Optimal Resource Provisioning for Scaling Enterprise Applications on the Cloud

Over the past years organizations have been moving their enterprise applications to the cloud to take advantage of cloud's utility computing and elasticity. However, in enterprise applications or workflows, generally, different components/tasks will have different scaling requirements and finding an ideal deployment configuration and having the application to scale up and down based on the incoming requests is a difficult task. This paper presents a novel resource provisioning policy that can find the most cost optimal setup of variety of instances of cloud that can fulfill incoming workload. All major factors involved in resource amount estimation such as processing power, periodic cost and configuration cost of each instance type and capacity of clouds are considered in the model. Additionally, the model takes lifetime of each running instance into account while trying to find the optimal setup. Benchmark experiments were conducted on Amazon cloud, using a real load trace and through two main control flow components of enterprise applications, AND and XOR. In these experiments, our model could find the most cost-optimal setup for each component/task of the application within reasonable time, making it plausible for auto-scaling any web/services based enterprise workflow/application on the cloud.

[1]  Satish Narayana Srirama,et al.  Migrating Scientific Workflows to the Cloud: Through Graph-Partitioning, Scheduling and Peer-to-Peer Data Sharing , 2014, 2014 IEEE Intl Conf on High Performance Computing and Communications, 2014 IEEE 6th Intl Symp on Cyberspace Safety and Security, 2014 IEEE 11th Intl Conf on Embedded Software and Syst (HPCC,CSS,ICESS).

[2]  Jing Xu,et al.  On the Use of Fuzzy Modeling in Virtualized Data Center Management , 2007, Fourth International Conference on Autonomic Computing (ICAC'07).

[3]  Wil M. P. van der Aalst,et al.  Workflow Patterns , 2004, Distributed and Parallel Databases.

[4]  Asser N. Tantawi,et al.  An analytical model for multi-tier internet services and its applications , 2005, SIGMETRICS '05.

[5]  Isis Truck,et al.  Using Reinforcement Learning for Autonomic Resource Allocation in Clouds: towards a fully automated workflow , 2011 .

[6]  Matthias Jarke,et al.  Mobile hosts in enterprise service integration , 2009, Int. J. Web Eng. Technol..

[7]  Jeffrey S. Chase,et al.  Automated control in cloud computing: challenges and opportunities , 2009, ACDC '09.

[8]  Martti Vasar,et al.  Framework for monitoring and testing web application scalability on the cloud , 2012, WICSA/ECSA Companion Volume.

[9]  Ibm Redbooks,et al.  Patterns: Service Oriented Architecture And Web Services , 2004 .

[10]  Brice Morin,et al.  REMICS- REuse and Migration of Legacy Applications to Interoperable Cloud Services , 2011, ServiceWave.

[11]  Randy H. Katz,et al.  A view of cloud computing , 2010, CACM.

[12]  Philip S. Yu,et al.  Dynamic Load Balancing on Web-Server Systems , 1999, IEEE Internet Comput..

[13]  Dana Petcu,et al.  MODAClouds: A model-driven approach for the design and execution of applications on multiple Clouds , 2012, 2012 4th International Workshop on Modeling in Software Engineering (MISE).

[14]  Philippe Rohou.  PaaSage Model Based Cloud Platform Upperware Deliverable D 8 . 2 , 2014 .

[15]  Johan Tordsson,et al.  An adaptive hybrid elasticity controller for cloud infrastructures , 2012, 2012 IEEE Network Operations and Management Symposium.