The sellar region tumor is a brain tumor that only exists in the brain sellar, which affects the central nervous system. The early diagnosis of the sellar region tumor subtypes helps clinicians better understand the best treatment and recovery of patients. Magnetic resonance imaging (MRI) has proven to be an effective tool for the early detection of sellar region tumors. However, the existing sellar region tumor diagnosis still remains challenging due to the small amount of dataset and data imbalance. To overcome these challenges, we propose a novel self-supervised multi-scale multi-modal graph pool Transformer (MMGPT) network that can enhance the multi-modal fusion of small and imbalanced MRI data of sellar region tumors. MMGPT can strengthen feature interaction between multi-modal images, which makes our model more robust. A contrastive learning equipped auto-encoder (CAE) via self-supervised learning (SSL) is adopted to learn more detailed information between different samples. The proposed CAE transfers the pre-trained knowledge to the downstream tasks. Finally, a hybrid loss is equipped to relieve the performance degradation caused by data imbalance. The experimental results show that the proposed method outperforms state-of-the-art methods and obtains higher accuracy and AUC in the classification of sellar region tumors.

Reliability is a critical metric in the design and development of scale-out data storage clusters. A general multiway replication-based declustering scheme has been widely used in enterprise large-scale storage systems to improve the I/O parallelism. Unfortunately, given an increasing number of node failures, how often a cluster starts losing data when being scaled-out is not well investigated. In this paper, we studied the reliability of multi-way declustering layouts by developing an extended model, more specifically abstracting the Continuous Time Markov chain to an ordinary differentiate equation group, and analyzing their potential parallel recovery possibilities. Our comprehensive simulation results on Mat lab and SHARPE show that the shifted declustering layout outperforms the random declustering layout in a multi-way replication scale-out architecture, in terms of data loss probability and system reliability by up to 63% and 85% respectively. Our study on both 5-year and 10-year system reliability equipped with various recovery bandwidth settings shows that, the shifted declustering layout surpasses the random declustering layout in both cases by consuming up to 5.2% and 11% less recovery bandwidth.

Technology trends are making sophisticated replication-based storage architectures become a standard commercial practice in today's computing. Existing solutions successfully developed optimal and near-optimal parallelism layouts such as declustered parity organizations at small-scale storage architectures. There are very few studies on multiway replication-based storage architectures that are significantly different from parity-based storage architectures. It is difficult to scale up to a large size because current placement-ideal solutions have a limited number of configurations. In this paper, we retrofit the desirable properties of optimal parallelism definitions in parity architectures for replication architectures, and propose a novel placement-ideal data layout-shifted declustering. Shifted declustering layout obtains optimal parallelism in a wide range of configurations, and obtains optimal high performance and load balancing in both fault-free and degraded mode. Our theoretical proofs and comprehensive simulation results show that shifted declustering is superior in performance, load balancing, and reliability to traditional layout schemes such as standard mirroring, chained declustering, group-rotational declustering, and existing parity layout schemes PRIME and RELPR.

Recent years have seen a growing interest in the deployment of sophisticated replication based storage architecture in data-intensive computing. Existing placement-ideal data layout solutions place an emphasis on declustered parity based storage. However, there exist major differences between parity and replication architectures, especially in data layouts. We retrofit the desirable properties of optimal parallelism in parity architectures for replication architectures, and propose a novel placement-ideal data layout ---- shifted declustering for replication based storage. Shifted declustering layout obtains optimal parallelism in a wide range of configurations, and obtains optimal high performance and load balancing in both fault-free and degraded modes. Our theoretical proofs and comprehensive simulation results show that shifted declustering is superiour in performance and load balancing to traditional replication layout schemes such as standard mirroring, chained declustering, group rotational declustering and existing parity layout schemes PRIME and RELPR in reference [4].

Today's storage systems place an imperative demand on energy efficiency. A storage system often places single-rotation- rate disks into standby mode by stopping them from spinning to conserve energy when the workload is not heavy. The major obstacle of this method is a high spin-up cost introduced by passively waking up the standby disk to service requests. In this paper, we propose a redundancy-based hierarchical I/O cache architecture called RIMAC to solve the problem. The idea of RIMAC is to enable data on the standby disk(s) to be recovered by accessing a two-level I/O cache and/or active disks if needed. In parity-based redundant disk arrays, RIMAC exploits parity redundancy to dynamically XOR-reconstruct the data being accessed toward standby disk(s) at both the cache and disk levels. By avoiding passive spin-ups, RIMAC can significantly improve both energy efficiency and performance. In RIMAC, we developed 1) two power-aware read request transformation schemes called Transformable Read in Cache (TRC) and Transformable Read on Disk (TRD), 2) a power-aware write request transformation policy for parity update, and 3) a second-chance parity cache replacement algorithm to favor the request transformation rate. We evaluated RIMAC by augmenting a validated storage system simulator, DiskSim, and tested three real-life server traces, including HP's cello99, UlUC's OLTP, and SPC's search engine. Comprehensive results indicate that RIMAC is able to reduce energy consumption by up to 18 percent and simultaneously improve the average response time by up to 34 percent compared with threshold-based power management schemes for single-rotation-rate disk- based RAIDs.

Recently, high-energy consumption has become a serious concern for both storage servers and data centers. Recent research studies have utilized the short transition times of multispeed disks to decrease energy consumption. Manufacturing challenges and costs have so far prevented commercial deployment of multispeed disks. In this paper, we propose an energy saving policy, eRAID (energy-efficient RAID), for conventional disk-based mirrored and parity redundant disk array architectures. eRAID saves energy by spinning down partial or the entire mirror disk group with constraints of acceptable performance degradation. We first develop a multiconstraint energy-saving model for the RAID environment by considering both disk characteristics and workload features. Then, we develop a performance (response time and throughput) control scheme for eRAID based on the analytical model. Experimental results show that eRAID can save up to 32 percent energy while satisfying the predefined performance requirement.