Comparison of scheduling schemes for on-demand IaaS requests

Highlights? We investigate the interaction aspects between on-demand requests and the allocation of virtual machines in a server farm operated by a specific infrastructure owner. ? We compare scheduling algorithms from the aspect of the average energy consumption and heat emission of servers as well as the blocking probabilities of on-demand requests. ? A saving on the energy consumption is possible in the operational range (where on-demand requests do not face unpleasant blocking) with the allocation of virtual machines to physical servers based on the priority. Infrastructure-as-a-service (IaaS) is one of emerging powerful cloud computing services provided by IT industry at present. This paper considers the interaction aspects between on-demand requests and the allocation of virtual machines in a server farm operated by a specific infrastructure owner. We formulate an analytic performance model of the server farm taking into account the quality of service (QoS) guaranteed to users and the operational energy consumption in the server farm. We compare several scheduling algorithms from the aspect of the average energy consumption and heat emission of servers as well as the blocking probabilities of on-demand requests. Based on numerical results of a comparison of different allocation strategies, a saving on the energy consumption is possible in the operational range (where on-demand requests do not face unpleasant blocking probability) with the allocation of virtual machines to physical servers based on the priority.

[1]  Marta Kwiatkowska,et al.  Advances and challenges of probabilistic model checking , 2010, 2010 48th Annual Allerton Conference on Communication, Control, and Computing (Allerton).

[2]  Masahiro Fujita,et al.  Multi-Terminal Binary Decision Diagrams: An Efficient Data Structure for Matrix Representation , 1997, Formal Methods Syst. Des..

[3]  Tien Van Do,et al.  Solution for a retrial queueing problem in cellular networks with the Fractional Guard Channel policy , 2011, Math. Comput. Model..

[4]  Erol Gelenbe,et al.  Energy-Efficient Cloud Computing , 2010, Comput. J..

[5]  Borja Sotomayor,et al.  Virtual Infrastructure Management in Private and Hybrid Clouds , 2009, IEEE Internet Computing.

[6]  Matthias Werner,et al.  Event-driven processor power management , 2010, e-Energy.

[7]  G. F. Newell,et al.  Introduction to the Theory of Queues. , 1963 .

[8]  Marios D. Dikaiakos,et al.  Cloud Computing: Distributed Internet Computing for IT and Scientific Research , 2009, IEEE Internet Computing.

[9]  Mariusz Glabowski,et al.  Modeling of systems with overflow multi-rate traffic , 2007, The Third Advanced International Conference on Telecommunications (AICT'07).

[10]  Mariusz Glabowski,et al.  Modeling of Systems with Overflow Multi-Rate Traffic , 2007, The Third Advanced International Conference on Telecommunications (AICT'07).

[11]  Alex Delis,et al.  VM Placement in non-Homogeneous IaaS-Clouds , 2011, ICSOC.

[12]  C. Michael Olsen,et al.  Multi-processor Computer System Having Low Power Consumption , 2002, PACS.

[13]  Brian J. Watson,et al.  Autonomic Virtual Machine Placement in the Data Center , 2008 .

[14]  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.

[15]  Marta Z. Kwiatkowska,et al.  Probabilistic symbolic model checking with PRISM: a hybrid approach , 2004, International Journal on Software Tools for Technology Transfer.

[16]  Michele Mazzucco,et al.  Reserved or On-Demand Instances? A Revenue Maximization Model for Cloud Providers , 2011, 2011 IEEE 4th International Conference on Cloud Computing.

[17]  Karthick Rajamani,et al.  Energy Management for Commercial Servers , 2003, Computer.

[18]  Ben H. H. Juurlink,et al.  Trade-Offs Between Voltage Scaling and Processor Shutdown for Low-Energy Embedded Multiprocessors , 2007, SAMOS.

[19]  Dmytro Dyachuk,et al.  Balancing electricity bill and performance in server farms with setup costs , 2012, Future Gener. Comput. Syst..

[20]  Jean-Marc Menaud,et al.  Autonomic virtual resource management for service hosting platforms , 2009, 2009 ICSE Workshop on Software Engineering Challenges of Cloud Computing.

[21]  Adam Kaliszan,et al.  Modeling product-form state-dependent systems with BPP traffic , 2010, Perform. Evaluation.

[22]  Qian Huang,et al.  Performance Modeling for Heterogeneous Wireless Networks with Multiservice Overflow Traffic , 2009, GLOBECOM 2009 - 2009 IEEE Global Telecommunications Conference.

[23]  Helen D. Karatza,et al.  Evaluation of gang scheduling performance and cost in a cloud computing system , 2010, The Journal of Supercomputing.

[24]  Calton Pu,et al.  Improving Performance and Availability of Services Hosted on IaaS Clouds with Structural Constraint-Aware Virtual Machine Placement , 2011, 2011 IEEE International Conference on Services Computing.

[25]  M. Siegle,et al.  Multi Terminal Binary Decision Diagrams to Represent and Analyse Continuous Time Markov Chains , 1999 .

[26]  Holger Hermanns,et al.  Symbolic partition refinement with automatic balancing of time and space , 2010, Perform. Evaluation.

[27]  Tien Van Do.  Comparison of Allocation Schemes for Virtual Machines in Energy-Aware Server Farms , 2011, Comput. J..

[28]  Gernot Heiser,et al.  Dynamic voltage and frequency scaling: the laws of diminishing returns , 2010 .