Networks are an effective abstraction for representing real systems. Consequently, network science is increasingly used in academia and industry to solve problems in many fields. Computations that determine structure properties and dynamical behaviors of networks are useful because they give insights into the characteristics of real systems. We introduce a newly built and deployed cyberinfrastructure for network science (CINET) that performs such computations, with the following features: (i) it offers realistic networks from the literature and various random and deterministic network generators; (ii) it provides many algorithmic modules and measures to study and characterize networks; (iii) it is designed for efficient execution of complex algorithms on distributed high performance computers so that they scale to large networks; and (iv) it is hosted with web interfaces so that those without direct access to high performance computing resources and those who are not computing experts can still reap the system benefits. It is a combination of application design and cyberinfrastructure that makes these features possible. To our knowledge, these capabilities collectively make CINET novel. We describe the system and illustrative use cases, with a focus on the CINET user.

The management of power consumption in datacenters has become an important problem. This needs a systematic evaluation of the as-is scenario to identify potential areas for improvement and quantify the impact of any strategy. We present a measurement study of a production datacenter from a joint perspective of power and performance at the individual server level. Our observations help correlate power consumption of production servers with their activity, and identify easily implementable improvements. We find that production servers are underutilized from an activity perspective; are overrated from a power perspective; execute temporally similar workloads over a granularity of weeks; do not idle efficiently; and have power consumptions that are well tracked by their CPU utilizations. Our measurements suggest the following steps for improvement: staggering periodic activities on servers; enabling deeper sleep states; and provisioning based on measurement.