Search

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 one of the powerful engines driving global industrial upgrading and the booming digital economy. However, the explosive growth of cloud data centers (DCs) has resulted in inevitable energy consumption and carbon emission problems. Therefore, constructing energy-efficient and sustainable DCs will be essential for green cloud computing. This review makes several efforts to thoroughly investigate and track the research progress and routes to sustainable DCs. Firstly, we construct a new conceptual model of sustainable DCs to cover cutting-edge research results and indicate future evolutionary directions. Secondly, this review provides a comprehensive survey of five topics from a technical perspective: workload management, virtual resource management, energy management, thermal management, and waste heat recovery. Subsequently, some real-world datasets relevant to the topics, including workload traces, renewable energy data, and electricity price traces, have been specifically collected to support researchers in conducting further research. Finally, based on observations of existing works, we highlight some salient technical challenges and promising solutions to provide sensible energy and carbon reduction suggestions in sustainable DCs. • This review presents research advances and routes for sustainable data centers. • A new conceptual model of data centers is constructed to cover cutting-edge topics. • This review categorizes and analyzes five hot topics from a technical perspective. • Some real-world datasets are collected to support related research. [ABSTRACT FROM AUTHOR]

With the rapid development of the digital economy and intelligent industry, the energy consumption of data centers (DCs) has increased significantly. Various optimization methods are proposed to improve the energy efficiency of servers in DCs. However, existing solutions usually adopt model-based heuristics and best practices to select operations, which are not universally applicable. Moreover, existing works primarily focus on the optimization methods for individual components, with a lack of work on the joint optimization of multiple components. Therefore, we propose a multi-agent reinforcement learning-based method, named MRDF, combining DVFS and dynamic fan control to achieve a trade-off between power consumption and performance while satisfying thermal constraints. MRDF is model-free and learns by continuously interacting with the real server without prior knowledge. To enhance the stability of MRDF in dynamic environments, we design a data-driven baseline comparison method to evaluate the actual contribution of a single agent to the global reward. In addition, an improved Q-learning is proposed to deal with the large state and action space of the multi-core server. We implement MRDF on a Huawei Taishan 200 server and verify the effectiveness by running benchmarks. Experimental results show that the proposed method improves energy efficiency by an average of 3.9% compared to the best baseline solution, while flexibly adapting to different thermal constraints. • We propose a multi-agent reinforcement learning-based method to optimize server efficiency. • A data-driven baseline comparison method is designed to improve the stability of online learning. • An improved Q-learning algorithm is proposed to address the vast state and action space. [ABSTRACT FROM AUTHOR]