Resource allocation methods for Fog computing systems

Abstract

Fog computing is gaining popularity as a suitable computer paradigm for the Internet of things (IoT). It is a virtualised platform that sits between IoT devices and centralised cloud computing. Fog computing has several characteristics, including proximity to IoT devices, low latency, geo-distribution, a large number of fog nodes, and real-time interaction. A key challenge in fog is resource allocation because existing resource allocation methods for cloud computing cannot directly apply to fog computing. Hence, many resource allocation methods for fog computing have been proposed since the birth of fog computing. However, most of these methods are centralised and not truthful, which means that users are not incentivised always to provide the true information of their tasks and their efficiency could decrease significantly if some users are strategic. Hence, an efficient resource allocation mechanism for this computing paradigm, which can be used in a strategic environment, is in need. Furthermore, a decentralised resource allocation algorithm is needed when there is no central control in the fog computing system. To this purpose, we consider three challenges: (1) near-optimal resource allocation in a fog system; (2) incentivising self-interested IoT users to truthfully report their tasks; and (3) decentralised resource allocation in a fog system. In this thesis, we examine relevant literature and describe its achievements and shortcomings. Currently, many resource allocation mechanisms using various techniques are proposed for resource allocation in cloud computing and fog computing. However, there is little work that studies truthful fog computing resource allocation mechanisms. Furthermore, reinforcement learning is also widely used in resource allocation for fog computing. However, most of these studies focus on single-agent reinforcement learning and centralised resource allocation. In summary, they only address a subset of the challenges in our fog computing resource allocation problem, and their application scenarios are highly limited. Therefore, we introduce our resource allocation model, i.e., Resource Allocation in Fog Computing (RAFC) and Distributed Resource Allocation in Fog Computing (DRAFC) in detail and choose the benchmark mechanisms to evaluate our proposed resource allocation mechanisms. Then, we develop and test an efficient and truthful mechanism called Flexible Online Greedy (FlexOG) using simulations. The simulations demonstrate that our mechanism can reach a higher level of social welfare than the truthful benchmark mechanisms by up to 10% and that it often achieves about 90% of the theoretical upper bound. To make FlexOG more scalable, we propose a modification of FlexOG called Semi-FlexOG, which is shown to use less processing time. Furthermore, to allocate resources in a decentralised fog system, we propose Decentralised Auction with PPO (DAPPO), which uses online reverse auctions and decentralised reinforcement learning for allocating tasks to resources in the fog. By enabling competition between resource providers, these auctions ensure that the most suitable provider is chosen for a given task, but without the computational and communication overheads of a centralised solution. In order to derive effective bidding strategies for nodes, we use a Proximal Policy Optimisation (PPO) reinforcement learning algorithm that takes into account the status of a node and task characteristics and that aims to maximise the node’s long-term revenue. Hence, DAPPO deals naturally with highly dynamic systems, where the pattern of tasks could change dramatically. The results of our simulations show that DAPPO achieves a good performance in terms of social welfare. Specifically, its performance is close to the upper bound (around 90%) and better than benchmarks (0% to 30%). Finally, we conclude and outline possible future work.

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ORCID for Sebastian Stein: orcid.org/0000-0003-2858-8857

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