Virtual Machine (VM) environments (e.g., VMware and Xen) are experiencing a resurgence of interest for diverse uses including server consolidation and shared hosting. An application's performance in a virtual machine environment can differ markedly from its performance in a nonvirtualized environment because of interactions with the underlying virtual machine monitor and other virtual machines. However, few tools are currently available to help debug performance problems in virtual machine environments.In this paper, we present Xenoprof, a system-wide statistical profiling toolkit implemented for the Xen virtual machine environment. The toolkit enables coordinated profiling of multiple VMs in a system to obtain the distribution of hardware events such as clock cycles and cache and TLB misses.We use our toolkit to analyze performance overheads incurred by networking applications running in Xen VMs. We focus on networking applications since virtualizing network I/O devices is relatively expensive. Our experimental results quantify Xen's performance overheads for network I/O device virtualization in uni-and multi-processor systems. Our results identify the main sources of this overhead which should be the focus of Xen optimization efforts. We also show how our profiling toolkit was used to uncover and resolve performance bugs that we encountered in our experiments which caused unexpected application behavior.

We study an inventory system under periodic review in the presence of two suppliers (or delivery modes). The emergency supplier has a shorter lead-time than the regular supplier, but the unit price he offers is higher. Excess demand is backlogged. We show that the classical "Lost Sales inventory problem" is a special case of this problem. Then, we generalize the recently studied class of Dual Index policies (Veeraraghavan and Scheller-Wolf (2007)) by proposing two classes of policies. The first class consists of policies that have an orderup-to structure for the emergency supplier. We provide analytical results that are useful for determining optimal or near-optimal policies within this class. This analysis and the policies that we propose leverage the connections we make between our problem and the lost sales problem. The second class consists of policies that have an order-up-to structure for the combined orders of the two suppliers. Here, we derive bounds on the optimal order quantity from the emergency supplier, in any period, and use these bounds for finding effective policies within this class. Finally, we undertake an elaborate computational investigation to compare the performance of the policies we propose with that of Dual Index policies. One of our policies provides an average cost-saving of 1.1 % over the Best Dual Index policy and has the same computational requirements. Another policy that we propose has a cost performance similar to the Best Dual Index policy but its computational requirements are lower.

We study a single-product single-location inventory system under periodic review, where excess demand is lost and the replenishment lead time is positive. The performance measure of interest is the long run average holding cost and lost sales penalty. For a large class of demand distributions, we show that when the lost sales penalty becomes large compared to the holding cost, the relative difference between the cost of the optimal policy and the best order-up-to policy converges to zero. For any given cost parameters, we establish a bound on this relative difference. Numerical experiments show that the best order-up-to policy performs well, yielding an average cost that is within 1.5% of the optimal cost even when the ratio between the lost sales penalty and the holding cost is just 100.

We study a stationary, single-stage inventory system, under periodic review, with fixed ordering costs and multiple sales levers (such as pricing, advertising, etc.). We show the optimality of s S-type policies in these settings under both the backordering and lost-sales assumptions. Our analysis is constructive and is based on a condition that we identify as being key to proving the s S structure. This condition is entirely based on the single-period profit function and the demand model. Our optimality results complement the existing results in this area.

We consider multiunit Vickrey auctions for procurement in supply chain settings. This is the first paper that incorporates transportation costs into auctions in a complex supply network. We first introduce an auction mechanism that makes simultaneous production and transportation decisions so that the total supply chain cost is minimized and induces truth telling from the suppliers. Numerical study shows that considerable supply chain cost savings can be achieved if production and transportation costs are considered simultaneously. However, the buyer's payments in such auctions can be high. We then develop a new Vickrey-type auction that incorporates the buyer's reservation price function into quantity allocation and payment decision. As a result, the buyer has some control over his payments at the expense of introducing uncertainty in the quantity acquired in the auction.mechanism design, VCG auctions, supply chain procurement