Availability, as a part of Service Level Agreement (SLA), is a critically important issue in cloud services, as an application may not be able to run after certain server or network failures. Cloud service providers seek to not only fulfill the SLA, but also simultaneously minimize their operating costs, which are dominated by the energy consumption. In order to minimize the impact of a server/switch failure inside the datacenter on a single application, one would like to spread out the Virtual Machines (VM) for the application across different racks. However, in doing so, the power consumption may increase significantly. In this paper, we develop a variance-based metric to measure the risk of violating the availability requirement. We then propose two heuristic algorithms to place VMs in online and offline manners, respectively. These algorithms aim to strike a balance between minimizing the risk of violating the availability requirement and minimizing the energy, in order to reduce the overall cost.

A central challenge in developing cloud datacenters Service Level Agreements is the estimation of downtime distribution of a set of provisioned servers over a service window, which is compounded by three facts. First, while steady-state probabilities have been derived for birth-death processes involving server failures and repairs, they could be highly inaccurate under transience. Furthermore, steady-state cannot be assured under typical service windows. Therefore, estimation of transient distributions is essential. Second, the processes of failures and repairs may follow any distribution and hence need to be extracted using system log data and modeled using appropriate general distributions. Third, downtime distributions over service windows depend on the number of servers and their deployment structure for a contract. We develop an efficient and generalized sample path randomization approach to precisely estimate transient probabilities under three different checkpointing strategies and three flexible failure distribution models. The estimators are unbiased, consistent, efficient and sufficient. Their asymptotic convergence is established. The estimation algorithms are computationally efficient in solving practical problems and yield rich information on transient system behaviors. The methodology is general and extensible to various server failure and repair processes characterized using birth-death modeling.

BACKGROUND AND OBJECTIVE: We postulate that professional proximity due to common patients and geographical proximity among practice locations are significant factors influencing the adoption of health information exchange (HIE) services by healthcare providers. The objective of this study is to investigate the direct and indirect network effects of these drivers on HIE diffusion. DESIGN: Multi-dimensional scaling and clustering are first used to create different clusters of physicians based on their professional and geographical proximities. Extending the Bass diffusion model to capture direct and indirect network effects among groups, the growth of HIE among these clusters is modeled and studied. The network effects among the clusters are investigated using adoption data over a 3-year period for an HIE based in Western New York. MEASUREMENT: HIE adoption parameters-external sources of influence as well as direct and indirect network coefficients-are estimated by the extended version of the Bass diffusion model. RESULTS: Direct network effects caused by common patients among physicians are much more influential on HIE adoption as compared with previously investigated social contagion and external factors. Professional proximity due to common patients does influence adoption decisions; geographical proximity is also influential, but its effect is more on rural than urban physicians. CONCLUSIONS: Flow of patients among different groups of physicians is a powerful factor in HIE adoption. Rather than merely following the market trend, physicians appear to be influenced by other physicians with whom they interact with and have common patients.

Commodities such as cloud resources (storage, computing, bandwidth) are often sold to clients on a pay-as-you-go basis. Thus, resource providers absorb all risk arising from end users' demand volatilities. We focus on the revenue risk management of commodities with highly volatile demand profiles using cloud computing as the application domain and bandwidth as the exemplar commodity. We extend the state of the art in risk hedging by introducing a new concept of dynamic forward contracts where a provider and a client flexibly interact through offers and responses over a set of time periods in a horizon. We develop an optimal pricing mechanism that takes into account the risk propensities of the provider and the client. The overall mechanism is modeled as a pair of nested dynamic programs denoting the offer-response interactions. The mechanism also incorporates two learning components: short-term learning on the client's demand and long-term learning on the client's risk propensity. We characterize two approaches for predicting the client's demand—a recursive demand prediction model and an aggregate demand prediction model. Detailed experimental studies of the proposed mechanism using real Web traffic data on the clients of Amazon Web Services have been carried out. The empirical results clearly demonstrate the superiority of the proposed mechanism over benchmark mechanisms such as the current industry practice of spot markets and static forward pricing mechanisms proposed in the literature in ex ante and ex post settings. The results also highlight key interaction effects among parameters controllable by a provider and the risk propensities of the market players, leading to valuable managerial implications for the practical adoption of the proposed mechanism.