ComplexCloudSim: Towards Understanding Complexity in QoS-Aware Cloud Scheduling

Abstract

The cloud is generally assumed to be homogeneous in most of the research efforts related to cloud resource management and the performance of cloud resource can be determined as it is predictable. However, a plethora of complexities are associated with cloud resources in the real world: dynamicity, heterogeneity and uncertainty. For heterogeneous cloud resources experiencing vast dynamic changes in performance, a critical role is played by the statistical characteristics of execution times, related to different cloud resources, to facilitate decision making in management. The cloud’s performance can be considerably influenced by the differences between the estimated and actual execution times, which may affect the robustness of resource management systems. Limitation exists in the study of cloud resource management systems’ complexities even though extensive research has been done on complexity issues in various fields from decision making in economics to computational biology. This paper concentrates on managing the research question regarding the complexity’s role in QoS-aware cloud resource management systems. We present the ComplexCloudSim. Here, CloudSim, a popular simulation tool-kit, is extended through modelling of complexity factors in the cloud, including dynamic changes of run-time performance, resource heterogeneity, and task execution times’ uncertainty. The effects of complexity on performance within cloud environments are examined by comparing four widely used heuristic cloud scheduling algorithms, given that the execution time information is inaccurate. Furthermore, a damage spreading analysis, one amongst the available complex system analysis methods, is applied to the system and simulations are run to reveal the system’s sensitivity to initial conditions within specific parameter regions. Finally, how small of a damage can spread throughout the system within the region is discussed as well as research is done for the potential ways to avoid such chaotic behaviours and develop more robust systems.