The cloud computing paradigm is attracting an increased number of complex applications to run in remote data centers. Many complex applications require parallel processing capabilities. Parallel applications of certain nature often show a decreasing utilization of CPU resources as parallelism grows, mainly because of the communication and synchronization among parallel processes. It is challenging but important for a data center to achieve a certain level of utilization of its nodes while maintaining the level of responsiveness of parallel jobs. Existing parallel scheduling mechanisms normally take responsiveness as the top priority and need nontrivial effort to make them work for data centers in the cloud era. In this paper, we propose a priority-based method to consolidate parallel workloads in the cloud. We leverage virtualization technologies to partition the computing capacity of each node into two tiers, the foreground virtual machine (VM) tier (with high CPU priority) and the background VM tier (with low CPU priority). We provide scheduling algorithms for parallel jobs to make efficient use of the two tier VMs to improve the responsiveness of these jobs. Our extensive experiments show that our parallel scheduling algorithm significantly outperforms commonly used algorithms such as extensible argonne scheduling system in a data center setting. The method is practical and effective for consolidating parallel workload in data centers.

The recent cloud computing paradigm represents a trend of moving business applications to platforms run by parties located in different administrative domains. A cloud platform is often highly scalable and cost-effective through its pay-as-you-go pricing model. However, being shared by a large number of users, the running of applications in the platform faces higher performance uncertainty compared to a dedicated platform. Existing Service Level Agreements (SLAs) cannot sufficiently address the performance variation issue. In this paper, we use utility theory leveraged from economics and develop a new utility model for measuring customer satisfaction in the cloud. Based on the utility model, we design a mechanism to support utility-based SLAs in order to balance the performance of applications and the cost of running them. We consider an infrastructure-as-a-service type cloud platform (e.g., Amazon EC2), where a business service provider leases virtual machine (VM) instances with spot prices from the cloud and gains revenue by serving its customers. Particularly, we investigate the interaction of service profit and customer satisfaction. In addition, we present two scheduling algorithms that can effectively bid for different types of VM instances to make tradeoffs between profit and customer satisfaction. We conduct extensive simulations based on the performance data of different types of Amazon EC2 instances and their price history. Our experimental results demonstrate that the algorithms perform well across the metrics of profit, customer satisfaction and instance utilization.