Multi-access Edge Cloud (MEC) networks are powerful for providing emerging computation-intensive and latency-sensitive applications with low latency leveraging ubiquitous edge devices. These networks enable complex applications to be split into multiple components/subtasks and deployed among multiple edge servers with limited computation and communication resources. However, multiple subtasks within an application are dependent on each other. They cannot be executed in parallel, resulting in non-trivial resource waste when allocating resources to every subtask throughout the lifetime of the application. This paper investigates the multi-component task offloading problem in MEC networks that addresses the dependencies among components and three-dimensional (3D) resource allocation, i.e., computation, communication, and time slots. The problem is NP-hard and challenging to solve due to the complex task dependencies, including triangular dependencies among multiple subtasks and the routing of edges between dependent subtasks. To address the challenge, we first propose a non-destructive task reconfiguration algorithm that transforms a task call graph into multiple sequential layers, breaking out the triangular dependency. Then, we develop a dePendency-awaRe task offloAding algorithm wIth taSk rEconfiguration (PRAISE) algorithm to maximize the total offloading benefit.PRAISEdecouples the original problem into task offloading and 3D convex resource optimization. Simulation results show thatPRAISEoutperforms baselines with higher system benefits and lower resource costs.

Cloud Computing (CC) is powerful for the computation offloading of services, promoting the implementation of various modern applications. Mobile Edge Computing (MEC) can provide low-latency services utilizing edge servers locating in proximity to users. The combination of MEC and CC can give play to the dual advantages of both. However, it is a challenging problem to offload service requests to the collaborative edge-cloud networks aiming at minimizing costs due to the resource limitation of edge servers and the online feature of services. To address this issue, we mathematically model the service requests with multiple inter-connected functions. Then, the problem of computation offloading of multi-function service requests in collaborative edge-cloud networks is formulated to be an Integer Linear Programming (ILP) and is proved to be NP-hard. Furthermore, a Cost-minimized Computation Offloading with Reconfiguration (CCOR) algorithm is proposed to minimize the total cost of online services. Finally, simulation results show that the proposed CCOR algorithm can effectively reduce the cost of computation offloading with higher resource utilization of edge cloud compared with baseline algorithms.

Nowadays, owing to the advantages of high bandwidth and low power consumption, the wired optical network-on-chip (W-ONoC) has emerged as a high-performance on-chip communication solution. However, the W-ONoC suffers from increased latency and limited scalability since the multi-hop data transmission is frequently performed in wired structures. In addition, several problems such as photoelectric conversion and thermal sensitivity pose challenges to the design of WONoCs. In this paper, we develop a novel on-chip communication architecture based on free-space optics (FSO), and leverage a suite of emerging devices. The proposed architecture eliminates the power loss caused by the waveguide and microring resonator previously deployed in W-ONoCs, and the transmission latency is also reduced through simplifying the packet switching. Moreover, compared with the traditional single-layer FSO NoC, our 3D FSO NoC further decreases the number of consumed lasers and power. Extensive simulation results demonstrate the aforementioned advantages of our solution.