IEEE International Performance Computing and Communications Conference (2011)
Orlando, FL, USA
Nov. 17, 2011 to Nov. 19, 2011
Jianzhe Tai , Northeastern University, Boston, MA 02115, USA
Juemin Zhang , Northeastern University, Boston, MA 02115, USA
Jun Li , Northeastern University, Boston, MA 02115, USA
Waleed Meleis , Northeastern University, Boston, MA 02115, USA
Ningfang Mi , Northeastern University, Boston, MA 02115, USA
Cloud computing nowadays becomes quite popular among a community of cloud users by offering a variety of resources. However, burstiness in user demands often dramatically degrades the application performance. In order to satisfy peak user demands and meet Service Level Agreement (SLA), efficient resource allocation schemes are highly demanded in the cloud. However, we find that conventional load balancers unfortunately neglect cases of bursty arrivals and thus experience significant performance degradation. Motivated by this problem, we propose new burstiness-aware algorithms to balance bursty workloads across all computing sites, and thus to improve overall system performance. We present a smart load balancer, which leverages the knowledge of burstiness to predict the changes in user demands and on-the-fly shifts between the schemes that are "greedy" (i.e., always select the best site) and "random" (i.e., randomly select one) based on the predicted information. Both simulation and real experimental results show that this new load balancer can adapt quickly to the changes in user demands and thus improve performance by making a smart site selection for cloud users under both bursty and non-bursty workloads.
W. Meleis, N. Mi, J. Zhang, J. Li and J. Tai, "ArA: Adaptive resource allocation for cloud computing environments under bursty workloads," IEEE International Performance Computing and Communications Conference(PCCC), Orlando, FL, USA, 2011, pp. 1-8.