The ubiquity of cold starts in serverless architectures poses a critical barrier to low-latency inference. While existing prewarming methods leverage idle container memory to pre-load functions, they often neglect resource contention among functions within the shared container, frequently resulting in severe request blocking. To address these challenges, this paper proposes SFP, a Similarity-Aware Function Pre-Loading strategy which optimizes function distribution by selecting target containers for pre-loading functions. SFP deploys functions with low invocation similarity within the same container while distributing those with high invocation similarity across distinct containers. Function invocation similarity, a metric proposed in this study, is derived from the Jaccard similarity coefficient and quantifies the temporal overlap between disparate function invocations. Experimental results based on real-world workloads demonstrate that, compared to state-of-the-art methods, the proposed strategy improves inference request throughput by up to 230% and achieves memory savings ranging from 6.7% to 34.8%.

In the context of global integration, the theory of financial complex networks has made significant contributions to the establishment of stable financial systems and effective regulatory systems. This survey paper presents a systematic methodology for the multi-layer financial complex networks and their applications. Several typical financial networks in existing research are first summarized: the interbank network, the credit network, the international trade network, and the dealer network. The main methods used in the existing literature to analyze the structure of financial networks include maximum entropy methods, network characterization, community detection, and dynamic multi-layer network. Financial risk contagion as well as spillover effects are the core issues that most of the literature focuses on. Finally, this paper reviews the shortcomings of the existing literature and suggests future research directions in this area.

Large-scale risk events in the venture capital (VC) market can easily lead to systemic risk in the financial market. This paper collects the comprehensive dataset of Chinese VC market from 1999 to 2020, and constructs the multi-layer networks of VC market. After that, a risk contagion model of venture capital market is designed considering both the capital loss transmission and social relations effect (investor herd behavior). By simulating various situations with different risk origins and scales, the speed of risk contagion and the total impacts on the overall stability of the market (network) are compared. We find that the herd behavior of the market will generally aggravate the consequences of risk contagion. Compared with unicorns, the failure of a leading VC firm can cause a wider range of risk contagion. The contagion consequences caused by the failure of random financing companies are more serious than those caused by random VC firms. Moreover, we identify that telecommunications services and information technology are high-risk industries in VC market. Based on the empirical results, this article provides policy implications for the market regulatory sectors to prevent VC market risks.

Venture capital market is one of the most important financial markets, which plays an important role in promoting industrial innovation and economic development. In this study, the complete data set of all venture capital events occurred in China from 2009 to 2020 was used to construct two kinds of complex networks, whose topological property and dynamic evolution trend of the network are studied. Specifically, we construct the co-investment network among investor, as well as the binary complex network of investor-entrepreneur, and propose the evaluation method of the importance and performance of venture capital institutions. Based on the proposed method, we identify some leading venture capital institutions with high performance and importance. Furthermore, this paper enlightens venture capital practitioners by discovering the investment behavior and preference of these leading institutions.

Facing horizontal channel competition in a hierarchical distribution system, independent intermediaries such as wholesalers and retailers are keen to find the optimal location and pricing strategies that enable them to adapt to the increasingly competitive business environment. To help market intermediaries to address their challenges, we propose in this paper a spatial agent-based model (SAM), grounded in complex adaptive systems, which comprises four types of agents, namely the world, the manufacturer, firms, and consumers. We derive the firms' optimal behaviors in response to competition by evaluating the evolutionary location and pricing strategies using a genetic algorithm. We observe that a pyramid structure and the bullwhip effect in demand emerge from the evolutionary behavior of the SAM. We also find that buyers' searching ability enhanced by information technology has a significant effect on the degree of competition in a hierarchical distribution system. In addition, we find that firms that distribute elastic goods are likely to lower their prices to attract more buyers and move closer to their suppliers to save transport costs. In the case that the product demand is inelastic, intermediaries will move as close to their buyers as possible because they can maximize their profits in the SAM.