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One of the key goals in the data center today is providing storage services with service-level objectives (SLOs) for performance metrics such as latency and throughput. Meeting such SLOs is challenging due to the dynamism observed in these environments. In this position paper, we propose dynamic instantiation of virtual appliances, that is, virtual machines with storage functionality, as a mechanism to meet storage SLOs efficiently.In order for dynamic instantiation to be realistic for rapidlychanging environments, it should be automated. Therefore, an important goal of this paper is to show that such automation is feasible. We do so through a caching case study. Specifically, we build the automation framework for dynamically instantiating virtual caching appliances. This framework identifies sets of interfering workloads that can benefit from caching, determines the cache-size requirements of workloads, non-disruptively migrates the application to use the cache, and warms the cache to quickly return to acceptable service levels. We show through an experiment that this approach addresses SLO violations while using resources efficiently.

Increasingly, storage vendors are finding it difficult to leverage existing white-box and black-box modeling techniques to build robust system models that can predict system behavior in the emerging dynamic and multi-tenant data centers. White-box models are becoming brittle because the model builders are not able to keep up with the innovations in the storage system stack, and black-box models are becoming brittle because it is increasingly difficult to a priori train the model for the dynamic and multi-tenant data center environment. Thus, there is a need for innovation in system model building area. In this paper we present a machine learning based blackbox modeling algorithm called M-LISP that can predict system behavior in untrained region for these emerging multitenant and dynamic data center environments. We have implemented and analyzed M-LISP in real environments and the initial results look very promising. We also provide a survey of some common machine learning algorithms and how they fare with respect to satisfying the modeling needs of the new data center environments.

The oil and gas industry is continuously looking for material and tool designs with more robustness that provides greater operational flexibility in aggressive environments. New elastomeric systems that have been enhanced at the nanometer scale have been engineered to address these needs. Nano-enhanced EPDM showed 95-percent lower swelling rate in oil at ambient temperature. Nano-enhanced HNBR compound exhibited 15 times lower absorption in 300°F hydrocarbon fluid. Nano-enhanced FEPM compound showed 20 times lower absorption. Nano-enhanced FEPM bladder material submersed in oil at 300°F yielded a three-fold reduction in the gas transmission rate of a hydrocarbon blend containing both CO2 and of H2S. The same nano-enhanced material showed a 50-percent reduction in the degradation effects of H2S on the NBR physical properties. All of the above are lab results, and the comparisons were made to baseline commercially available rubber compounds without nano-enhancement. Our results demonstrated that nanotechnology can be very effectively used to significantly modify properties of commonly used rubber compounds in the oil and gas industry. The oil swelling rate can be drastically reduced to give operators a greater flexibility in setting the packers and reducing intervention. Sour gas-based slowdown of material retardation translates to higher tool life. These findings can be used to design new packers, sealing elements and other elastomeric components used in downhole environment. This paper will present our recent lab results along with postulated mechanism on how nanotechnologies can impact material performance in downhole applications.