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The tin-selenide and tin-sulfide classes of materials undergo multiple structural transitions under high pressure leading to periodic lattice distortions, superconductivity, and topologically non-trivial phases, yet a number of controversies exist regarding the structural transformations in these systems. We perform first-principles calculations within the framework of density functional theory and a careful comparison of our results with available experiments on SnSe 2 reveals that the apparent contradictions among high-pressure results can be attributed to differences in experimental conditions. We further demonstrate that under hydrostatic pressure a superstructure can be stabilized above 20 GPa in SnS 2 via a periodic lattice distortion as found recently in the case of SnSe 2 , and that this pressure-induced phase transition is due to the combined effect of Fermi surface nesting and electron–phonon coupling at a momentum wave vector q = (1/3, 1/3, 0). In addition, we investigate the contribution of nonadiabatic corrections on the calculated phonon frequencies, and show that the quantitative agreement between theory and experiment for the high-energy A 1g phonon mode is improved when these effects are taken into account. Finally, we examine the nature of the superconducting state recently observed in SnSe 2 under nonhydrostatic pressure and predict the emergence of superconductivity with a comparable critical temperature in SnS 2 under similar experimental conditions. Interestingly, in the periodic lattice distorted phases, the critical temperature is found to be reduced by an order of magnitude due to the restructuring of the Fermi surface. 

Academic cloud infrastructures require users to specify an estimate of their resource requirements. The resource usage for applications often depends on the input file sizes, parameters, optimization flags, and attributes, specified for each run. Incorrect estimation can result in low resource utilization of the entire infrastructure and long wait times for jobs in the queue. We have designed a Resource Utilization based Migration (RUMIG) system to address the resource estimation problem. We present the overall architecture of the two-stage elastic cluster design, the Apache Mesos-specific container migration system, and analyze the performance for several scientific workloads on three different cloud/cluster environments. In this paper we (b) present a design and implementation for container migration in a Mesos environment, (c) evaluate the effect of right-sizing and cluster elasticity on overall performance, (d) analyze different profiling intervals to determine the best fit, (e) determine the overhead of our profiling mechanism. Compared to the default use of Apache Mesos, in the best cases, RUMIG provides a gain of 65% in runtime (local cluster), 51% in CPU utilization in the Chameleon cloud, and 27% in memory utilization in the Jetstream cloud. 

Apache Mesos, a two-level resource scheduler, provides resource sharing across multiple users in a multi-tenant clustered environment. Computational resources (i.e., CPU, memory, disk, etc.) are distributed according to the Dominant Resource Fairness (DRF) policy. Mesos frameworks (users) receive resources based on their current usage and are responsible for scheduling their tasks within the allocation. We have observed that multiple frameworks can cause fairness imbalance in a multi-user environment. For example, a greedy framework consuming more than its fair share of resources can deny resource fairness to others. The user with the least Dominant Share is considered first by the DRF module to get its resource allocation. However, the default DRF implementation, in Apache Mesos' Master allocation module, does not consider the overall resource demands of the tasks in the queue for each user/framework. This lack of awareness can lead to poor performance as users without any pending task may receive more resource offers, and users with a queue of pending tasks can starve due to their high dominant shares. In a multi-tenant environment, the characteristics of frameworks and workloads must be understood by cluster managers to be able to define fairness based on not only resource share but also resource demand and queue wait time. We have developed a policy driven queue manager, Tromino, for an Apache Mesos cluster where tasks for individual frameworks can be scheduled based on each framework's overall resource demands and current resource consumption. Dominant Share and demand awareness of Tromino and scheduling based on these attributes can reduce (1) the impact of unfairness due to a framework specific configuration, and (2) unfair waiting time due to higher resource demand in a pending task queue. In the best case, Tromino can significantly reduce the average waiting time of a framework by using the proposed Demand-DRF aware policy.