Mobile Edge Computing (MEC) pushes computing functionalities away from the centralized cloud to the proximity of data sources, thereby reducing service provision latency and saving backhaul network bandwidth. Although computation offloading for MEC systems has been extensively studied in the literature, service placement is an equally, if not more, important design topic of MEC, yet receives much less attention. Service placement refers to configuring the service platform and storing the related libraries/databases at the edge server, e.g., MEC-enabled Base Station (BS), which enables corresponding computation tasks to be executed. Due to the limited computing resource, the edge server can host only a small number of services and hence which services to host has to be judiciously decided to maximize the system performance. In this paper, we investigate collaborative service placement in MEC-enabled dense small cell networks. An efficient decentralized algorithm, called CSP (Collaborative Service Placement), is proposed where a network of small cell BSs optimize service placement decisions collaboratively to address a number of challenges in MEC systems, including service heterogeneity, spatial demand coupling, and decentralized coordination. CSP is developed based on parallel Gibbs sampling by exploiting the graph coloring on the small cell network. The algorithm significantly improves the time efficiency compared to conventional Gibbs sampling, yet guarantees provable convergence and optimality. CSP is further extended to work with selfish BSs, where BSs are allowed to choose “to cooperate” or “not to cooperate.” We employ coalitional game to investigate the strategic behaviors of selfish BSs and design a coalition formation scheme to form stable BS coalitions using merge-and-split rules. Simulations results show that CSP can effectively reduce edge system operational cost for both cooperative and selfish BSs.

中文翻译：

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密集小型蜂窝网络中边缘计算的协同服务部署

移动边缘计算 (MEC) 将计算功能从集中式云推到数据源附近，从而减少服务提供延迟并节省回程网络带宽。尽管 MEC 系统的计算卸载已经在文献中进行了广泛的研究，但服务放置是 MEC 的一个同样重要的设计主题，但很少受到关注。服务放置是指配置服务平台并将相关的库/数据库存储在边缘服务器，例如支持MEC的基站（BS），从而能够执行相应的计算任务。由于计算资源有限，边缘服务器只能托管少量服务，因此必须明智地决定托管哪些服务以最大化系统性能。在本文中，我们研究了启用 MEC 的密集小型蜂窝网络中的协作服务放置。提出了一种称为 CSP（协作服务放置）的高效分散算法，其中小小区 BS 网络协同优化服务放置决策，以解决 MEC 系统中的许多挑战，包括服务异构、空间需求耦合和分散协调。CSP 是基于并行吉布斯采样开发的，利用了小蜂窝网络上的图着色。与传统的 Gibbs 采样相比，该算法显着提高了时间效率，同时保证了可证明的收敛性和最优性。CSP 进一步扩展到与自私的 BS 合作，其中允许 BS 选择“合作”或“不合作”。” 我们采用联盟博弈来研究自私 BS 的战略行为，并设计联盟形成方案以使用合并和拆分规则形成稳定的 BS 联盟。仿真结果表明，CSP 可以有效降低合作基站和自私基站的边缘系统运营成本。