Recommendation research has evolved from solely improving accuracy to addressing ethical and fairness concerns. While prior works focus on optimizing fairness from either the user or product perspective, recent research emphasizes the importance of multi-sided fairness. This issue is inherently challenging due to the competing goals of different stakeholders. To tackle this challenge, we propose a Consistent and Personalized Fairness Recommendation framework with Multi-Objective Integer Programming (CPFR-MOIP). Our framework introduces two key innovations. First, we develop a novel similarity-based individual fairness metric for the user side and formulate a consistent product-side fairness metric, ensuring that the generated recommendation list aligns with the user preference distribution and the expected product exposure distribution. Second, we incorporate users' variety-seeking levels as a moderating factor to adjust fairness trade-offs and introduce personalized weights to balance user-side and product-side fairness. To effectively solve this optimization problem, we devise an alternating algorithm with theoretical guarantee and demonstrate the Pareto optimality of the obtained solutions. Extensive experiments on two real-world datasets demonstrate that our CPFR-MOIP achieves superior multi-sided fairness while maintaining competitive recommendation accuracy. Furthermore, ablation analysis highlights the advantages of incorporating user variety-seeking levels for personalizing fairness trade-offs. Our work paves the way for more ethical and personalized recommendation systems. The implementation code is available at: https://github.com/P0ise-Wang/CPFR-MOIP.

With the benefit of virtualization technology and an ascending trend towards more cloud applications, virtual cluster (VC) allocation concerning different requirements of application deployment is vital to cloud datacenters and edge networks. Most of the state-of-the-art approaches for VC allocation problems are probability-based heuristics, and none of them absolutely outperforms any other approach in any case. Thus, it is necessary to collect and select effective and efficient approaches to jointly solve diverse and customizable VC allocation problems. This paper presents VCA-Solver, a VC allocation solver that can draw on collectively feasible schemes from multiple effective approaches. We describe the architecture of VCA-Solver and present a preliminary evaluation with a prototype implementation.

Mobile edge networks enhance cloud applications by offering reduced delay for mobile terminals. However, due to the cumulative increase of mobile terminals and applications, edge networks fail to accommodate all applications and are difficult to guarantee the end-to-end delay demand, which is an important metric of QoS. One potential solution to address this challenge is to place virtual clusters (VCs) in mobile micro-clouds (MMCs) to enhance the capabilities of edge networks and the cloud. Therefore, in this paper, we build the optimization model of VC placement in edges for maximizing the benefit function, with constraints of 1). the satisfaction for end-to-end delay demand of each application; 2). the resource capacity of each MMC no more than the whole demands of those VCs placed in. We use Stochastic Network Calculus (SNC) to build the delay model more accurately, considering stochastic arrivals of flows and stochastic services of service nodes. Moreover, we use Martingale Theory to enhance the solvability of the SNC-based delay model, achieving tighter delay bound reliability.

In this paper, we propose a system framework of Pub/Sub-based multimodal data transmission to mobile terminals. For various sensors, we classify the data in different topics, but transmit them in the uniform data channel, with different transmission mechanisms. In this case, mobile terminals receive data via different mechanisms, the push notification and request-response connection. The proposed framework makes the data transmission more efficient and flexible.