Online Work Distribution to Clouds

The Cloud supports diverse workloads and simple schemes are needed to allocate jobs with satisfactory QoS and low overhead. This paper presents a further study on the potential of an online work distribution approach in adaptively distributing workloads under variable load conditions for optimizing the two contradictory criteria: reducing the energy consumption per job while maintaining the best possible job response time. For cloud systems spanning multiple geographical regions, this paper describes a smart distributed system which deploys a Task Allocation Platform (TAP) in each cloud and takes decisions to allocate tasks dynamically to the service that offers the best overall Quality of Service. Experiments are conducted in dynamic and heterogeneous environments at the global intercontinental level, both to collect data for decision making and to illustrate the effectiveness of our approach.

[1]  Erol Gelenbe,et al.  Synchronized Interactions in Spiked Neuronal Networks , 2008, Comput. J..

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

[3]  Rajkumar Buyya,et al.  A Particle Swarm Optimization-Based Heuristic for Scheduling Workflow Applications in Cloud Computing Environments , 2010, 2010 24th IEEE International Conference on Advanced Information Networking and Applications.

[4]  Erol Gelenbe,et al.  Random Neural Networks with Multiple Classes of Signals , 1999, Neural Computation.

[5]  Erol Gelenbe.  Stability of the Random Neural Network Model , 1990, EURASIP Workshop.

[6]  Erol Gelenbe,et al.  Autonomous search for mines , 1998, Eur. J. Oper. Res..

[7]  Richard S. Sutton,et al.  Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.

[8]  Erol Gelenbe,et al.  Trade-offs between energy and quality of service , 2012, 2012 Sustainable Internet and ICT for Sustainability (SustainIT).

[9]  V. Paxson.  End-to-end routing behavior in the internet , 2006, CCRV.

[10]  Micah Beck,et al.  An end-to-end approach to globally scalable programmable networking , 2003, FDNA '03.

[11]  Farnam Jahanian,et al.  Impact of path diversity on multi-homed and overlay networks , 2004, International Conference on Dependable Systems and Networks, 2004.

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

[13]  Erol Gelenbe,et al.  Big Data for Autonomic Intercontinental Overlays , 2016, IEEE Journal on Selected Areas in Communications.

[14]  Christina Delimitrou,et al.  QoS-Aware scheduling in heterogeneous datacenters with paragon , 2013, TOCS.

[15]  Salim Hariri,et al.  Performance-Effective and Low-Complexity Task Scheduling for Heterogeneous Computing , 2002, IEEE Trans. Parallel Distributed Syst..

[16]  Erol Gelenbe,et al.  Random Neural Networks with Synchronized Interactions , 2008, Neural Computation.

[17]  Ling Liu,et al.  Cost-Effective Resource Provisioning for MapReduce in a Cloud , 2015, IEEE Transactions on Parallel and Distributed Systems.

[18]  Nick Feamster,et al.  The case for separating routing from routers , 2004, FDNA '04.

[19]  Daniel Grosu,et al.  Combinatorial Auction-Based Dynamic VM Provisioning and Allocation in Clouds , 2011, CloudCom.

[20]  Hari Balakrishnan,et al.  Resilient overlay networks , 2001, SOSP.

[21]  Alexandru Iosup,et al.  Performance Analysis of Cloud Computing Services for Many-Tasks Scientific Computing , 2011, IEEE Transactions on Parallel and Distributed Systems.

[22]  Michael Dahlin,et al.  End-to-end WAN service availability , 2001, TNET.

[23]  G. Finn,et al.  A Virtual Internet Architecture , 2003 .

[24]  Abhijit Bose,et al.  Delayed Internet routing convergence , 2000, SIGCOMM.

[25]  Scott Shenker,et al.  Overcoming the Internet impasse through virtualization , 2005, Computer.

[26]  Nirwan Ansari,et al.  A Genetic Algorithm for Multiprocessor Scheduling , 1994, IEEE Trans. Parallel Distributed Syst..

[27]  Erol Gelenbe,et al.  Emergency response simulation using wireless sensor networks , 2008, Ambi-Sys '08.

[28]  Xiaona Li,et al.  Cost-Aware Cooperative Resource Provisioning for Heterogeneous Workloads in Data Centers , 2013, IEEE Transactions on Computers.

[29]  Farnam Jahanian,et al.  Internet routing instability , 1997, SIGCOMM '97.

[30]  Erol Gelenbe,et al.  Random Neural Networks with Negative and Positive Signals and Product Form Solution , 1989, Neural Computation.

[31]  Daniel Grosu,et al.  Combinatorial Auction-Based Dynamic VM Provisioning and Allocation in Clouds , 2011, 2011 IEEE Third International Conference on Cloud Computing Technology and Science.

[32]  E. Gelenbe.  Search in unknown random environments. , 2010, Physical review. E, Statistical, nonlinear, and soft matter physics.