The development of machine learning has increasingly extended to edge mobile devices like Unmanned Aerial Vehicles (UAVs). It leads that the security of grouped Unmanned Aerial Vehicles (UAVs) data collection in harsh environment is also concerned. Deploying a storage system in the UAVs, called mobile grouped storage clusters, can effectively manage data while ensuring data reliability and security. Compared with replication storage systems, erasure-coded storage systems reduce storage overhead, but have high repair cost. Partial decoding repair method is an effective strategy to minimize cross-group repair traffic for erasure-coded storage systems. However, existing methods are not suitable for the mobile cluster with varying bandwidths, which can not minimize repair time. We propose FLARepair, a fast location-aware repair strategy, based on partial decoding and machine learning prediction technology, to minimize the repair time and cross-group repair traffic. It determines the reconstruction sets adaptively to minimize the cross-group repair traffic according to the location of surviving nodes. It also dynamically repairs each failed strip and finds the optimal middle partial decoding nodes of each failed strip to minimize repair time. FLARepair has minimal repair time compared to 2 exiting methods (CAR and ClusterSR) and the basic method (NonPD) via dynamic numerical and static local cluster simulations.

Mobile distributed erasure coded Internet of Things (IoT) clusters store popular data, reducing communication latency, and ensuring data reliability while requiring low storage overhead. However, it suffers a high repair overhead to ensure data reliability and availability due to mobile device failures or leaving the cluster. Predictive repair is an effective strategy for reducing repair overhead that has gained attention with the development in accurate failure and mobile node movement trajectory prediction technologies in recent years. We propose LFPR, a hybrid lazy fast predictive repair strategy that combines two baseline predictive repair approaches (reconstruction and migration), including LFPRH and LFPRC for a hot and cold data distributed cluster, respectively. LFPRC and LFPRH adopt different mechanisms to determine whether a block should perform predictive repair immediately. The predictive repair mechanisms of LFPR couples migration and reconstruction in parallel to reduce average repair time per block. LFPR significantly reduces average repair time per block via large-scale simulation and local cluster experiments, compared with existing predictive repair solutions, such as FastPR and the two baselines.

Erasure coding has gained popularity in today's storage systems as a low-storage overhead and high-reliability fault-tolerant method. However, it is hampered by the high repair costs. The temporary failures in storage systems amplify this drawback resulting in a lot of unnecessary repair traffic. It leads to a dilemma that traditional repair schemes can not optimize repair traffic and reliability at the same time.

Federated Learning (FL) enables the multiple participating devices to collaboratively contribute to a global neural network model while keeping the training data locally. Unlike the centralized training setting, the non-IID, imbalanced (statistical heterogeneity) and distribution shifted training data of FL is distributed in the federated network, which will increase the divergences between the local models and the global model, further degrading performance. In this paper, we propose a flexible clustered federated learning (CFL) framework named FlexCFL, in which we 1) group the training of clients based on the similarities between the clients’ optimization directions for lower training divergence; 2) implement an efficient newcomer device cold start mechanism for framework scalability and practicality; 3) flexibly migrate clients to meet the challenge of client-level data distribution shift. FlexCFL can achieve improvements by dividing joint optimization into groups of sub-optimization and can strike a balance between accuracy and communication efficiency in the distribution shift environment. The convergence and complexity are analyzed to demonstrate the efficiency of FlexCFL. We also evaluate FlexCFL on several open datasets and made comparisons with related CFL frameworks. The results show that FlexCFL can significantly improve absolute test accuracy by$+10.6\%$on FEMNIST compared withFedAvg,$+3.5\%$on FashionMNIST compared withFedProx,$+8.4\%$on MNIST compared withFeSEM,$+4.7\%$on Sentiment140 compare withIFCA. The experiment results show that FlexCFL is also communication efficient in the distribution shift environment.

Modern NVMe SSDs have been widely deployed in multi-tenant cloud computing environments or multi-programming systems. When multiple applications concurrently access one SSD hardware, unfairness within the shared SSD will slow down the application significantly and lead to a violation of service level objectives. However, traditional data cache management within SSDs mainly focuses on improving cache hit ratio, which causes data cache contention and sacrifices fairness among multiple applications. In this paper, we propose a DRAM-based Over-Provisioning (OP) cache management mechanism, named Justitia, to reduce data cache contention and improve fairness for modern SSDs. Justitia consists of two stages includingStatic-OPstage andDynamic-OPstage. Through the novel OP mechanism in the two stages, Justitia reduces the max slowdown by$4.5\times$on average. At the same time, Justitia increases fairness by$20.6\times$and buffer hit ratio by$19.6\%$averagely, compared with the traditional shared mechanism.

Energy harvesting technology has been popularly adopted in embedded systems. However, unstable energy source results in unsteady operation. In this paper, we devise a long-term energy efficient task scheduling targeting for solar-powered sensor nodes. The proposed method exploits a reinforcement learning with a solar energy prediction method to maximize the energy efficiency, which finally enhances the long-term quality of services (QoS) of the sensor nodes. Experimental results show that the proposed scheduling improves the energy efficiency by 6.0%, on average and achieves the better QoS level by 54.0%, compared with a state-of-the-art task scheduling algorithm.