Intelligent Sensor Placement for Hot Server Detection in Data Centers

Abstract

Abstract-Recent studies have shown that a significant portion of the total energy consumption of many data centers is caused by the inefficient operation of their cooling systems. Without effective thermal monitoring with accurate location information, the cooling systems often use unnecessarily low temperature set points to overcool the entire room, resulting in excessive energy consumption. Sensor network technology has recently been adopted for data-center thermal monitoring because of its nonintrusive nature for the already complex data center facilities and robustness to instantaneous CPU or disk activities. However, existing solutions place sensors in a simplistic way without considering the thermal dynamics in data centers, resulting in unnecessarily degraded hot server detection probability. In this paper, we first formulate the problems of sensor placement for hot server detection in a data center as constrained optimization problems in two different scenarios. We then propose a novel placement scheme based on computational fluid dynamics (CFD) to take various factors, such as cooling systems and server layout, as inputs to analyze the thermal conditions of the data center. Based on the CFD analysis in various server overheating scenarios, we apply data fusion and advanced optimization techniques to find a near-optimal sensor placement solution, such that the probability of detecting hot servers is significantly improved. Our empirical results in a real server room demonstrate the detection performance of our placement solution. Extensive simulation results in a large-scale data center with 32 racks also show that the proposed solution outperforms several commonly used placement solutions in terms of detection probability.

Optimization-Based Approaches for Minimizing Deployment Costs for Wireless Sensor Networks with Bounded Estimation Errors

“…Recent studies have considered constraints such as network connectivity and energy consumption. All coverage formulas assume that the sensor has a given detection range in event perception methods or assume the distribution of sensor measurement values in the appropriate perception method; examples include [27] for wind monitoring and [28] for data center server overheating detection. To design deployment methods, considering the characteristics of application instances, a new application-aware deployment method shown in Table 1 is proposed.…”

On the Deployment of Wireless Sensor Networks for Air Quality Mapping: Optimization Models and Algorithms

“…Even if the recent works take into account network constraints like connectivity and energy consumption, all coverage formulations either assume that sensors have a given detection range, which is the case of event-aware methods, or the assumption is instead made on the distribution of sensor measurements, which is the case of correlation-aware methods. Novel application-aware deployment methods have been recently proposed to consider the characteristics of the application case in the design of the deployment approach; examples include the work of [26] on wind monitoring and the work of [27] for hot server detection in data centers. Following the same direction, we propose in the next section to consider the context of air pollution mapping in order to define an appropriate formulation of coverage quality and then we derive optimization models and heuristic algorithms in the following sections.…”

Challenges, Applications, and Future of Wireless Sensors in Internet of Things: A Review

“…There are some applications, where these WS nodes can be equipped with a continuous power supply, like for instance, in measuring the thermal heat in data centers etc., [32]. Mostly, the WS nodes are deployed in hardto-reach locations and are equipped with removable and rechargeable batteries.…”