An Efficient Power Aware Algorithm for Optimizing Energy Consumption of Cloud Resources Using Multi Agent Model

Cloud computing offers different kinds of resources to the user based on the current need. The resource includes hardware, software and platform through virtualization. User requirements are mapped to the corresponding Virtual Machine (VM) by the host of the data center. Every resource in the data center consumed a considerable amount of energy. The existing power aware models are used to minimize the energy consumption but it follows only one specific layer in the cloud. These methods suffer from performance problems due to excess energy by the idle resources. The resources like data center, host and VM consumed energy in case of idle workload and minimum workload. Idle resources are identified and removed from the resource list in order to minimize the excess energy consumption. The resource with the minimum workload is migrated to the other suitable resources using the migration method. The proposed power aware model uses multi agent support for minimizing the energy in Data center, Host and VM level. The overall analysis is carried out based on the performance parameter like Service Level Agreement (SLA) based host shutdown and violation etc. The proposed method consumes minimum energy when compared to other existing models. It achieves maximum performance by considering power aware parameters in the cloud.

[1]  Zhili Wang,et al.  Distributed Edge Computing Offloading Algorithm Based on Deep Reinforcement Learning , 2020, IEEE Access.

[2]  Paulo Carreira,et al.  Energy Cloud: Real-Time Cloud-Native Energy Management System to Monitor and Analyze Energy Consumption in Multiple Industrial Sites , 2014, 2014 IEEE/ACM 7th International Conference on Utility and Cloud Computing.

[3]  Xiaomin Zhu,et al.  Real-Time Tasks Oriented Energy-Aware Scheduling in Virtualized Clouds , 2014, IEEE Transactions on Cloud Computing.

[4]  Subhra Priyadarshini Biswal,et al.  Fuzzy Logic Based Cost and Energy Efficient Load Balancing in Cloud Computing Environment , 2018, 2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS).

[5]  Enda Barrett,et al.  An advanced reinforcement learning approach for energy-aware virtual machine consolidation in cloud data centers , 2017, 2017 12th International Conference for Internet Technology and Secured Transactions (ICITST).

[6]  Mumtaz Karatas,et al.  A UAV location and routing problem with spatio-temporal synchronization constraints solved by ant colony optimization , 2018, J. Heuristics.

[8]  Rajkumar Buyya,et al.  Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in Cloud data centers , 2012, Concurr. Comput. Pract. Exp..

[9]  Zhisheng Niu,et al.  Energy-optimal and delay-bounded computation offloading in mobile edge computing with heterogeneous clouds , 2020, China Communications.

[10]  Lide Duan,et al.  Optimizing Cloud Data Center Energy Efficiency via Dynamic Prediction of CPU Idle Intervals , 2015, 2015 IEEE 8th International Conference on Cloud Computing.

[11]  Rajkumar Buyya,et al.  DVFS-Aware Consolidation for Energy-Efficient Clouds , 2015, 2015 International Conference on Parallel Architecture and Compilation (PACT).

[12]  Khalid Moussaid,et al.  An intelligent scheduling algorithm for energy efficiency in cloud environment based on artificial bee colony , 2017, 2017 3rd International Conference of Cloud Computing Technologies and Applications (CloudTech).

[13]  Mingsheng Shang,et al.  Coordinated Power and Performance-Efficient Virtual Machines Scheduling in the Cloud , 2018, 2018 10th International Conference on Communications, Circuits and Systems (ICCCAS).

[14]  Tarik Taleb,et al.  Energy and Delay Aware Task Assignment Mechanism for UAV-Based IoT Platform , 2019, IEEE Internet of Things Journal.

[15]  Yongqiang Zhang,et al.  Energy-Efficient Dynamic Task Offloading for Energy Harvesting Mobile Cloud Computing , 2018, 2018 IEEE International Conference on Networking, Architecture and Storage (NAS).

[16]  Xuyun Zhang,et al.  EnReal: An Energy-Aware Resource Allocation Method for Scientific Workflow Executions in Cloud Environment , 2016, IEEE Transactions on Cloud Computing.

[17]  Pasi Liljeberg,et al.  Energy-Efficient Virtual Machines Consolidation in Cloud Data Centers Using Reinforcement Learning , 2014, 2014 22nd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing.

[18]  Hiroshi Yamada,et al.  Energy-Price-Driven Request Dispatching for Cloud Data Centers , 2012, 2012 IEEE Fifth International Conference on Cloud Computing.

[19]  Erol Gelenbe,et al.  Adaptive Dispatching of Tasks in the Cloud , 2015, IEEE Transactions on Cloud Computing.

[20]  Yashi Goyal,et al.  Energy efficient hybrid policy in green cloud computing , 2015, 2015 International Conference on Green Computing and Internet of Things (ICGCIoT).

[21]  Helmut Hlavacs,et al.  A Cooperative Multi Agent Learning Approach to Manage Physical Host Nodes for Dynamic Consolidation of Virtual Machines , 2015, 2015 IEEE Fourth Symposium on Network Cloud Computing and Applications (NCCA).

[22]  William Sause.  Coordinated Reinforcement Learning Agents in a Multi-agent Virtual Environment , 2013, 2013 12th International Conference on Machine Learning and Applications.

[23]  Rajkumar Buyya,et al.  Dynamic Voltage and Frequency Scaling‐aware dynamic consolidation of virtual machines for energy efficient cloud data centers , 2017, Concurr. Comput. Pract. Exp..

[24]  Khushdeep Kaur,et al.  Hybrid soft computing approach for energy efficiency in cloud computing , 2016, 2016 International Conference on Communication and Electronics Systems (ICCES).

[25]  Xia Zhang,et al.  Energy aware cloud application management in private cloud data center , 2011, 2011 International Conference on Cloud and Service Computing.