Category-Based Workload Modeling for Hardware Load Prediction in a Heterogeneous IaaS Cloud

Henryk Krawczyk, Jerzy Proficz, Tomasz Ziółkowski


The paper presents a method of hardware load prediction using workload models based on application categories and high-level characteristics. Application of the method to the problem of optimization of virtual machine scheduling in a heterogeneous Infrastructure as a Service (IaaS) computing cloud is described.


IaaS; cloud computing; workload modeling; hardware load prediction

Full Text:



Manvi S. S., Shaym G. K.: Resource management for Infrastructure as a Service (IaaS) in cloud computing: A survey. [in:] Journal of Network and Computer Applications, vol. 41, Elsevier, 2014, p. 424-440.

Moreno I. S., Garraghan P., Townend P., Xu J.: Analysis, Modeling and Simulation of Workload Patterns in a Large-Scale Utility Cloud. [in:] Cloud Computing, IEEE Transactions on, vol. 2, no. 2, IEEE, 2014, p. 208-221.

Mian R., Martin P., Vazquez-Poletti J. L.: Provisioning data analytic workloads in a cloud. [in:] Future Generation Computer System, vol. 29, no. 6, Elsevier, 2013,

p. 1452-1458.

Kousiouris G., Menychtas A., Kyriazis D., Gogouvitis S., Varvarigou T.: Dynamic, behavioral-based estimation of resource provisioning based on high-level application terms in Cloud platforms. [in:] Future Generation Computer Systems, vol. 32, Elsevier, 2014, p. 27-40.

Zhang W., Liu J., Liu C., Zheng Q., Zhang W.: Workload modeling for virtual machine-hosted application. [in:] Expert Systems with Applications, vol. 42, no. 4, Elsevier, 2015, p. 1835-1844.

Weingärtner R., Bräscher G. B., Westphall C. B.: Cloud resource management:

A survey on forecasting and profiling models. [in:] Journal of Network and Computer Applications, vol. 47, Elsevier, 2015, p. 99-106.

Kephart J. O., Chess D. M.: The vision of autonomic computing. [in:] Computer, vol. 36, no. 1, IEEE, 2003, p. 41-50.

Lorido-Botran T., Miguel-Alonso J., Lozano J. A.: A Review of Auto-scaling Techniques for Elastic Applications in Cloud Environments. [in:] Journal of Grid Computing, vol. 12, no. 4, Springer Netherlands, 2014, p. 559-592.

Srikantaiah S., Kansal A., Zhao F.: Energy Aware Consolidation for Cloud Computing. [in:] Proceedings of the 2008 Conference on Power Aware Computing and Systems, USENIX Association, 2008, p. 10.

Ferreto T. C., Netto M. A. S., Calheiros R. N., De Rose C. A. F.: Server consolidation with migration control for virtualized data centers. [in:] Future Generation Computer Systems, vol. 27, no. 8, Elsevier, 2011, p. 1027-1034.

Corradi A., Fanelli M., Foschini L.: VM consolidation: A real case based on OpenStack Cloud. [in:] Future Generation Computer Systems, vol. 32, Elsevier, 2014, p. 118-127.

Iyer R., Illikkal R., Tickoo O., Zhao L., Apparao P., Newell D.: VM3: Measuring, modeling and managing VM shared resources. [in:] Computer Networks, vol. 53, no. 17, Elsevier, 2009, p. 2873-2887.

Tesauro G., Jong N. K., Das R., Bennani M. N.: On the Use of Hybrid Reinforcement Learning for Autonomic Resource Allocation. [in:] Cluster Computing, vol. 10, no. 3, Springer US, 2007, p. 287-299.

Gmach D., Rolia J., Cherkasova L., Kemper A.: Resource Pool Management: Reactive Versus Proactive or Let's Be Friends. [in:] Computer Networks, vol. 53, no. 17, Elsevier, 2009, p. 2905-2922.

Huang C. J., Guan C. T., Chen H. M., Wang Y. W., Chang S. C., Li C. Y., Weng C. H.: An adaptive resource management scheme in cloud computing. [in:] Engineering Applications of Artificial Intelligence, vol. 26, no. 1, Elsevier, 2013, p. 382-389.

Beloglazov A.: Energy-Efficient Management of Virtual Machines in Data Centers for Cloud Computing. Department of Computing and Information Systems, The University of Melbourne, 2013.

Orzechowski P., Proficz J., Krawczyk H., Szymański J.: Categorization of Cloud Workload Types with Clustering. [in:] International Conference on Signal, Networks, Computing, and Systems 2016, Proceedings of, New Delhi, India (in press).