Related papers: Collaborative Resource Management and Workloads Sc…
With the development of mobile edge computing (MEC) and blockchain-based federated learning (BCFL), a number of studies suggest deploying BCFL on edge servers. In this case, resource-limited edge servers need to serve both mobile devices…
In the edge-cloud continuum, datacenters provide microservices (MSs) to mobile users, with each MS having specific latency constraints and computational requirements. Deploying such a variety of MSs matching their requirements with the…
The energy sustainability of multi-access edge computing (MEC) platforms is here addressed by developing Energy-Aware job Scheduling at the Edge (EASE), a computing resource scheduler for edge servers co-powered by renewable energy…
Efficient resource allocation and scheduling algorithms are essential for various distributed applications, ranging from wireless networks and cloud computing platforms to autonomous multi-agent systems and swarm robotic networks. However,…
The exponential growth of Internet of Things (IoT) devices has intensified the demand for efficient and responsive services. To address this demand, fog and edge computing have emerged as distributed paradigms that bring computational…
In Mobile Edge Computing (MEC), Internet of Things (IoT) devices offload computationally-intensive tasks to edge nodes, where they are executed within containers, reducing the reliance on centralized cloud infrastructure. Frequent upgrades…
Contemporary Distributed Computing Systems (DCS) such as Cloud Data Centres are large scale, complex, heterogeneous, and distributed across multiple networks and geographical boundaries. On the other hand, the Internet of Things…
In this thesis, we study the downlink multiuser scheduling and power allocation problem for systems with simultaneous wireless information and power transfer (SWIPT). In the first part of the thesis, we focus on multiuser scheduling. We…
Edge computing has become critical for enabling latency-sensitive applications, especially when paired with cloud resources to form cloud-assisted edge clusters. However, efficient resource management remains challenging due to edge nodes'…
Internet of Things (IoT) is leading to the pervasive availability of streaming data about the physical world, coupled with edge computing infrastructure deployed as part of smart cities and 5G rollout. These constrained, less reliable but…
The emerging edge-hub-cloud paradigm has enabled the development of innovative latency-critical cyber-physical applications in the edge-cloud continuum. However, this paradigm poses multiple challenges due to the heterogeneity of the…
Low latency communication is one of the fundamental requirements for 5G wireless networks and beyond. In this paper, a novel approach for joint caching, user scheduling and resource allocation is proposed for minimizing the queuing latency…
Artificial intelligence is one of the important technologies for industrial applications, but it requires a lot of computing resources and sensor data to support it. With the development of edge computing and the Internet of Things,…
With the emergence of more and more applications of Internet-of-Things (IoT) mobile devices (IMDs), a contradiction between mobile energy demand and limited battery capacity becomes increasingly prominent. In addition, in ultra-dense IoT…
Mobile edge computing (MEC) is a new paradigm that provides cloud computing services at the edge of networks. To achieve better performance with limited computing resources, peer offloading between cooperative edge servers (e.g. MEC-…
To enhance resource utilization and address interference issues in ultra-dense networks with mobile edge computing (MEC), a resource utilization approach is first introduced, which integrates orthogonal frequency division multiple access…
In this paper, we consider resource allocation for edge computing in internet of things (IoT) networks. Specifically, each end device is considered as an agent, which makes its decisions on whether offloading the computation tasks to the…
Lightweight containers provide an efficient approach for deploying computation-intensive applications in network edge. The layered storage structure of container images can further reduce the deployment cost and container startup time.…
Edge computing is promoted to meet increasing performance needs of data-driven services using computational and storage resources close to the end devices, at the edge of the current network. To achieve higher performance in this new…
By offloading intensive computation tasks to the edge cloud located at the cellular base stations, mobile-edge computation offloading (MECO) has been regarded as a promising means to accomplish the ambitious millisecond-scale end-to-end…