Huawei collaborates with some Chinese large busi-ness companies to store and process exabytes of nationwide operational data in data lake storage to provide business insights. Specifically, our customers will ask to store and process massive log message data to support their real-time and decision-making applications. Thus, we need computation and storage components in the analytic platform to process and store these data cost-efficiently. To meet these user requirements, we have designed a storage system in data lake, StreamLake, which introduces a novel design to serve log message streaming and batch data processing in distributed storage, with high scalability, efficiency, reliability and low cost. Specifically, we introduce a stream (storage) object as a storage abstraction for message streaming data to achieve the storage-disaggregated architecture with high scalability and reliability. Moreover, we utilize the erasure coding and tiered storage to save the storage cost, and furthermore, the stream object can be automatically converted to a table object such that cost-effective stream and batch data processing can be achieved. For tabular data, we implement the lakehouse functionality to support ACID via the table object, with a metadata acceleration to improve the efficiency of data access between the compute and storage engines. Also, we design a LakeBrain optimizer at the storage side to optimize the query performance and resource utilization under the storage-disaggregated architecture. Finally, we have also deployed StreamLake in China Mobile, the world's largest mobile network operator to serve over 20PB production data, and the results demonstrate improvements of 30% to 4x in terms of query performance and over 37% in terms of cost saving.

The past few years have witnessed a growth in size and computational requirements for training and inference with neural networks. Currently, a common approach to address these requirements is to use a heterogeneous distributed environment with a mixture of hardware devices such as CPUs and GPUs. Importantly, the decision of placing parts of the neural models on devices is often made by human experts based on simple heuristics and intuitions. In this paper, we propose a method which learns to optimize device placement for TensorFlow computational graphs. Key to our method is the use of a sequence-to-sequence model to predict which subsets of operations in a TensorFlow graph should run on which of the available devices. The execution time of the predicted placements is then used as the reward signal to optimize the parameters of the sequence-to-sequence model. Our main result is that on Inception-V3 for ImageNet classification, and on RNN LSTM, for language modeling and neural machine translation, our model finds non-trivial device placements that outperform hand-crafted heuristics and traditional algo-rithmic methods.

As a primary technology for broadband wireless access (BWA), mobile WiMAX, recently has gained tremendous momentum in the industrial and academic sectors. However, due to significant loss of signal strength along the propagation path especially at higher frequency, the transmit power constraint of mobile terminals, and the interference from neighboring cells, the sustainable coverage area for a specific high data rate is often of limited geographical size. FFR (Fractional Frequency Reuse) can be used to improve the coverage and capacity in order to deliver cost-effective wireless services. This paper is the first time to provide a quantitative discussion on the system level simulation performance for the WiMAX FFR.

Bandwidth and energy reservation is a major challenge in a large scale wireless ad-hoc sensor network, such as IEEE802.15.4-based LR-WPAN. To prolong lifetime of the whole network, improve network throughput, and support a simple topology management method in a large scale sensor network, a hierarchical clustering algorithm is proposed in this paper. This algorithm can be used for a network consisting of various capability devices, such as IEEE802.15.4-based LR- WPAN. Through computing and comparing the potential of serving as a cluster-head device (PSCD) in distributed manner, devices with more capability are chosen for cluster heads and PNC (Piconet coordinator). Comparing with other methods, such as LEACH (low energy adaptive clustering hierarchy) (Hernzelman et al., 2000) and direct communication, simulation results show this method saves energy with significantly high throughput.