This paper focuses research focuses on automatic provisioning of cloud resources performed by an intermediary enterprise that provides a virtual private cloud for a single client enterprise by using resources from a public cloud. This paper concerns auto-scaling techniques for dynamically controlling the number of resources used by the client enterprise. We focus on proactive auto-scaling that is based on predictions of future workload based on the past workload. The primary goal of the auto-scaling techniques is to achieve a profit for the intermediary enterprise while maintaining a desired grade of service for the client enterprise. The technique supports both on demand requests and requests with service level agreements (SLAs). This paper presents an auto-scaling algorithm and includes a discussion of system design and implementation experience for a prototype system that implements the technique. A detailed performance analysis based on measurements made on the prototype is presented.

Additional Metadata
Keywords auto-scaling, dynamic resource provisioning, machine learning, resource allocation, resource management on clouds, scheduling with SLAs
Persistent URL dx.doi.org/10.1109/SERVICES.2015.54
Conference IEEE World Congress on Services, SERVICES 2015
Citation
Biswas, A. (Anshuman), Majumdar, S, Nandy, B. (Biswajit), & El-Haraki, A. (Ali). (2015). Predictive Auto-scaling Techniques for Clouds Subjected to Requests with Service Level Agreements. Presented at the IEEE World Congress on Services, SERVICES 2015. doi:10.1109/SERVICES.2015.54