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An Edge-Cloud Continuum integrates edge and cloud resources to provide a flexible and scalable infrastructure. This paradigm can minimize latency by processing data closer to the source at the edge while leveraging the vast computational…
We consider a hierarchical edge-cloud architecture in which services are provided to mobile users as chains of virtual network functions. Each service has specific computation requirements and target delay performance, which require placing…
Driven by great demands on low-latency services of the edge devices (EDs), mobile edge computing (MEC) has been proposed to enable the computing capacities at the edge of the radio access network. However, conventional MEC servers suffer…
The emerging computing continuum paves the way for exploiting multiple computing devices, ranging from the edge to the cloud, to implement the control algorithm. Different computing units over the continuum are characterized by different…
We propose integrating the edge-computing paradigm into the multi-robot collaborative scheduling to maximize resource utilization for complex collaborative tasks, which many robots must perform together. Examples include collaborative…
Heterogeneous systems are present from powerful supercomputers, to mobile devices, including desktop computers, thanks to their excellent performance and energy consumption. The ubiquity of these architectures in both desktop systems and…
Edge computing operates between the cloud and end users and strives to provide low-latency computing services for simultaneous users. Redundant use of multiple edge nodes can reduce latency, as edge systems often operate in uncertain…
Problem Definition: Allocating sufficient capacity to cloud services is a challenging task, especially when demand is time-varying, heterogeneous, contains batches, and requires multiple types of resources for processing. In this setting,…
We present a framework for performance optimization in serverless edge-cloud platforms using dynamic task placement. We focus on applications for smart edge devices, for example, smart cameras or speakers, that need to perform processing…
The demand for distributed applications has significantly increased over the past decade, with improvements in machine learning techniques fueling this growth. These applications predominantly utilize Cloud data centers for high-performance…
We present a number of novel algorithms, based on mathematical optimization formulations, in order to solve a homogeneous multiprocessor scheduling problem, while minimizing the total energy consumption. In particular, for a system with a…
Hierarchical edge-cloud computing-aided Internet of Things (IoT) networks offer low-latency and cost-efficient services to a growing number of data-intensive IoT devices. However, optimizing service placement, which involves determining the…
The demand for stringent interactive quality-of-service has intensified in both mobile edge computing (MEC) and cloud systems, driven by the imperative to improve user experiences. As a result, the processing of computation-intensive tasks…
Real-time applications in the next generation networks often rely upon offloading the computational task to a \textit{nearby} server to achieve ultra-low latency. Augmented reality applications for instance have strict latency requirements…
With the proliferation of the Internet of Things (IoT) and the wide penetration of wireless networks, the surging demand for data communications and computing calls for the emerging edge computing paradigm. By moving the services and…
Motivated by applications such as on-device collaborative neural network inference, this work investigates edge-facilitated collaborative fog computing - in which edge-devices collaborate with each other and with the edge of the network to…
With the widespread use of Internet of Things (IoT) devices and the arrival of the 5G era, edge computing has become an attractive paradigm to serve end-users and provide better QoS. Many efforts have been done to provision some merging…
The high computational, memory, and energy demands of Deep Learning (DL) applications often exceed the capabilities of battery-powered edge devices, creating difficulties in meeting task deadlines and accuracy requirements. Unlike previous…
The edge-cloud system has the potential to combine the advantages of heterogeneous devices and truly realize ubiquitous computing. However, for service providers to guarantee the Service-Level-Agreement (SLA) priorities, the complex…
The proliferation of IoT devices in smart cities challenges 6G networks with conflicting energy-latency requirements across heterogeneous slices. Existing approaches struggle with the energy-latency trade-off, particularly for massive scale…