Plug4Green: A flexible energy-aware VM manager to fit data centre particularities

To maintain an energy footprint as low as possible, data centres manage their VMs according to conventional and established rules. Each data centre is however made unique due to its hardware and workload specificities. This prevents the ad hoc design of current VM managers from taking these particularities into account to provide additional energy savings. In this paper, we present Plug4Green, an energy-aware VM placement algorithm that can be easily specialized and extended to fit the specificities of the data centres. Plug4Green computes the placement of the VMs and state of the servers depending on a large number of constraints, extracted automatically from SLAs. The flexibility of Plug4Green is achieved by allowing the constraints to be formulated independently from each other but also from the power models. This flexibility is validated through the implementation of 23 SLA constraints and 2 objectives aiming at reducing either the power consumption or the greenhouse gas emissions. On a heterogeneous test bed, Plug4Green specialization to fit the hardware and the workload specificities allowed to reduce the energy consumption and the gas emission by up to 33% and 34%, respectively. Finally, simulations showed that Plug4Green is capable of computing an improved placement for 7500 VMs running on 1500 servers within a minute.

[1]  Arun Venkataramani,et al.  Black-box and Gray-box Strategies for Virtual Machine Migration , 2007, NSDI.

[2]  Rajkumar Buyya,et al.  Cost of Virtual Machine Live Migration in Clouds: A Performance Evaluation , 2009, CloudCom.

[3]  Jeffrey S. Chase,et al.  Making Scheduling "Cool": Temperature-Aware Workload Placement in Data Centers , 2005, USENIX Annual Technical Conference, General Track.

[4]  Giovanni Giuliani,et al.  Cloud computing and its interest in saving energy: the use case of a private cloud , 2012, Journal of Cloud Computing: Advances, Systems and Applications.

[5]  Giovanni Giuliani,et al.  A methodology to predict the power consumption of servers in data centres , 2011, e-Energy.

[6]  Jacob Feldman,et al.  An Integrated Business Rules and Constraints Approach to Data Centre Capacity Management , 2010, CP.

[7]  Alex Delis,et al.  Hint-Based Execution of Workloads in Clouds with Nefeli , 2013, IEEE Transactions on Parallel and Distributed Systems.

[8]  Dang Minh Quan,et al.  Energy Efficient Resource Allocation Strategy for Cloud Data Centres , 2011, ISCIS.

[9]  Akshat Verma,et al.  pMapper: Power and Migration Cost Aware Application Placement in Virtualized Systems , 2008, Middleware.

[10]  Dave Cliff,et al.  A financial brokerage model for cloud computing , 2011, Journal of Cloud Computing: Advances, Systems and Applications.

[11]  Christoforos E. Kozyrakis,et al.  A Comparison of High-Level Full-System Power Models , 2008, HotPower.

[12]  Xavier Lorca,et al.  Bin Repacking Scheduling in Virtualized Datacenters , 2011, CP.

[13]  Jeffrey S. Chase,et al.  Balance of power: dynamic thermal management for Internet data centers , 2005, IEEE Internet Computing.

[14]  Hermann de Meer,et al.  Evaluating and modeling power consumption of multi-core processors , 2012, 2012 Third International Conference on Future Systems: Where Energy, Computing and Communication Meet (e-Energy).

[15]  Andrzej Kochut,et al.  Dynamic Placement of Virtual Machines for Managing SLA Violations , 2007, 2007 10th IFIP/IEEE International Symposium on Integrated Network Management.

[16]  Hermann de Meer,et al.  Modelling and analysing the power consumption of idle servers , 2012, 2012 Sustainable Internet and ICT for Sustainability (SustainIT).

[17]  Toby Walsh,et al.  Handbook of Constraint Programming , 2006, Handbook of Constraint Programming.

[18]  Jing Xu,et al.  Multi-Objective Virtual Machine Placement in Virtualized Data Center Environments , 2010, 2010 IEEE/ACM Int'l Conference on Green Computing and Communications & Int'l Conference on Cyber, Physical and Social Computing.

[19]  Adrian Paschke,et al.  A Categorization Scheme for SLA Metrics , 2006, Service Oriented Electronic Commerce.

[20]  Christos Kozyrakis,et al.  Full-System Power Analysis and Modeling for Server Environments , 2006 .

[21]  Xavier Lorca,et al.  Entropy: a consolidation manager for clusters , 2009, VEE '09.

[22]  Thomas Schulze,et al.  An energy aware framework for virtual machine placement in cloud federated data centres , 2012, 2012 Third International Conference on Future Systems: Where Energy, Computing and Communication Meet (e-Energy).

[23]  P ? ? ? ? ? ? ? % ? ? ? ? , 1991 .

[24]  Rajkumar Buyya,et al.  Energy-aware resource allocation heuristics for efficient management of data centers for Cloud computing , 2012, Future Gener. Comput. Syst..

[25]  Paul Shaw,et al.  A Constraint for Bin Packing , 2004, CP.

[26]  Ricardo Bianchini,et al.  Energy conservation in heterogeneous server clusters , 2005, PPoPP.

[27]  Elliot K. Kolodner,et al.  Guaranteeing High Availability Goals for Virtual Machine Placement , 2011, 2011 31st International Conference on Distributed Computing Systems.

[28]  Fabien Hermenier,et al.  BtrPlace: A Flexible Consolidation Manager for Highly Available Applications , 2013, IEEE Transactions on Dependable and Secure Computing.

[29]  Wolf-Dietrich Weber,et al.  Power provisioning for a warehouse-sized computer , 2007, ISCA '07.