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Cloud task scheduling and resource allocation (TSRA) constitute a core issue in cloud computing. Batch submission is a common user task deployment mode in cloud computing systems. In this mode, it has been a challenge for cloud systems to balance the quality of user service and the revenue of cloud service provider (CSP). To this end, with multi-objective optimization (MOO) of minimizing task latency and energy consumption, we propose a cloud TSRA framework based on deep learning (DL). The system solves the TSRA problems of multiple task queues and virtual machine (VM) clusters by uniting multiple deep neural networks (DNNs) as task scheduler of cloud system. The DNNs are divided into exploration part and exploitation part. At each scheduling time step, the model saves the best outputs of all scheduling policies from each DNN to the experienced sample memory pool (SMP), and periodically selects random training samples from SMP to train each DNN of exploitation part. We designed a united deep learning (UDL) algorithm based on this framework. Experimental results show that the UDL algorithm can effectively solve the MOO problem of TSRA for cloud tasks, and performs better than benchmark algorithms such as heterogeneous distributed deep learning (HDDL) in terms of task scheduling performance. [ABSTRACT FROM AUTHOR]

VMP (Virtual Machine Placement) is a crucial technology for energy consumption optimization of the cloud data center. Existing works mainly focus on virtual machine consolidation to increase resource utilization and reduce computing energy consumption. However, existing studies usually ignore the thermal effect that an intensive workload on IT (Information Technology) equipment can raise energy consumption by cooling systems and generate hotspots. In addition, an excessive number of virtual machine migrations increases migration costs and risks violating the SLA (Service Level Agreement) signed with users. In this paper, we present a comprehensive system model and formulate the problem as a constrained multi-objective optimization. We propose a novel thermal-aware VMP strategy to solve the problem by jointly considering virtual machines' migration cost, energy consumption, and heat recirculation around server racks. Our strategy makes placement decisions using MOPFGA (Multi-objective algorithm based on Pathfinder Algorithm and Genetic Algorithm) that combines classic MOPFA and GA enhanced by OBL (Opposition Based Learning) for fast convergence and avoidance of local optimum. Extensive experiments based on CloudSim using real data center workload data from PlanetLab show that our algorithm overcomes the defects of the MOPFA (multi-objective pathfinder algorithm) and GA (genetic algorithm) and significantly improves the overall efficiency of a data center. Compared with several state-of-the-art algorithms, MOPFGA on average reduces virtual machine migrations by 77.52%, increases CRAC (Computer Room Air Conditioner) supply temperature by 1.24%, and reduces cooling energy consumption by 24.78% and computational energy consumption by 23.62%. [ABSTRACT FROM AUTHOR]

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]

Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. The publisher apologizes for the typesetting mistake. Publisher Correction to: Thermal-aware virtual machine placement based on multi-objective optimization. [Extracted from the article]