## 1 Introduction  
With the advent of the era of intelligent everything, various new application scenarios are constantly emerging, such as augmented reality, ultra-high-speed railway, holographic communication, and smart industrial Internet of Things [1]. A large number of heterogeneous terminal devices are connected to the Internet, generating tremendous data at the edge of the network, which needs to be efficiently transmitted and processed [2]. According to the Internet Data Center, by 2025, the annual data generated worldwide will grow to 175 Zettabytes [3]. By leveraging the massive amount of increased data, various advanced services with customized requirements can be provided to enhance the comprehensive analysis capacities of complex application scenarios. For instance, in the scenario of ultra-high-speed railway, the obtained audio and video data are used to effectively ensure the operation safety of ultra-high-speed railway vehicles through artificial intelligence (AI) algorithms. In the scenario of emergency response, the remote sensing data are used to improve rescue efficiency through AI algorithms. These services have the common characteristics of being compute-intensive and time-sensitive. Therefore, how to promote efficient computing is particularly critical to support plenty of advanced services in the intelligent everything scenarios. New computing paradigms have been proposed to achieve the above target. Driven by powerful computing capabilities, cloud computing with large storage capacity and high data throughput has become the main platform for the application of AI algorithms [4]. However, this \"in-network computing\" paradigm has problems such as extended decision transmission delay and increased network burden when interacting with terminal devices. By sinking computing and storage resources from the cloud to edge of the network, edge computing paradigm and fog computing paradigm have arisen, reducing task transmission delay and greatly promoting the development of AI technology [5; 6]. With limited computing resources, the compute-intensive tasks are offloaded to multiple edge nodes to collaboratively utilize decentralized computing resources, leading to data privacy issues. To this end, the distributed computing [7], such as federated learning, has attracted widespread attention from industry and academia. As advanced services tend to be customized, diversified, and intelligent, a single computing paradigm is difficult to meet the increasingly complex quality of service (QoS) and quality of experience (QoE) demands. Therefore, it is crucial to build a unified computing platform that integrates multiple computing paradigms, in which massive concurrent computing tasks can be globally orchestrated more efficiency to guarantee advanced service requirements. Orchestrating massive concurrent computing services on a unified computing platform faces the following challenges. First, orchestrating computing tasks requires dynamic scheduling of multi-dimensional heterogeneous resources from the perspectives of communication and computing. Moreover, considering the upper-layer diversity advanced services and underlying differentiated networks, the computing power and network in the existing networks are relatively independent. In addition, the unified computing platform needs to manage massive concurrent computing tasks generated by various advanced services such as live image analytics of remote sensing and large-scale AI model training, which makes it more complicated to flexibly schedule multi-dimensional heterogeneous resources to handle massive concurrent computing services. Therefore, it is necessary to design a unified computing platform to support customized services by coordinating the scheduling of multi-dimensional heterogeneous resources from the perspective of both communication and computation. Then, diversified computing services are giving rise to masses of QoS and QoE requirements, such as deterministic delay, model accuracy, clarity level, privacy protection, and so forth [8].