This survey reviews the evolution of data-driven AI-augmented technologies and their impact on computing systems. Prior work focuses on improving existing systems using AI across a wide range of domains, such as efficient resource provisioning, application deployment, task placement, and service management. The frontiers of these computing technologies have been boosted by shift from manually encoded algorithms to Artificial Intelligence (AI)-driven autonomous systems for optimum and reliable management of distributed computing resources. In recent years, the landscape of computing paradigms has witnessed a gradual yet remarkable shift from monolithic computing to distributed and decentralized paradigms such as Internet of Things (IoT), Edge, Fog, Cloud, and Serverless. This allows for improvement in energy consumption, response time and service level agreement violations by up to 74, 63 and 82 percent, respectively. Specifically, the proposed method gives higher F1 scores for fault-detection than the best deep learning (DL) method, while consuming lower memory than the heuristic methods. Extensive experiments with real-world edge computing benchmarks on multiple Raspberry-Pi based federated edge configurations show that DRAGON can outperform the baseline methods in fault-detection and Quality of Service (QoS) metrics. Leveraging the low memory footprint of GONs, we propose a decentralized fault-tolerance method called DRAGON that runs simulations (as per a digital modeling twin) to quickly predict and optimize the performance of the edge federation. Unlike GANs, GONs use a single network to both discriminate input and generate samples, significantly reducing their memory footprint. To address this challenge, we propose a novel memory-efficient deep learning based model, namely generative optimization networks (GON). A key challenge in such systems is the deployment of latency-critical and AI based resource-intensive applications in constrained devices. Experiments conducted using real-world data on fog applications using the GOBI and GOBI* methods, show a significant improvement in terms of energy consumption, response time, Service Level Agreement and scheduling time by up to 15, 40, 4, and 82 percent respectively when compared to the state-of-the-art algorithms.Įdge Federation is a new computing paradigm that seamlessly interconnects the resources of multiple edge service providers. Co-simulation and the back-propagation approaches allow these methods to adapt quickly in volatile environments. Using this, we create a hybrid simulation driven decision approach, GOBI*, to optimize Quality of Service (QoS) parameters. Further, we leverage the accuracy of predictive models and simulation capabilities by developing a Coupled Simulation and Container Orchestration Framework (COSCO). To achieve this, we propose a Gradient Based Optimization Strategy using Back-propagation of gradients with respect to Input (GOBI). Therefore, there is a need for scheduling policies that are both reactive to work efficiently in volatile environments and have low scheduling overheads. The former often fail to quickly adapt in highly dynamic environments, whereas the latter have run-times that are slow enough to negatively impact response time. Container orchestration platforms have emerged to alleviate this problem with prior art either using heuristics to quickly reach scheduling decisions or AI driven methods like reinforcement learning and evolutionary approaches to adapt to dynamic scenarios. Intelligent task placement and management of tasks in large-scale fog platforms is challenging due to the highly volatile nature of modern workload applications and sensitive user requirements of low energy consumption and response time.
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