Jonathan Lenchner

Minimum-cost coverage of point sets by disks

We consider a class of geometric facility location problems in which the goal is to determine a set <i>X</i> of disks given by their centers <i>(t_j)</i> and radii <i>(r_j)</i> that cover a given set of demand points <i>Y∈R</i>² at the smallest possible cost. We consider cost functions of the form Ε<i>_jf(r_j)</i>, where <i>f(r)=r</i>^α is the cost of transmission to radius <i>r</i>. Special cases arise for α=1 (sum of radii) and α=2 (total area); power consumption models in wireless network design often use an exponent α>2. Different scenarios arise according to possible restrictions on the transmission centers <i>t_j</i>, which may be constrained to belong to a given discrete set or to lie on a line, etc.We obtain several new results, including (a) exact and approximation algorithms for selecting transmission points <i>t_j</i> on a given line in order to cover demand points <i>Y∈R</i>²; (b) approximation algorithms (and an algebraic intractability result) for selecting an optimal line on which to place transmission points to cover <i>Y</i>; (c) a proof of NP-hardness for a discrete set of transmission points in <i>R²</i> and any fixed α>1; and (d) a polynomial-time approximation scheme for the problem of computing a <i>minimum cost covering tour</i> (MCCT), in which the total cost is a linear combination of the transmission cost for the set of disks and the length of a tour/path that connects the centers of the disks.

Utility-function-driven energy-efficient cooling in data centers

The sharp rise in energy usage in data centers, fueled by increased IT workload and high server density, and coupled with a concomitant increase in the cost and volatility of the energy supply, have triggered urgent calls to improve data center energy efficiency. In response, researchers have developed energy-aware IT systems that slow or shut down servers without sacrificing performance objectives. Several authors have shown that utility functions are a natural and advantageous framework for self-management of servers to joint power and performance objectives. We demonstrate that utility functions are a similarly powerful framework for flexibly managing entire data centers to joint power and temperature objectives. After showing how utility functions can capture a wide range of objectives and tradeoffs that an operator might wish to specify, we illustrate the resulting range in behavior and energy savings using experimental results from a real data center that is cooled by two computer room air-conditioning (CRAC) units equipped with variable-speed fan drives.

Towards data center self-diagnosis using a mobile robot

We describe an inexpensive robot that serves as a physical autonomic element, capable of navigating, mapping and monitoring data centers with little or no human involvement, even ones that it has never seen before. Through a series of real experiments and simulations, we establish that the robot is sufficiently accurate, efficient and robust to be of practical benefit in real data center environments. We demonstrate how the robots integration with Maximo for Energy Optimization, a commercial data center energy management product, supports autonomic management at the level of the data center as a whole, particularly self-diagnosis of emerging thermal problems.

Robotic mapping and monitoring of data centers

We describe an inexpensive autonomous robot capable of navigating previously unseen data centers and monitoring key metrics such as air temperature1. The robot provides real-time navigation and sensor data to commercial IBM software, thereby enabling real-time generation of the data center layout, a thermal map and other visualizations of energy dynamics. Once it has mapped a data center, the robot can efficiently monitor it for hot spots and other anomalies using intelligent sampling. We demonstrate the robots effectiveness via experimental studies from two production data centers.

A Symbiotic Cognitive Computing Perspective on Autonomic Computing

Symbiotic Cognitive Systems (SCS) are multi-agent systems comprising both human and software agents that are designed to collectively perform cognitive tasks such as decision-making better than humans or software agents can unaided. Autonomic Computing Systems (ACS) are multi-agent systems that manage applications as well as software and hardware resources in accordance with goals specified by human administrators and users. SCS and ACS share some key characteristics. First, both are designed to extend human intellectual capabilities, and as such they require effective means by which humans can communicate their objectives to the computing system. Second, their natural architecture is a multi-agent system in which dozens, hundreds or even more semi-autonomous entities interact. In both SCS and ACS, issues of inter-agent communication and coordination come to the fore. We report our experience with a moderate-scale SCS prototype that helps human experts make decisions with financial impacts ranging from millions to even billions of US: corporate mergers and acquisitions. Taking advantage of the commonalities, we translate this experience into insights that may benefit future research on ACS, and recommend a stronger focus on agent-human communication and building realistic system prototypes.

Conversational Bootstrapping and Other Tricks of a Concierge Robot

We describe the effective use of online learning to enhance the conversational capabilities of a concierge robot that we have been developing over the last two years. The robot was designed to interact naturally with visitors and uses a speech recognition system in conjunction with a natural language classifier. The online learning component monitors interactions and collects explicit and implicit user feedback from a conversation and feeds it back to the classifier in the form of new class instances and adjusted threshold values for triggering the classes. In addition, it enables a trusted master to teach it new question-answer pairs via question-answer paraphrasing, and solicits help with maintaining question-answer-class relationships when needed, obviating the need for explicit programming. The system has been completely implemented and demonstrated using the SoftBank Robotics [34] humanoid robots Pepper and NAO, and the telepresence robot known as Double from Double Robotics [4].

A robot as mobile sensor and agent in data center energy management

In this poster/software demonstration we illustrate the integration of an autonomous mobile robot into a slightly customized version of a commercially available asset and data center energy management application known as Maximo Asset Management for Energy Optimization (MEO), version 7.1.1, through a number of practical scenarios. The scenarios showcase increasing degrees of autonomy and sophistication in the areas of data center mapping, monitoring and thermally-aware diagnostics.

Opposite-Quadrant Depth in the Plane

Given a set S of n points in the plane, the opposite-quadrant depth of a point p∈S is defined as the largest number k such that there are two opposite axis-aligned closed quadrants (NW and SE, or SW and NE) with apex p, each quadrant containing at least k elements of S. We prove that S has a point with opposite-quadrant depth at least n/8. If the elements of S are in convex position, then we can guarantee the existence of an element whose opposite-quadrant depth is at least n/4. Both results are asymptotically best possible.

Data center asset tracking using a mobile robot

Management and monitoring of data centers is a growing field of interest, with much current research, and the emergence of a variety of commercial products aiming to improve performance, resource utilization and energy efficiency of the computing infrastructure. Despite the large body of work on optimizing data center operations, few studies actually focus on discovering and tracking the physical layout of assets in these centers. Such asset tracking is a prerequisite to faithfully performing administration and any form of optimization that relies on physical layout characteristics. In this work, we describe an approach to completely automated asset tracking in data centers, employing a vision-based mobile robot in conjunction with an ability to manipulate the indicator LEDs in blade centers and storage arrays. Unlike previous large-scale asset-tracking methods, our approach does not require the tagging of assets (e.g., with RFID tags or barcodes), thus saving considerable expense and human labor. The approach is validated through a series of experiments in a production industrial data center.

Demo: A robot-in-residence for data center thermal monitoring and energy efficiency management

We will demonstrate a robot for data center energy management, in action, on a simulated data center floor. We shall highlight the robots navigation, tile and obstacle classification, event scheduling and preemption capabilities, along with its ability to discover charging docks, and successfully dock with extreme precision. We shall also show simulations on real data center layouts evincing navigational efficiency gains obtained by our latest heuristic enhancements.