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In the edge computing environment, Internet of Things (IoT) application service providers can rent resources from edge servers to cache their service items such as datasets and code libraries, and thus significantly reducing the service request latency and the core network traffic. Since IoT service providers need to pay for the rented edge computing resources, it is essential to find a dynamical service caching strategy to minimize the service cost while optimizing the performance objective such as service latency reduction. However, most of the existing studies either overlooked the problem of collaborative service caching or failed to consider the system's long-term service cost and latency. In this paper, to address such a problem, we coordinate multiple edge servers to cache service items and formulate the collaborative service caching problem using a multi-agent multi-armed bandit model. Furthermore, we propose a utility-aware collaborative service caching (UACSC) scheme based on a multi-agent reinforcement learning. The UACSC scheme can coordinate multiple edge servers to make a dynamic joint caching decision, aiming at maximizing the system's long-term utility. To evaluate the performance of our proposed scheme, we implement four representative baseline algorithms and compare them with six different performance metrics. In addition, a real-world case study is also presented to demonstrate the effectiveness of the UACSC scheme. Comprehensive experimental results show that the UACSC scheme can effectively coordinate multiple edge servers to cache service items, and achieve higher service latency reduction and lower service cost compared with other baseline algorithms. • A new problem formulation for the dynamic collaborative service caching at the edge. • Aim to maximize the long-term system utility defined by the service cost and latency reduction. • A new multi-agent reinforcement learning based utility-aware service caching scheme. [ABSTRACT FROM AUTHOR]

Mobile devices (MDs) and applications are receiving extensive popularity and attracting significant attention. Mobile applications, especially for artificial intelligence (AI) applications, require powerful computation-intensive resources. Hence, running all the AI applications on a single MD introduces high energy consumption and application delay, as it has limited battery capacity and computation resources. Fortunately, the emerging edge-cloud computing (ECC) architecture pushes the computation resource to both the network edge and remote cloud to cope with challenging AI applications. Although the advantage of ECC greatly benefits various mobile applications, data security remains an important open issue in this scenario, which has not been well studied. This paper focuses on the profit maximization (PM) problem for security-aware task offloading in an ECC environment, i.e., considering the tasks from MDs with different service demands, edge nodes should decide them to be processed on the edge node or the remote cloud with a security guarantee. Specifically, we first construct the security model to measure the time overhead for each task under various scenarios. We then formulate the PM problem by jointly considering the security demand and deadline constraints of tasks. Finally, we propose a genetic algorithm-based PM (GA-PM) algorithm, the coding strategy of which considers the task execution location and execution order. Moreover, the crossover and mutation operations are implemented based on the coding strategy. Extensive simulation experiments with various parameters varying demonstrate that our GA-PM can achieve better performance than all the comparison algorithms. • The security model is built to measure the execution time of tasks under different parameters. • A genetic algorithm (GA)-based PM algorithm is proposed to implement task offloading and obtain optimal profit. • A coding strategy is devised by considering the tasks execution location and execution order. • Extensive simulation results demonstrate that GA-PM algorithm achieves the optimal profit. [ABSTRACT FROM AUTHOR]