Bong Jun Ko

Transmit power estimation using spatially diverse measurements under wireless fading

Received power measurements at spatially distributed monitors can be usefully exploited to deduce various characteristics of active wireless transmitters. In this paper, we study the problem of “blind” estimation of a wireless nodes transmit power utilizing solely received power measurements at spatially distributed monitors, without any prior knowledge about the transmitters location or any statistical characterization of its transmit power. We first consider a deterministic setup and utilize a geometrical approach to obtain fundamental limitations on estimating the transmit power and location of an unknown wireless node. We show that a regular placement of monitors, though appealing, does not provide sufficient measurement diversity to yield a unique estimate. We then extend the setup to consider wireless fading and present a theoretical analysis of maximum likelihood (ML) estimate, which is analytically shown to be asymptotically optimal. Finally, we provide numerical results comparing the performance of the estimator through simulations and on a dataset of field measurements.

Cooperative Transmit-Power Estimation in MANETs

Transmit-power estimation is an important part in power-aware designs of mobile ad-hoc networks (MANETs). In this paper, we consider the cooperation among multiple monitor-nodes to estimate the transmit power of other nodes. Utilizing a geometric approach, we characterize the theoretical performance of such cooperative monitoring schemes and propose transmit-power estimation techniques with different number of cooperating nodes. We introduce the novel concept of confidence region that provides a fundamental confidence level for the accuracy of the power estimation and enables the development of techniques for allocating network monitors. Finally, we present a simple, distributed cooperative estimation scheme for a large-scale wireless network and give illustrative simulation results to quantify its performance.

Adaptive algorithms for diagnosing large-scale failures in computer networks

In this paper, we propose an algorithm to efficiently diagnose large-scale clustered failures. The algorithm, Cluster-MAX-COVERAGE (CMC), is based on greedy approach. We address the challenge of determining faults with incomplete symptoms. CMC makes novel use of both positive and negative symptoms to output a hypothesis list with a low number of false negatives and false positives quickly. CMC requires reports from about half as many nodes as other existing algorithms to determine failures with 100% accuracy. Moreover, CMC accomplishes this gain significantly faster (sometimes by two orders of magnitude) than an algorithm that matches its accuracy. Furthermore, we propose an adaptive algorithm called Adaptive-MAX-COVERAGE (AMC) that performs efficiently during both kinds of failures, i.e., independent and clustered. During a series of failues that include both independent and clustered, AMC results in a reduced number of false negatives and false positives.

Power Efficient Decode-and-Forward Cooperative Relaying

Cooperative communications have been proven to be effective in enhancing performance of wireless networks. In this letter, we introduce and propose adaptive relay-selection rules to offer a tradeoff between fairness and energy consumption at each node for a network with multiple, mutually cooperative nodes. Our performance analysis is supplemented by simulation results to illustrate the significant energy savings of the proposed optimal power allocation and the relay-selection rules.

On-Demand Discovery of Software Service Dependencies in MANETs

The dependencies among the components of service-oriented software applications hosted in a mobile ad hoc network (MANET) are difficult to determine due to the inherent loose coupling of the services and the transient communication topologies of the network. Yet understanding these dependencies is critical to making good management decisions, since dependence data underlie important analyses such as fault localization and impact analysis. Current methods for discovering dependencies, developed primarily for fixed networks, assume that dependencies change only slowly and require relatively long monitoring periods as well as substantial memory and communication resources, all of which are impractical in the MANET environment. We describe a new dynamic dependence discovery method designed specifically for this environment, yielding dynamic snapshots of dependence relationships discovered through observations of service interactions. We evaluate the performance of our method in terms of the accuracy of the discovered dependencies, and draw insights on the selection of critical parameters under various operational conditions. Although operated under more stringent conditions, our method is shown to provide results comparable to or better than existing methods.

Migrating running applications across mobile edge clouds: poster

Mobile edge clouds (MECs) are small cloud-like infrastructures deployed in close proximity to users, allowing users to have seamless and low-latency access to cloud services. When users move across different locations, their service applications often need to be migrated to follow the user so that the benefit of MEC is maintained. In this paper, we propose a layered framework for migrating running applications that are encapsulated either in virtual machines (VMs) or containers. We evaluate the migration performance of various real applications under the proposed framework.

Fault Localization in MANET-Hosted Service-Based Systems

Fault localization in general refers to a technique for identifying the likely root causes of failures observed in systems formed from components. Fault localization in systems deployed on mobile ad hoc networks (MANETs) is a particularly challenging task because those systems are subject to a wider variety and higher incidence of faults than those deployed in fixed networks, the resources available to track fault symptoms are severely limited, and many of the sources of faults in MANETs are by their nature transient. We present a method for localizing the faults occurring in service-based systems hosted on MANETs. The method is based on the use of dependence data that are discovered dynamically through decentralized observations of service interactions. We employ both Bayesian and timing-based reasoning techniques to analyze the data in the context of a specific fault propagation model, deriving a ranked list of candidate fault locations. We present the results of an extensive set of experiments exploring a wide range of operational conditions to evaluate the accuracy of our method.

Live Service Migration in Mobile Edge Clouds

Mobile edge clouds (MECs) bring the benefits of the cloud closer to the user, by installing small cloud infrastructures at the network edge. This enables a new breed of real-time applications, such as instantaneous object recognition and safety assistance in intelligent transportation systems, that require very low latency. One key issue that comes with proximity is how to ensure that users always receive good performance as they move across different locations. Migrating services between MECs is seen as the means to achieve this. This article presents a layered framework for migrating active service applications that are encapsulated either in virtual machines (VMs) or containers. This layering approach allows a substantial reduction in service downtime. The framework is easy to implement using readily available technologies, and one of its key advantages is that it supports containers, which is a promising emerging technology that offers tangible benefits over VMs. The migration performance of various real applications is evaluated by experiments under the presented framework. Insights drawn from the experimentation results are discussed.

Diagnosing degradation of services in hybrid wireless tactical networks

In this paper, we consider a problem related to service management and deployment in tactical military networks. Tactical networks are typically hybrid wireless networks in which there are both static and mobile nodes with several wireless interfaces, such as 802.11, 3G, satellite, etc. In tactical networks, performance degradation in services could prove fatal, so it must be diagnosed quickly. This degradation could be due to mobility or bottlenecks in capacity at network layer. We provide a cross-layer framework to detect and diagnose these causes of performance degradation as part of service management; it includes a monitoring model of services and a network model for hybrid wireless networks. In addition, we give a working example in tactical military networks to illustrate our framework. We provide an experimental setup to simulate our hybrid wireless tactical network scenario along with preliminary results.

Accuracy analysis of data aggregation for network monitoring

The quality of computing certain aggregation functions based on incomplete measurements for the purpose of distributed network monitoring is considered. Network monitoring plays a fundamental role in network management systems by providing timely information on the network status, which is crucial for administration purposes. To reduce network overhead and for easier assimilation, this information is usually presented by calculating a few key aggregate metrics. The aggregates are periodically computed from a large number of detailed events collected continuously during the course of the network operations. Under errors induced by network delays, the accuracy of typical aggregation functions used in network management systems is evaluated both analytically and by simulations. The results provide a quantifiable trade-off between accuracy and timeliness of the information acquired, which can then be used to design and optimize network management systems.