Optimal Resource Provisioning for Workflows in Cloud

Cloud computing has gained significant popularity over past few years. Employing service-oriented architecture and resource virtualization technology, cloud provides the highest level of scalability for enterprise applications with variant load. This feature of cloud is the main attraction for migration of workflows to the cloud. Since each task of a workflow requires different processing power to perform its operation, at time of load variation it must scale in a manner fulfilling its specific requirements the most. Scaling can be done manually, provided that the load change periods are deterministic, or automatically, when there are unpredicted load spikes and slopes in the workload. A number of auto-scaling policies have been proposed so far. Some of these methods try to predict next incoming loads, while others tend to react to the incoming load at its arrival time and change the resource setup based on the real load rate rather than predicted one. However, in both methods there is need for an optimal resource provisioning policy that determines how many servers must be added to or removed from the system in order to fulfill the load while minimizing the cost. Current methods in this field take into account several of related parameters such as incoming workload, CPU usage of servers, network bandwidth, response time, processing power and cost of the servers. Nevertheless, none of them incorporates the life duration of a running server, the metric that can contribute to finding the most optimal policy. This parameter finds importance when the scaling algorithm tries to optimize the cost with employing a spectrum of various instance types featuring different processing powers and costs. In this paper, we will propose a generic LP(linear programming) model that takes into account all major factors involved in scaling including periodic cost, configuration cost and processing power of each instance type, instance count limit of clouds, and also life duration of each instance with customizable level of precision, and outputs an optimal combination of possible instance types suiting each task of a workflow the most. We created a simulation tool based on the proposed model and used 24hour workload of ClarkNet ISP to conduct performance experiments. The results of experiments suggest that our optimal policy can minimize the cost of running a workflow in the cloud.