Fluid Approximation of Pool Depletion Systems

Today’s most of high performance computing applications use parallel programming paradigms to reach the desired efficiency objectives. In particular, they divide the problem into small elements that can be solved in parallel by as many computing devices as available. Some examples are Apache Spark, the evolution of Hadoop and map-reduce, GPGPU (General Purpose Graphical Processing Units) applications, many-core and multi-core embedded systems. In many cases this type of applications can be modeled by pool depletion systems, i.e. queuing models characterized by a set of parallel servers whose goal is to execute a predetermined number of tasks. Although the modeling paradigm is very simple, it suffers from state space explosion, and can be used to model systems with a limited degree of parallelism only. The main contribution provided by this work consists of presenting a fluid approximation approach capturing the main features of the considered pool depletion systems and solving the above mentioned issues.

[1]  Kevin Skadron,et al.  Scalable parallel programming , 2008, 2008 IEEE Hot Chips 20 Symposium (HCS).

[2]  Mauro Iacono,et al.  Exploiting mean field analysis to model performances of big data architectures , 2014, Future Gener. Comput. Syst..

[3]  Sanjay Ghemawat,et al.  MapReduce: Simplified Data Processing on Large Clusters , 2004, OSDI.

[4]  Guanying Wang,et al.  A simulation approach to evaluating design decisions in MapReduce setups , 2009, 2009 IEEE International Symposium on Modeling, Analysis & Simulation of Computer and Telecommunication Systems.

[5]  Mauro Iacono,et al.  A Performance Modeling Language For Big Data Architectures , 2013, ECMS.

[6]  S. Amari,et al.  Closed-form expressions for distribution of sum of exponential random variables , 1997 .

[7]  Scott Shenker,et al.  Spark: Cluster Computing with Working Sets , 2010, HotCloud.

[8]  Giuseppe Serazzi,et al.  JMT: performance engineering tools for system modeling , 2009, PERV.

[9]  Marco Ajmone Marsan,et al.  Modelling with Generalized Stochastic Petri Nets , 1995, PERV.

[10]  Mauro Iacono,et al.  Performance evaluation of NoSQL big-data applications using multi-formalism models , 2014, Future Gener. Comput. Syst..

[11]  Levente Bodrog Control of queues with MAP servers : experimental results , 2013 .

[12]  Keke Chen,et al.  CRESP: Towards Optimal Resource Provisioning for MapReduce Computing in Public Clouds , 2014, IEEE Transactions on Parallel and Distributed Systems.

[13]  Mauro Iacono,et al.  Modeling Hybrid Systems in SIMTHESys , 2016, PASM.

[14]  Scott Shenker,et al.  Discretized streams: fault-tolerant streaming computation at scale , 2013, SOSP.

[15]  Jane Hillston,et al.  A compositional approach to performance modelling , 1996 .

[16]  Lei Yu,et al.  A Hadoop MapReduce Performance Prediction Method , 2013, 2013 IEEE 10th International Conference on High Performance Computing and Communications & 2013 IEEE International Conference on Embedded and Ubiquitous Computing.

[17]  Edward D. Lazowska,et al.  Quantitative system performance - computer system analysis using queueing network models , 1983, Int. CMG Conference.

[18]  Giuseppe Serazzi,et al.  Stochastic Analysis of Energy Consumption in Pool Depletion Systems , 2016, MMB/DFT.

[19]  Kevin Wilkinson,et al.  Analytical Performance Models for MapReduce Workloads , 2012, International Journal of Parallel Programming.

[20]  Roy H. Campbell,et al.  Resource Provisioning Framework for MapReduce Jobs with Performance Goals , 2011, Middleware.

[21]  Himabindu Pucha,et al.  Towards Optimizing Hadoop Provisioning in the Cloud , 2009, HotCloud.

[22]  Mauro Iacono,et al.  Modeling and analysis of performances for concurrent multithread applications on multicore and graphics processing unit systems , 2016, Concurr. Comput. Pract. Exp..