Ali Yadavar Nikravesh

Learn More
This paper investigates the accuracy of predictive auto-scaling systems in the Infrastructure as a Service (IaaS) layer of cloud computing. The hypothesis in this research is that prediction accuracy of auto-scaling systems can be increased by choosing an appropriate time-series prediction algorithm based on the performance pattern over time. To prove this(More)
Elasticity is one of the key benefits of cloud computing which helps customers reduce the cost. Although elasticity is beneficiary in terms of cost, obligation of maintaining Service Level Agreements leads to necessity in dealing with the cost-performance trade-off. Proactive auto-scaling is an efficient approach to overcome this problem. In this approach(More)
The elasticity characteristic of cloud computing enables clients to acquire and release resources on demand. This characteristic reduces clients' cost by making them pay for the resources they actually have used. On the other hand, clients are obligated to maintain Service Level Agreement (SLA) with their users. One approach to deal with this(More)
Mobile networks are critical for today's social mobility and the Internet. More and more people are subscribing to mobile networks, which has led to substantial demands. The network operators need to find ways of meeting the huge demands. Since mobile network resources, such as spectrum, are expensive, there is a need for efficient management of network(More)
Cost-performance trade off is one of the critical challenges in cloud computing environments. Predictive auto-scaling systems mitigate this issue by scaling in/out system automatically based on performance prediction results. The goal of this research is to investigate the impact of different prediction results on the scaling actions generated by predictive(More)
  • 1