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Erasure codes have been widely considered as a promising solution to enhance data reliability at low storage costs. However, in modern geo-distributed storage systems, erasure codes may incur high data access latency as they require data retrieval from multiple remote storage nodes. This hinders the extensive application of erasure codes to data-intensive applications. This paper proposes novel caching schemes to achieve low latency in distributed coded storage systems. Assuming that future data popularity and network latency information are available, an offline caching scheme is proposed to explore the optimal caching solution for low latency. The proposed scheme categorizes all feasible caching decisions into a set of cache partitions, and then obtains the optimal caching decision through market clearing price for each cache partition. Furthermore, guided by the optimal scheme, an online caching scheme is proposed according to the measured data popularity and network latency information in real time, without the need to completely override the existing caching decisions. Both theoretical analysis and experiment results demonstrate that the online scheme can approximate the offline optimal scheme well with dramatically reduced computation complexity. [ABSTRACT FROM AUTHOR]

Ultra-dense networking (UDN) is a promising technology to improve the network capacity in the next-generation mobile communication system. The virtualization paradigm is tightly integrated into UDN to address the problem of interference management. However, mobility management based on virtual cells meets significant challenges in UDN due to the frequent handovers and massive signaling overhead. These problems become severe for vehicles owing to their high-speed movement. In this paper, driven by trajectory prediction using a real-world vehicle mobility dataset, we propose a proactive mobility management solution based on virtual cells. Four modules are designed in the centralized Software-Defined Networking controller to support the proposed solution. The proposed LSTM-DR framework predicts the next locations of vehicles by integrating Long Short-Term Memory (LSTM) networks and Dead Reckoning (DR) method. The active gNBs selection algorithm selects the serving gNBs to form virtual cells according to predicted locations and mobility preferences. The corresponding signaling procedure is then carefully designed to further reduce the signaling overhead. Simulation results show that the proposed prediction framework can achieve higher accuracy and robustness in trajectory prediction. The proposed proactive solution reduces the handover frequency and handover failure rate and thereby saves the handover signaling overhead significantly. [ABSTRACT FROM AUTHOR]

In geo-distributed cloud storage systems, data replication has been widely used to serve the ever more users around the world for high data reliability and availability. How to optimize the data replica placement has become one of the fundamental problems to reduce the inter-node traffic and the system overhead of accessing associated data items. In the big data era, traditional solutions may face the challenges of long running time and large overheads to handle the increasing scale of data items with time-varying user requests. Therefore, novel offline community discovery and online community adjustment schemes are proposed to solve the replica placement problem in a scalable and adaptive way. The offline scheme can find a replica placement solution based on the average read/write rates for a certain period of time. The scalability can be achieved as 1) the computation complexity is linear to the amount of data items and 2) the data-node communities can evolve in parallel for a distributed replica placement. Furthermore, the online scheme is adaptive to handle the bursty data requests, without the need to completely override the existing replica placement. Driven by real-world data traces, extensive performance evaluations demonstrate the effectiveness of our design to handle large-scale datasets. [ABSTRACT FROM AUTHOR]