Virtual machines hosted in virtualized data centers are important providers of computational resources in the era of cloud computing. Efficient scheduling of data centers' virtual machines can reduce the number of physical servers needed to host the virtual machines and, in turn, reduce the energy and other capital costs for maintaining the virtualized data centre. In this paper, we propose an innovative approach to achieve efficient pro-active VM scheduling. Our approach uses a multi-capacity bin packing technique that efficiently places VMs onto physical servers. We use time-series analysis techniques to extract not only low frequency information about future VM workloads but also high frequency information for VM workload correlations. We show that the proposed algorithms mathematically guarantee the VM scheduling meets the Service Level Objectives (SLO) and, moreover, guarantee statistically that the desired success probability of the SLO is met. Evaluation of our technique on production (real) workloads shows that our approach reduces by up to 15% the number of physical machines. We also see improvements of up to 18% for production workloads in machine utilization.

The increased popularity of grid systems and cycle sharing across organizations requires scalable systems that provide facilities to locate resources, to be fair in the use of those resources, and to monitor jobs executing on remote systems. This paper describes the GridCop system which allows a computation on a remote, and potentially fraudulent, host system to be monitored for progress and execution correctness. A novel feature of our system is that it constructs cooperating submitter and host programs from the original program, and these programs allow both progress and execution correctness to be monitored with negligible overhead while providing protection against common fraudulent behaviors. Experimental results show that the overhead of this monitoring is low on both the submitting and host machines. We describe compiler algorithms that allow the required monitoring code to be automatically generated.