Online Energy-efficient Resource Allocation in Cloud Computing Data Centers

Energy efficiency is a major topic in every scientific field, since being energy efficient means producing more for a smaller cost. Data centers are no exception to this rule as their energy use represents a large portion of the global consumption, and it is needless to say that they ought to perform optimally while being eco-friendly in order to preserve natural resources as much as possible while providing a high quality service for the users. In this paper, we propose an efficient algorithm for allocating users to a pool of servers in an energy-efficient way. Our allocation model emphasizes the critical importance of nondominant resource types such as memory, which usually tend to be wasted by homogeneous allocation approaches. We show that the performance of the algorithm makes it worthy of being used in real-time environments where split-second decisions must be made. We compare our algorithm to the most well-known metaheuristics used in operations research and we show that they do not provide a significant improvement in a reasonable time.

[1]  Bo Cheng,et al.  Availability-Aware and Energy-Efficient Virtual Cluster Allocation Based on Multi-Objective Optimization in Cloud Datacenters , 2020, IEEE Transactions on Network and Service Management.

[2]  Himansu Das,et al.  Energy aware scheduling using genetic algorithm in cloud data centers , 2016, 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT).

[3]  Jaume Salom,et al.  Minimization of Costs and Energy Consumption in a Data Center by a Workload-Based Capacity Management , 2014, E2DC.

[4]  Keqiu Li,et al.  Energy Consumption in Cloud Computing Data Centers , 2014, CloudCom 2014.

[5]  Bibhudatta Sahoo,et al.  Energy-Efficient Service Allocation Techniques in Cloud: A Survey , 2020, IETE Technical Review.

[6]  Hyokyung Bahn,et al.  Tight Evaluation of Real-Time Task Schedulability for Processor’s DVS and Nonvolatile Memory Allocation , 2019, Micromachines.

[7]  Sherali Zeadally,et al.  A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems , 2016, Computing.

[8]  Rolf Stadler,et al.  Gossip-based resource allocation for green computing in large clouds , 2011, 2011 7th International Conference on Network and Service Management.

[9]  Brunilde Sansò,et al.  A Tabu Search Algorithm for the Location of Data Centers and Software Components in Green Cloud Computing Networks , 2013, IEEE Transactions on Cloud Computing.

[10]  P. Ghodous,et al.  Beyond CPU: Considering memory power consumption of software , 2016, 2016 5th International Conference on Smart Cities and Green ICT Systems (SMARTGREENS).

[11]  Kenneth Salem,et al.  An analysis of memory power consumption in database systems , 2017, DaMoN.