrumental role in materializing smart city solutions. Thus, democratizing EC and EI opens an entirely new tech market that is people-owned, democratically governed, and accessible/profitable to all.
Collaborative Service Provisioning at the Edge in 5G Wireless Networks
5G is the next-generation mobile network. 5G promises to provide the most reliable high-speed communications infrastructure to the growing number of mobile users, including connected devices, machines, and vehicles linked with the Internet of Things (IoT). 5G must also support the demand for streaming video traffic and social network data. Services in 5G networks will need support for high mobility, high data rates, and low response time. In addition, processing an always-increasing volume of data in real-time requires innovative solutions. Since data in 5G networks is mostly generated and delivered at the edge of the network, it is desirable to have solutions that enable data processing as close to the edge as possible. This is known as edge computing. Edge computing has been proven to significantly impact reducing the amount of data going to (or coming from) central cloud services, hence reducing network congestion and improving users’ Quality of Service (QoS). Collaborative (Shared) edge computing services are provided through the use of computing, storage, and communication resources within the user’s device, for example, a smartphone, along with the infrastructure of the service provider.
This research uses edge resources to improve the ability of 5G networks to meet users’ demands and QoS needs. It uses novel predictive and collaborative techniques for service provisioning that adapt to changes in user mobility patterns and traffic demands. Machine learning techniques will be used to make better predictions. The research will be helpful in providing universal wireless broadband access for Canadians and will boost Canada’s position in the telecommunications and mobile service areas.
The training of highly qualified personnel (HQP) involved in this program will include practice in 5G network design, edge computing techniques, and network management. There is a strong demand for these HQPs in the information technology and telecommunications sectors, and their future employment will accelerate the dissemination of next-generation communications technology to the Canadian industry.
