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Task scheduling is a necessary prerequisite for performance optimization and resource management in the cloud computing system. Focusing on accurate scaled cloud computing environment and efficient task scheduling under resource constraints problems, we introduce fine-grained cloud computing system model and optimization task scheduling scheme in this paper. The system model is comprised of clearly defined separate submodels including task schedule submodel, task execute submodel and task transmission submodel, so that they can be accurately analyzed in the order of processing of user requests. Moreover the submodels are scalable enough to capture the flexibility of the cloud computing paradigm. By analyzing the submodels, where results are repeated to obtain sufficient accuracy, we design a novel task scheduling scheme based on reinforcement learning and queuing theory to optimize task scheduling under the resource constraints, and the state aggregation technologies is employed to accelerate the learning progress. Our results, on the one hand, demonstrate the efficiency of the task scheduling scheme and, on the other hand, reveal the relationship between the arrival rate, server rate, number of VMs and the number of buffer size. [ABSTRACT FROM AUTHOR]

As one of the core issues for cloud computing, resource management adopts virtualization technology to shield the underlying resource heterogeneity and complexity which makes the massive distributed resources form a unified giant resource pool. It can achieve efficient resource provisioning by using the rational implementing resource management methods and techniques. Therefore, how to manage cloud computing resources effectively becomes a challenging research topic. By analyzing the executing progress of a user job in the cloud computing environment, we proposed a novel resource provisioning scheme based on the reinforcement learning and queuing theory in this study. With the introduction of the concepts of Segmentation Service Level Agreement (SSLA) and Utilization Unit Time Cost (UUTC), we viewed the resource provisioning problem in cloud computing as a sequential decision issue, and then we designed a novel optimization object function and employed reinforcement learning to solve it. Experiment results not only demonstrated the effectiveness of the proposed scheme, but also proved to outperform the common methods of resource utilization rate in terms of SLA collision avoidance and user costs. [ABSTRACT FROM AUTHOR]

Reports on the systematic comparative study of zinc and carbon-doped heterojunction bipolar transistors. Transmission line method (TLM) pattern analysis for zinc and carbon-doped base layers; TLM measurement results for carbon-doped base and zinc-doped base; Small-signal microwave performance of zinc and carbon-doped.

Cloud computing is a computing service provided on demand through the Internet, which can provide sufficient computing resources for applications such as big data and the Internet of Things. Before using cloud computing resources, users need to lease the corresponding virtual machines in the data centers, and then submit the tasks to the leased virtual machines for execution. In these two stages, how to choose the optimal data center for resource deployment and task submission is particularly important for cloud users. To tackle the problem that it is difficult to select optimal data center to lease and use virtual machines in distributed data centers, we proposed data center selection algorithms based on deep reinforcement learning. In the stage of virtual machine lease, aiming to minimizing user costs, we considered both the cost of computing resources and the cost of network communication. Then, we used a deep enforcement learning algorithm to obtain the shortest communication path between the users and the data centers,and solve the problem of network communication costs which are difficult to calculate. We achieved an optimal selection of data centers and effectively reduce the overall user cost. In the stage of virtual machine use, to improve quality of service for the users, we use a deep reinforcement learning algorithm to obtain an optimal task scheduling strategy. This effectively solved the problem that user tasks cannot be effectively scheduled due to dynamic changes in the type and size of user tasks and in the state of the virtual machines. Moreover, the proposed scheme reduces the overall task completion time. [ABSTRACT FROM AUTHOR]

Cooling systems provide a safe thermal environment for the reliable operation of IT equipment in data centers (DCs) while generating significant energy consumption. Therefore, to achieve energy savings in cooling system control under dynamic thermal distribution in DCs, this paper proposes a multi-setpoint cooling control approach based on deep reinforcement learning (DRL). Firstly, a thermal model based on the XGBoost algorithm is constructed to precisely evaluate the thermal distribution in the rack room to guide real-time cooling control. Secondly, a multi-set point cooling control approach based on the deep Q-network algorithm (DQN-MSP) is designed to finely regulate the supply air temperature of each air conditioner by capturing the thermal fluctuations to ensure the dynamic balance of cooling supply and demand. Finally, we adopt the extended CloudSimPy simulation tool and the real workload trace of the PlanetLab system to evaluate the effectiveness and performance of the proposed approach. The simulation results show that the proposed control solution effectively reduces the cooling energy consumption by over 2.4% by raising the average air supply temperature of the air conditioner while satisfying the thermal constraints. [ABSTRACT FROM AUTHOR]

As an important raw material for the chemical industry, ethylene is one of the surest indicators that measure the development level of a country. The diene yield is an important production quality index parameter of ethylene units, and it is very important to detect and control them in real time. Due to the limitations of online analytical instrumentation technology, diene yields are difficult to measure online. Motivated by this, this article has studied soft-sensing technology for measuring diene yields. A diene yield prediction method based on a deep belief network algorithm network is proposed, and the regularity of historical diene yield data is fully explored by the method. First, the data feature vectors are fused and normalized. Then, the data are fed into a DBN consisting of two layers of restricted Boltzmann machines for unsupervised training, and finally, a DBN model is used to predict the diene yield. The experimental results show that the mean squared error of the test set with historical data is 1.15%, and the mean absolute percentage error of the measured data is 2.79%. The experimental results are provided to show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Crow search algorithm (CSA), as a new swarm intelligence algorithm that simulates the crows' behaviors of hiding and tracking food in nature, performs well in solving many optimization problems. However, while handling complex and high-dimensional global optimization problems, CSA is apt to fall into evolutionary stagnation and has slow convergence speed, low accuracy, and weak robustness. This is mainly because it only utilizes a single search stage, where position updating relies on random following among individuals or arbitrary flight of individuals. To address these deficiencies, a CSA with multi-stage search integration (MSCSA) is presented. Chaos and multiple opposition-based learning techniques are first introduced to improve original population quality and ergodicity. The free foraging stage based on normal random distribution and Lévy flight is designed to conduct local search for enhancing the solution accuracy. And the following stage using mixed guiding individuals is presented to perform global search for expanding the search space through tracing each other among individuals. Finally, the large-scale migration stage based on the best individual and mixed guiding individuals concentrates on increasing the population diversity and helping the population jump out of local optima by moving the population to a promising area. All of these strategies form multi-level and multi-granularity balances between global exploration and local exploitation throughout the evolution. The proposed MSCSA is compared with a range of other algorithms, including original CSA, three outstanding variants of CSA, two classical meta-heuristics, and six state-of-the-art meta-heuristics covering different categories. The experiments are conducted based on the complex and high-dimensional benchmark functions CEC 2017 and CEC 2010, respectively. The experimental and statistical results demonstrate that MSCSA is competitive for tackling large-scale complicated problems, and is significantly superior to the competitors. [ABSTRACT FROM AUTHOR]

Cloud computing, now a mature computing service, provides an efficient and economical solution for processing big data. As such, it attracts a lot of attention in academia and plays an important role in industrial applications. With the recent increase in the scale of cloud computing data centers, and improvements in user service quality requirements, the structure of the whole cloud system has become more complex, which has also made the resource scheduling management of these systems more challenging. Thus, the goal of this research was to resolve the conflict between cloud service providers (CSPs) who aim to minimize energy costs and those who seek to optimize service quality. Based on the excellent environmental awareness and online adaptive decision-making ability of deep reinforcement learning (DRL), we proposed an online resource scheduling framework based on the Deep Q-network (DQN) algorithm. The framework could make a trade-off of the two optimization objectives of energy consumption and task makespan by adjusting the proportion of the reward of different optimization objectives. Experimental results showed that this framework could effectively be used to make a trade-off of the two optimization objectives of energy consumption and task makespan, and exhibited obvious optimization effects compared with the baseline algorithm. Therefore, our proposed framework can dynamically adjust the optimization objective of the system according to the different requirements of the cloud system. [ABSTRACT FROM AUTHOR]

Aiming at the poor population diversity and serious imbalance between global exploration and local exploitation in the original fruit fly optimization algorithm (FOA), a novel elitist fruit fly optimization algorithm (EFOA) with elite guidance and population diversity maintenance is proposed. EFOA consists of two search phases: an osphresis search with elite and random individual guiding and a vision search with elite and boundary guiding in an iteration. The former contains two sub-stages: exploration with random individual guiding and exploitation with elite individual guiding. Randomly selected individual and flight control parameter constructed by the Sigmoid-based function are first introduced into the algorithm to improve the exploration. The elite guiding strategy with two position-update approaches is designed to augment the local ability of the proposed algorithm. With these stages, EFOA can search some areas of the problem space as much as possible. Finally, elite and boundary information is introduced into EFOA to enhance population diversity. The proposed EFOA is compared with other algorithms, including the original FOA, three outstanding FOA variants, and five state-of-the-art meta-heuristic algorithms. The validation tests are conducted based on the classical benchmark functions and CEC2017 benchmark functions. The Wilcoxon signed rank test and Friedman test are utilized to verify the significance of the results from the perspective of non-parametric statistics. The results demonstrate that the elite guiding strategy and the alternating execution of the three search stages can effectively balance the exploration and exploitation capabilities of the EFOA and enhance its convergence speed. [ABSTRACT FROM AUTHOR]

The multiantenna spectrum sensing (MSS) problem for correlated signal is addressed in this correspondence. First, the zero-lag and non-zero-lag covariance matrices are established, respectively, to capture the correlation feature of primary signals. Then, two scenarios are considered: 1) the noises at different antennas are spatially white; 2) the noises at different antennas are spatially correlated. Afterward, two variants of the covariance detection method are devised to detect the presence of primary user. Also, we derive the approximate closed-form expressions to calculate the decision thresholds of the proposed two methods. Simulation results are presented to demonstrate the superiority of the proposed two methods over the conventional MSS methods. [ABSTRACT FROM AUTHOR]