Multi-Access Edge Computing (MEC) is a paradigm that enables Internet-of-Things (IoT) applications and devices to run tasks in different locations, from IoT devices to servers in the Cloud. This way, less capable devices can offload computation loads to more powerful or available servers. However, choosing the optimal location for a particular task can be complex due to the features of each location and restrictions of the task. For this, numerous approaches in the literature adopt a centralized strategy for the computation offloading decision, which introduces a single point of failure and can be a bottleneck for resource-demanding applications. In this work, we propose a decentralized Deep Reinforcement Learning (DRL) agent to solve the choice of computing locations, and its assessment in a real testbed. This testbed is formed of an end-user device running the agent, which connects to a MEC server and a Cloud server through 5G. We compare the algorithm against four alternatives, one based on another DRL approach, and analyze their performance in terms of meeting the computing tasks’ requirements and energy consumption in the User Equipment (UE), where synthetically generated tasks are executed or offloaded. DRL algorithms are shown to provide the best tradeoff between performance and energy consumption in changing conditions.

The high performance computing landscape is shifting from collections of homogeneous nodes towards heterogeneous systems, in which nodes consist of a combination of traditional out-of-order execution cores and accelerator devices. Accelerators, built around GPUs, many-core chips, FPGAs or DSPs, are used to offload compute-intensive tasks. The advent of this type of systems has brought about a wide and diverse ecosystem of development platforms, optimization tools and performance analysis frameworks. This is a review of the state-of-the-art in performance tools for heterogeneous computing, focusing on the most popular families of accelerators: GPUs and Intel's Xeon Phi. We describe current heterogeneous systems and the development frameworks and tools that can be used for developing for them. The core of this survey is a review of the performance models and tools, including simulators, proposed in the literature for these platforms.