Data Center Coalition

A. Shaikh, S. Beylergil, L. Scorr, G. Kilic-Berkmen, A. Freeman, C. Klein, Johanna Junker, S. Loens, N. Brüggemann, A. Münchau, T. Bäumer, M. Vidailhet, E. Roze, C. Bonnet, J. Jankovic, J. Jimenez-shahed, Neepa Patel, L. Marsh, C. Comella, R. Barbano, B. Berman, I. Malaty, A. Wagle Shukla, S. Reich, M. LeDoux, A. Berardelli, G. Ferrazzano, N. Stover, W. Ondo, Sarah Pirio Richardson, R. Saunders-Pullman, Z. Mari, P. Agarwal, C. Adler, S. Chouinard, S. Fox, A. Brashear, D. Truong, O. Suchowersky, S. Frank, S. Factor, J. Perlmutter, H. Jinnah

OBJECTIVE To assess the clinical manifestations and predictors of different types of tremors in a individuals with different types of isolated dystonia. METHODS Clinical manifestations of tremor were assessed in a multicenter, international cross-sectional, cohort study of 2362 individuals with all types of isolated dystonia (focal, segmental, multifocal and generalized) recruited through the Dystonia Coalition. RESULTS Methodical and standardized assessments of all subjects in this cohort revealed the overall prevalence of any type of tremor was 53.3%. The prevalence of dystonic tremor varied from 36.9-48.4%, depending on criteria used to define it. To identify the factors associated with tremors in dystonia, the data were analyzed by generalized linear modeling and cluster analyses. Generalized linear modeling indicated two of the strongest factors associated with tremor included body region affected by dystonia and recruitment center. Tremor was also associated with severity of dystonia and duration of dystonia, but not with sex or race. The cluster analysis distinguished eight subgroups within the whole cohort; defined largely by body region affected with dystonia, and secondarily by other clinical characteristics. CONCLUSION The large number of cases evaluated by an international team of movement disorder experts facilitated the dissection of several important factors that influence the apparent prevalence and phenomenology of tremor in dystonia. These results are valuable for understanding the many differences reported in prior studies, and for guiding future studies of the nosology of tremor and dystonia.

Huaqing Zhang, Yong Xiao, Shengrong Bu, Richard Yu, D. Niyato, Zhu Han

The increasing demand of data computing and storage for cloud-based services motivates the development and deployment of large-scale data centers. This paper studies the resource allocation problem for the data center networking system when multiple data center operators (DCOs) simultaneously serve multiple service subscribers (SSs). We formulate a hierarchical game to analyze this system where the DCOs and the SSs are regarded as the leaders and followers, respectively. In the proposed game, each SS selects its serving DCO with preferred price and purchases the optimal amount of resources for the SS's computing requirements. Based on the responses of the SSs’ and the other DCOs’, the DCOs decide their resource prices so as to receive the highest profit. When the coordination among DCOs is weak, we consider all DCOs are noncooperative with each other, and propose a sub-gradient algorithm for the DCOs to approach a sub-optimal solution of the game. When all DCOs are sufficiently coordinated, we formulate a coalition game among all DCOs and apply Kalai-Smorodinsky bargaining as a resource division approach to achieve high utilities. Both solutions constitute the Stackelberg Equilibrium. The simulation results verify the performance improvement provided by our proposed approaches.

S. K. Addya, Anurag Satpathy, B. Ghosh, Sandip Chakraborty, S. Ghosh, Sajal K. Das

Applications hosted in commercial clouds are typically multi-tier and comprise multiple tightly coupled virtual machines (VMs). Service providers (SPs) cater to the users using VM instances with different configurations and pricing depending on the location of the data center (DC) hosting the VMs. However, selecting VMs to host multi-tier applications is challenging due to the trade-off between cost and quality of service (QoS) depending on the placement of VMs. This paper proposes a multi-cloud broker model called CoMCLOUD to select a sub-optimal VM coalition for multi-tier applications from an SP with minimum coalition pricing and maximum QoS. To strike a trade-off between the cost and QoS, we use an ant-colony-based optimization technique. The overall service selection game is modeled as a first-price sealed-bid auction aimed at maximizing the overall revenue of SPs. Further, as the hosted VMs often face demand spikes, we present a parallel migration strategy to migrate VMs with minimum disruption time. Detailed experiments show that our approach can improve the federation profit up to 23% at the expense of increased latency of approximately 15%, compared to the baselines.

A. Chalker, C. Hillegas, A. Sill, S. Geva, C. Stewart

The landscape of research in science and engineering is heavily reliant on computation and data processing. There is continued and expanded usage by disciplines that have historically used advanced computing resources, new usage by disciplines that have not traditionally used HPC, and new modalities of the usage in Data Science, Machine Learning, and other areas of AI. Along with these new patterns have come new advanced computing resource methods and approaches, including the availability of commercial cloud resources. The Coalition for Academic Scientific Computation (CASC) has long been an advocate representing the needs of academic researchers using computational resources, sharing best practices and offering advice to create a national cyberinfrastructure to meet US science, engineering, and other academic computing needs. CASC has completed the first of what we intend to be an annual survey of academic cloud and data center usage and practices in analyzing return on investment in cyberinfrastructure. Critically important findings from this first survey include the following: many of the respondents are engaged in some form of analysis of return in research computing investments, but only a minority currently report the results of such analyses to their upper-level administration. Most respondents are experimenting with use of commercial cloud resources but no respondent indicated that they have found use of commercial cloud services to create financial benefits compared to their current methods. There is clear correlation between levels of investment in research cyberinfrastructure and the scale of both cpu core-hours delivered and the financial level of supported research grants. Also interesting is that almost every respondent indicated that they participate in some sort of national cooperative or nationally provided research computing infrastructure project and most were involved in academic computing-related organizations, indicating a high degree of engagement by institutions of higher education in building and maintaining national research computing ecosystems. Institutions continue to evaluate cloud-based HPC service models, despite having generally concluded that so far cloud HPC is too expensive to use compared to their current methods.

S. Geva, A. Chalker, C. Hillegas, D. Petravick, A. Sill, Craig Stewart

Availability of cloud-based resource delivery modes is transforming many areas of computing. Academic research computing and data (RCD) support largely remains based on on-premises delivery and has adopted commercial clouds more slowly than the private sector for a variety of stated reasons including factors related to cost efficiency, return on investment, institutional requirements, high costs for bulk commercial cloud computing usage, and funding patterns. Other factors involved in selection of computing resource delivery modes include capabilities and applications that are available only in or best adapted to specific computing environments. It is important for the higher education and research communities to be able to learn from each other as institutions and individuals to make optimum use of appropriate modes of delivery for RCD resources. This paper reports an overview of results from the second annual community-wide survey conducted by the Coalition for Advanced Scientific Computation on patterns of funding, usage, and return on investment for academic research computing and data resources. The results show that on-premises delivery continues to remain the preferred mode for RCD resources for most responding institutions as found in the first survey, but that commercial cloud usage is beginning to be reported for production use by a small number of respondents to the survey. Reasons for these preferences are further explored in the survey and initial high-level results are reported here.