In multi-tiered fog computing systems, to accelerate the processing of computation-intensive tasks for real-time IoT applications, resource-limited IoT devices can offload part of their workloads to nearby fog nodes, whereafter such workloads may be offloaded to upper-tier fog nodes with greater computation capacities. Such hierarchical offloading, though promising to shorten processing latencies, may also induce excessive power consumptions and latencies for wireless transmissions. With the temporal variation of various system dynamics, such a tradeoff makes it rather challenging to conduct effective and online offloading decision making. Meanwhile, the fundamental benefits of predictive offloading to fog computing systems still remain unexplored. In this paper, we focus on the problem of dynamic offloading and resource allocation with traffic prediction in multitiered fog computing systems. By formulating the problem as a stochastic network optimization problem, we aim to minimize the time-average power consumptions with stability guarantee for all queues in the system. We exploit unique problem structures and propose PORA, an efficient and distributed predictive offloading and resource allocation scheme for multi-tiered fog computing systems. Our theoretical analysis and simulation results show that PORA incurs near-optimal power consumptions with queue stability guarantee. Furthermore, PORA requires only mild-value of predictive information to achieve a notable latency reduction, even with prediction errors.

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Abstract-In software-defined networking (SDN), as data plane scale expands, scalability and reliability of the control plane have become major concerns. To mitigate such concerns, two kinds of solutions have been proposed separately. One is multicontroller architecture, i.e., a logically centralized control plane with physically distributed controllers. The other is control devolution, i.e., delegating control of some flows back to switches. Most of existing solutions adopt either static switch-controller association or static devolution, which may not adapt well to the traffic variation, leading to high communication costs between switches and controller, and high computation costs of switches. In this paper, we propose a novel scheme to jointly consider both solutions, i.e., we dynamically associate switches with controllers and dynamically devolve control of flows to switches. Our scheme is an efficient online algorithm that does not need the statistics of traffic flows. By adjusting a parameter, we can make a tradeoff between costs and queue backlogs. Theoretical analysis and extensive simulations show that our scheme yields much lower costs or latency compared to other schemes, as well as balanced loads among controllers.In the last decade, cloud computing has emerged as the most influential computing paradigm to enable on-demand service hosting and delivery. Despite its importance, efficient resource allocation and network management in data centers are still main challenges to cloud providers.Previous works have proposed a variety of solutions to related problems, such as ensemble routing [15], energy budgeting [6], workflow scheduling [10], virtual slice provisioning [14], VM placement [20], etc. Meanwhile, softwaredefined networking (SDN) provides an alternative perspective to manage the whole network. The key idea of SDN is to decouple the control plane from the data plane [12]. In such a way, data plane can focus on performing basic functionalities such as packet forwarding at high speed, while the logically centralized control plane manages the whole network. Usually, switches send requests to the control plane for processing some flow events, e.g., flow install events.The control plane is a potential bottleneck of SDN in terms of scalability and reliability. As the data plane expands, control plane may not be able to process the increasing number of requests if implemented with a single controller, resulting unacceptable latency to flow setup. Reliability is also an issue since a single controller is a single point of failure, which may result in the break-down of the control plane and the entire network.Existing proposals to address such problems fall broadly into two categories. One is to implement the control plane as a distributed system with multiple controllers [7] For switch-controller association, the first category of solution, the usual design choice is to make a static switchcontroller association [7] [17]. However, such static association may result in overloading of controllers and inc...

In fog computing systems, one key challenge is online task scheduling, i.e., to decide the resource allocation for tasks that are continuously generated from end devices. The design is challenging because of various uncertainties manifested in fog computing systems; e.g., tasks' resource demands remain unknown before their actual arrivals. Recent works have applied deep reinforcement learning (DRL) techniques to conduct online task scheduling and improve various objectives. However, they overlook the multi-resource fairness for different tasks, which is key to achieving fair resource sharing among tasks but in general non-trivial to achieve. Thus it is still an open problem to design an online task scheduling scheme with multi-resource fairness. In this paper, we address the above challenges. Particularly, by leveraging DRL techniques and adopting the idea of dominant resource fairness (DRF), we propose FairTS, an online task scheduling scheme that learns directly from experience to effectively shorten average task slowdown while ensuring multi-resource fairness among tasks. Simulation results show that FairTS outperforms state-of-the-art schemes with an ultra-low task slowdown and better resource fairness.

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