Visualisation of Distributed Systems Simulation Made Simple

Distributed (computing) systems come in various sizes and scale. They range from a single workstation computer with several processors, a cluster of compute nodes (servers) to a federation of geographically distributed data centres with millions of servers. Job scheduling is a fundamental aspect for data centre efficiency. In this paper, we present ds-viz as a visualisation aid for ds-sim, a recently developed distributed systems simulator. In particular, ds-viz significantly helps leverage the evaluation and analysis of scheduling algorithms that ds-sim facilitates to design. We show the effectiveness of these tools with some examples.

[1]  Thomas A. Henzinger,et al.  Scheduling large jobs by abstraction refinement , 2011, EuroSys '11.

[2]  Albert Y. Zomaya,et al.  Multiple Frequency Selection in DVFS-Enabled Processors to Minimize Energy Consumption , 2012, ArXiv.

[3]  Henri Casanova,et al.  Versatile, scalable, and accurate simulation of distributed applications and platforms , 2014, J. Parallel Distributed Comput..

[4]  Yeh-Ching Chung,et al.  DRASH: A Data Replication-Aware Scheduler in Geo-Distributed Data Centers , 2016, 2016 IEEE International Conference on Cloud Computing Technology and Science (CloudCom).

[5]  Dzmitry Kliazovich,et al.  GreenCloud: a packet-level simulator of energy-aware cloud computing data centers , 2010, The Journal of Supercomputing.

[6]  Rajkumar Buyya,et al.  CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms , 2011, Softw. Pract. Exp..

[7]  Rajkumar Buyya,et al.  GridSim: a toolkit for the modeling and simulation of distributed resource management and scheduling for Grid computing , 2002, Concurr. Comput. Pract. Exp..

[8]  Anne-Marie Kermarrec,et al.  Hawk: Hybrid Datacenter Scheduling , 2015, USENIX Annual Technical Conference.

[9]  Rajkumar Buyya,et al.  CloudAnalyst: A CloudSim-Based Visual Modeller for Analysing Cloud Computing Environments and Applications , 2010, 2010 24th IEEE International Conference on Advanced Information Networking and Applications.

[10]  Randy H. Katz,et al.  Heterogeneity and dynamicity of clouds at scale: Google trace analysis , 2012, SoCC '12.

[11]  Jesús Carretero,et al.  iCanCloud: A Flexible and Scalable Cloud Infrastructure Simulator , 2012, Journal of Grid Computing.

[12]  Albert Y. Zomaya,et al.  Janus: A Generic QoS Framework for Software-as-a-Service Applications , 2018, 2018 IEEE International Conference on Cluster Computing (CLUSTER).

[13]  Young Choon Lee,et al.  Holistic Approach for Studying Resource Failures at Scale , 2019, 2019 IEEE 18th International Symposium on Network Computing and Applications (NCA).