Despite the growing popularity of Solid State Disks (SSDs) in the datacenter, little is known about their reliability characteristics in the field. The little knowledge is mainly vendor supplied, and such information cannot really help understand how SSD failures can manifest and impact the operation of production systems, in order to take appropriate remedial measures. Besides actual failure data and the symptoms exhibited by SSDs before failing, a detailed characterization effort requires wide set of data about factors influencing SSD failures, right from provisioning factors to the operational ones. This paper presents an extensive SSD failure characterization by analyzing a wide spectrum of data from over half a million SSDs that span multiple generations spread across several datacenters which host a wide spectrum of workloads over nearly 3 years. By studying the diverse set of design, provisioning and operational factors on failures, and their symptoms, our work provides the first comprehensive analysis of the what, when and why characteristics of SSD failures in production datacenters.

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This article optimizes lithium-ion battery management in a datacenter to: (i) maximize the dollar savings attainable through peak shaving, while (ii) minimizing battery degradation. To the best of the authors' knowledge, such multi-objective optimal datacenter battery management remains relatively unexplored. We solve this optimization problem using a second-order model of battery charge dynamics, coupled with a physics-based model of battery aging via solid electrolyte interphase (SEI) growth. Our optimization study focuses on a classical feedforward-feedback energy management policy, where feedforward control is used for peak shaving, and feedback is used for tracking a desired battery state of charge (SOC). Three feedforward-feedback architectures are examined: a proportional (P) control architecture, a proportionalintegral (PI) architecture, and a PI architecture with a deadband in its feedforward path. We optimize these architectures' parameters using differential evolution, for real datacenter power demand histories. Our results show a significant Pareto tradeoff between dollar savings and battery longevity for all architectures. The introduction of a deadband furnishes a more attractive Pareto front by allowing the feedforward controller to focus on shaving larger peaks. Moreover, the use of integral control improves the robustness of the feedback policy to demand uncertainties and battery pack sizing.

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Addressing nonrevenue water, a major issue for water utilities, requires identification of strategic metering locations using calibrated hydraulic models of the water network. However, calibrated hydraulic models use both static and dynamic network data and are often prohibitively expensive. We present an approach to understand water network operations that uses only the static information of the network. Specifically, we analyze water networks using augmented centrality measures. We use readily available static information about network elements (e.g., diameters of pipes) rather than calibrated dynamic information (e.g., roughness coefficients of pipes, demands at nodes), and model each network element appropriately for analysis using customized centrality measures. Our approach identifies: 1) pipes carrying higher flows; 2) nodes with higher delivery heads; and 3) pipes with higher failure impact. Each of the above helps in determining strategic instrumentation locations. We validate our analysis by comparison with fully calibrated hydraulic models for three benchmark topologies. Our experimental evaluation shows that centrality analysis yields results which have a match of more than 85% with those obtained using calibrated hydraulic models on benchmark networks without significant over-provisioning. We also present results from a real-life case study where our approach matched 78% with locations picked by experts.INDEX TERMS Water networks, centrality metrics, complex network analysis.