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When detecting the underground structure of Mars using an orbital subsurface investigation radar, it is essential to compensate for the ionosphere distortion of the received echo. The existing algorithms, such as the contrast method (CM) and the phase-gradient-autofocus (PGA) algorithm, can mainly compensate for the defocus of the echo, but it is difficult to compensate for the group delay at the same time, resulting in the deviation between the echo position and the terrain profile. This article analyzes the interference of Martian ionosphere to the echo of subsurface investigation radar and proposes a dual-band difference (DBD) ionosphere compensation algorithm. First, the group delay model of radar echo under ionospheric interference is established. Then, the equivalent plasma frequency of the ionosphere is calculated according to the group delay difference of echoes in two bands, and the Taylor coefficients of the phase error are calculated. Finally, the echoes are accurately compensated with the Taylor series model of phase shift. This algorithm can correct the defocus and sidelobe interference and simultaneously compensate for the group delay, which is greatly significant to accurately locate the subsurface structure of Mars. The experiments on the simulation data and the echo of the Mars Advanced Radar for Subsurface and Ionosphere Sounding (MARSIS) show the feasibility of this method. Compared with the traditional way, this algorithm is suitable for both high- and low-frequency situations, and the high computational efficiency without iteration makes on-orbit real-time ionosphere compensation possible. Moreover, with the effective correction of topographic offset, the total electron content (TEC) can also be accurately retrieved. [ABSTRACT FROM AUTHOR]

With the development of big data technologies, green cloud data centers have become a key factor in academia and industry. An energy-efficient cloud data center can save costs for cloud computing users. However, the problem of virtual machine mapping has always been a core problem. In the most existing research, the energy consumption generated by cloud data centers has become an important bottleneck restricting the technology of cloud computing. This paper establishes a consumption model of cloud data center energy and a virtual machine mapping rule. Based on this, a multi-dimensional double descending maximum padding priority (MD3MP2) virtual machine mapping algorithm is proposed. The algorithm can not only solve the one-dimensional virtual machine mapping problem of homogeneous data centers, but also successfully solve the multi-dimensional virtual machine mapping problem of homogeneous data centers. Finally, the algorithm is compared with four other algorithms. The experimental results show that the MD3MP2 algorithm is better than the compared algorithms. [ABSTRACT FROM AUTHOR]

Reducing duplicated data of database backups is an important application scenario for data deduplication technology. NewSQL is an emerging database system and is now being used more and more widely. NewSQL systems need to improve data reliability by periodically backing up in-memory data, resulting in a lot of duplicated data. The traditional deduplication method is not optimized for the NewSQL server system and cannot take full advantage of hardware resources to optimize deduplication performance. A recent research pointed out that the future NewSQL server will have thousands of CPU cores, large DRAM and huge NVRAM. Therefore, how to utilize these hardware resources to optimize the performance of data deduplication is an important issue. To solve this problem, we propose a deduplication optimization method (DOMe) for NewSQL system backup. To take advantage of the large number of CPU cores in the NewSQL server to optimize deduplication performance, DOMe parallelizes the deduplication method based on the fork-join framework. The fingerprint index, which is the key data structure in the deduplication process, is implemented as pure in-memory hash table, which makes full use of the large DRAM in NewSQL system, eliminating the performance bottleneck problem of fingerprint index existing in traditional deduplication method. The H-store is used as a typical NewSQL database system to implement DOMe method. DOMe is experimentally analyzed by two representative backup data. The experimental results show that: 1) DOMe can reduce the duplicated NewSQL backup data. 2) DOMe significantly improves deduplication performance by parallelizing CDC algorithms. In the case of the theoretical speedup ratio of the server is 20.8, the speedup ratio of DOMe can achieve up to 18; 3) DOMe improved the deduplication throughput by 1.5 times through the pure in-memory index optimization method. [ABSTRACT FROM AUTHOR]

To optimize the data migration performance between different supercomputing centers in China, we present TCP-DQN, which is an intelligent TCP congestion control method based on DQN (Deep Q network). The TCP congestion control process is abstracted as a partially observed Markov decision process. In this process, an agent is constructed to interact with the network environment. The agent adjusts the size of the congestion window by observing the characteristics of the network state. The network environment feeds back the reward to the agent, and the agent tries to maximize the expected reward in an episode. We designed a weighted reward function to balance the throughput and delay. Compared with traditional Q-learning, DQN uses double-layer neural networks and experience replay to reduce the oscillation problem that may occur in gradient descent. We implemented the TCP-DQN method and compared it with mainstream congestion control algorithms such as cubic, Highspeed and NewReno. The results show that the throughput of TCP-DQN can reach more than 2 times of the comparison method while the latency is close to the three compared methods. [ABSTRACT FROM AUTHOR]