Towards Energy-Aware Placement of Real-Time Virtual Machines in a Cloud Data Center

Cloud computing is an evolving paradigm which is becoming an adoptable technology for a variety of applications. However, cloud infrastructures must be able to fulfill application requirements before adopting cloud solutions. Cloud infrastructure providers communicate the characteristics of their services to their customers through Service Level Agreements (SLA). In order for a real-time application to be able to use cloud technology, cloud infrastructure providers have to be able to provide timing guarantees in the SLAs. In this paper, we present our ongoing work regarding a cloud solution in which periodic tasks are provided as a service in the Software as a Service (SaS) model. Tasks belonging to a certain application are mapped in a Virtual Machine (VM). We also study the problem of VM placement on a cloud infrastructure. We propose a placement mechanism which minimizes the energy consumption of the data center by consolidating VMs in a minimum number of servers while respecting the timing requirement of virtual machines.

[1]  Insup Lee,et al.  Real-time multi-core virtual machine scheduling in Xen , 2014, 2014 International Conference on Embedded Software (EMSOFT).

[2]  Hennadiy Leontyev,et al.  A hierarchical multiprocessor bandwidth reservation scheme with timing guarantees , 2008, 2008 Euromicro Conference on Real-Time Systems.

[3]  Jian-Jia Chen,et al.  Server resource reservations for computation offloading in real-time embedded systems , 2013, The 11th IEEE Symposium on Embedded Systems for Real-time Multimedia.

[4]  Rajkumar Buyya,et al.  Adaptive threshold-based approach for energy-efficient consolidation of virtual machines in cloud data centers , 2010, MGC '10.

[5]  Nasser Yazdani,et al.  Communication-aware and energy-efficient resource provisioning for real-time cloud services , 2013, The 17th CSI International Symposium on Computer Architecture & Digital Systems (CADS 2013).

[6]  Yue Gao,et al.  An energy and deadline aware resource provisioning, scheduling and optimization framework for cloud systems , 2013, 2013 International Conference on Hardware/Software Codesign and System Synthesis (CODES+ISSS).

[7]  Aman Kansal,et al.  Q-clouds: managing performance interference effects for QoS-aware clouds , 2010, EuroSys '10.

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

[9]  Giuseppe Lipari,et al.  A Framework for Hierarchical Scheduling on Multiprocessors: From Application Requirements to Run-Time Allocation , 2010, 2010 31st IEEE Real-Time Systems Symposium.

[10]  Thomas Nolte,et al.  Towards Energy-Aware Resource Scheduling to Maximize Reliability in Cloud Computing Systems , 2013, 2013 IEEE 10th International Conference on High Performance Computing and Communications & 2013 IEEE International Conference on Embedded and Ubiquitous Computing.

[11]  Tommaso Cucinotta,et al.  Challenges in real-time virtualization and predictable cloud computing , 2014, J. Syst. Archit..

[12]  Eduardo Tovar,et al.  Compositional multiprocessor scheduling: the GMPR interface , 2013, Real-Time Systems.

[13]  Insup Lee,et al.  Periodic resource model for compositional real-time guarantees , 2003, RTSS 2003. 24th IEEE Real-Time Systems Symposium, 2003.

[14]  Cucinotta Tommaso,et al.  Hierarchical Multiprocessor CPU Reservations for the Linux Kernel , 2009 .

[15]  Rizos Sakellariou,et al.  Energy-Constrained Provisioning for Scientific Workflow Ensembles , 2013, 2013 International Conference on Cloud and Green Computing.

[16]  Peter A. Dinda,et al.  VSched: Mixing Batch And Interactive Virtual Machines Using Periodic Real-time Scheduling , 2005, ACM/IEEE SC 2005 Conference (SC'05).

[17]  Laurent Lefèvre,et al.  Demystifying energy consumption in Grids and Clouds , 2010, International Conference on Green Computing.

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

[19]  Tommaso Cucinotta,et al.  Run-time Support for Real-Time Multimedia in the Cloud , 2013, REACTION.

[20]  Insup Lee,et al.  Optimal virtual cluster-based multiprocessor scheduling , 2009, Real-Time Systems.

[21]  José Antonio Lozano,et al.  Towards a Greener Cloud Infrastructure Management using Optimized Placement Policies , 2015, Journal of Grid Computing.

[22]  Rajkumar Buyya,et al.  Power-aware provisioning of Cloud resources for real-time services , 2009, MGC '09.

[23]  Thomas Stützle,et al.  MAX-MIN Ant System , 2000, Future Gener. Comput. Syst..

[24]  Jim McKie,et al.  Osprey: Operating system for predictable clouds , 2012, IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN 2012).