1 Characterization of Software Aging Effects in Elastic Storage Mechanisms for Private Clouds Rubens Matos, Jean Araujo, Vandi Alves and Paulo Maciel Presenter: Vandi Alves Federal University of Pernambuco, Brazil The 4th International Workshop on Software Aging and Rejuvenation (WoSAR 2012) Dallas, Texas, USA

2 Summary Introduction Related Works Experimental Study Result Analysis Final Remarks

3 Introduction Cloud Computing Fundamentally provide access to large pools of data and computational resources Cloud-based architectures are designed to be scalable, cost-effective and to provide a robust service platform

4 Introduction IaaS Framework Elastic block storage provides flexible allocation of remote storage volumes to the virtual machines

5 Related Works Differences from this paper to our previous work* The workload is to the KVM hypervisor directly (libvirt interface) and not through the Eucalyptus NC We try to isolate the hypervisor from the Eucalyptus framework This paper also gives a more detailed view on the aging effects by performing a correlation analysis with the collected data *R.MatosJunior,J.Araujo,V.Alves,P.Maciel,Experimental evaluation of software aging effects in the eucalyptus elastic block storage, in: Proceedings of the IEEE Int. Conf. on Systems, Man, and Cybernetics (SMC’12), Seoul, Korea, 2012

6 Experimental Study The workload used in this paper was designed to accelerate possible faults and the occurrence of related aging symptoms Shell Script These operations are issued using the virsh utility, which is part of the Libvirt, a software collection to manage virtual machines

7 Experimental Study The testbed uses one KVM virtual machine instantiated in a host with an Intel Core2Quad 2.66 GHz CPU, 4 GB of RAM, and a 250 GB SATA hard disk An Apache HTTP server runs on the VM We measured the response time of a simple page hosted in the VM, while running the aging acceleration workload

8 Experimental Study After a storage volume is attached to the VM, the script waits 20 seconds to detach that volume. After that, the script waits 10 seconds to attach a new volume to the VM

9 Experimental Study There is a growth of about 15 MB in the virtual and resident memory used by the Node Controller after 8 days of experiment

10 Result Analysis CPU utilization of the KVM process related to the VM increases and reaches more than 80% in average, with peaks of 100%

11 Result Analysis This can be considered the most critical result observed in this study, because such a high CPU utilization can make the system taking too much time to respond and even cause failures in the execution of new requests

12 Result Analysis The same resources were monitored inside the VM but no increase in both CPU or memory was noticed, therefore the hypervisor(KVM) is the most likely faulty point in the environment

13 Result Analysis The response time for the http requests was around 250 milliseconds and it reached more than 6 seconds in the end of the experiment, evidencing a big impact to the quality of service for end-users due the software aging phenomenon

14 Result Analysis A correlation analysis was performed to identify the relationship between the degradation of response time and the other performance measures in the same machine Pearson’s R correlation index was computed. High correlation detected between the variables. Correlation Matrix

15 Result Analysis Since memory usage had a small increase, and swap usage is not growing: Increase in CPU utilization is the most likely cause for response time degradation.

16 Final Remarks The high CPU utilization highlighted possible faults related to EBS volumes management supported by the KVM hypervisor. Since KVM is used in many Linux-based virtualized environments, the aging symptoms showed here may also occur in other systems besides the Eucalyptus-based ones.

17 Questions