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Graph Neural Networks (GNNs) have demonstrated remarkable capabilities across various domains. Despite the successes of GNN deployment, their utilization often reflects societal biases, which critically hinder their adoption in high-stake decision-making scenarios such as online clinical diagnosis, financial crediting, etc. Numerous efforts have been made to develop fair GNNs but they typically concentrate on either individual or group fairness, overlooking the intricate interplay between the two, resulting in the enhancement of one, usually at the cost of the other. In addition, existing individual fairness approaches using a ranking perspective fail to identify discrimination in the ranking. This paper introduces two innovative notions dealing with individual graph fairness and group-aware individual graph fairness, aiming to more accurately measure individual and group biases. Our Group Equality Individual Fairness (GEIF) framework is designed to achieve individual fairness while equalizing the level of individual fairness among subgroups. Preliminary experiments on several real-world graph datasets demonstrate that GEIF outperforms state-of-the-art methods by a significant margin in terms of individual fairness, group fairness, and utility performance. 

Existing tiered memory systems all use DRAM-Preferred as their al- location policy whereby pages get allocated from higher-performing DRAM until it is filled after which all future allocations are made from lower-performing persistent memory (PM). The novel insight of this work is that the right page allocation policy for a workload can help to lower the access latencies for the newly allocated pages. We design, implement, and evaluate three page allocation policies within the real system deployment of the state-of-the-art dynamic tiering system. We observe that the right page allocation policy can improve the performance of a tiered memory system by as much as 17x for certain workloads. 

Storage is the Achilles heel of hybrid cloud deployments of workloads. Accessing persistent state over a WAN link, even a dedicated one, delivers an over-whelming performance blow to application performance. We propose FAB, a new storage architecture for the hybrid cloud. FAB addresses two major challenges for hybrid cloud storage, performance efficiency and backup efficiency. It does so by creating a new FAB layer in the storage stack that enables fault-tolerance, performance acceleration, and backup for FAB storage volumes. A preliminary evaluation of FAB's performance acceleration mechanism when deployed over Ceph's distributed block storage system offers encouragement to pursue this new hybrid cloud storage architecture.