Yibei Ling

Identifying the missing tags in a large RFID system

Compared to the classical barcode system, radio frequency identification (RFID) extends the operational distance from inches to a number of feet (passive RFID tags) or even hundreds of feet (active RFID tags). Their wireless transmission, processing, and storage capabilities enable them to support full automation of many inventory management functions in industry. This paper studies the practically important problem of monitoring a large set of active RFID tags and identifying the missing ones-the objects that the missing tags are associated with are likely to be missing as well. This monitoring function may need to be executed frequently and therefore should be made efficient in terms of execution time in order to avoid disruption of normal inventory operations. Based on probabilistic methods, we design a series of missing-tag identification protocols that employ novel techniques to reduce the execution time. Our best protocol reduces the time for detecting the missing tags by an order of magnitude when compared to existing protocols.

Fast and compact per-flow traffic measurement through randomized counter sharing

Traffic measurement provides critical real-world data for service providers and network administrators to perform capacity planning, accounting and billing, anomaly detection, and service provision. One of the greatest challenges in designing an online measurement module is to minimize the per-packet processing time in order to keep up with the line speed of the modern routers. To meet this challenge, we should minimize the number of memory accesses per packet and implement the measurement module in the on-die SRAM. The small size of SRAM requires extremely compact data structures to be designed for storing per-flow information. The best existing work, called counter braids, requires more than 4 bits per flow and performs 6 or more memory accesses per packet. In this paper, we design a fast and compact measurement function that estimates the sizes of all flows. It achieves the optimal processing speed: 2 memory accesses per packet. In addition, it provides reasonable measurement accuracy in a tight space where the counter braids no longer work. Our design is based on a new data encoding/decoding scheme, called randomized counter sharing. This scheme allows us to mix per-flow information together in storage for compactness and, at the decoding time, separate the information of each flow through statistical removal of the error introduced during information mixing from other flows. The effectiveness of our online per-flow measurement approach is analyzed and confirmed through extensive experiments based on real network traffic traces.

Per-flow traffic measurement through randomized counter sharing

Traffic measurement provides critical real-world data for service providers and network administrators to perform capacity planning, accounting and billing, anomaly detection, and service provision. One of the greatest challenges in designing an online measurement module is to minimize the per-packet processing time in order to keep up with the line speed of the modern routers. To meet this challenge, we should minimize the number of memory accesses per packet and implement the measurement module in the on-die SRAM. The small size of SRAM requires extremely compact data structures to be designed for storing per-flow information. The best existing work, called counter braids, requires more than 4 bits per flow and performs 6 or more memory accesses per packet. In this paper, we design a fast and compact measurement function that estimates the sizes of all flows. It achieves the optimal processing speed: 2 memory accesses per packet. In addition, it provides reasonable measurement accuracy in a tight space where the counter braids no longer work. Our design is based on a new data encoding/decoding scheme, called randomized counter sharing. This scheme allows us to mix per-flow information together in storage for compactness and, at the decoding time, separate the information of each flow through statistical removal of the error introduced during information mixing from other flows. The effectiveness of our online per-flow measurement approach is analyzed and confirmed through extensive experiments based on real network traffic traces.

Efficient protocols for identifying the missing tags in a large RFID system

Compared to the classical barcode system, radio frequency identification (RFID) extends the operational distance from inches to a number of feet (passive RFID tags) or even hundreds of feet (active RFID tags). Their wireless transmission, processing, and storage capabilities enable them to support full automation of many inventory management functions in industry. This paper studies the practically important problem of monitoring a large set of active RFID tags and identifying the missing ones--the objects that the missing tags are associated with are likely to be missing as well. This monitoring function may need to be executed frequently and therefore should be made efficient in terms of execution time in order to avoid disruption of normal inventory operations. Based on probabilistic methods, we design a series of missing-tag identification protocols that employ novel techniques to reduce the execution time. Our best protocol reduces the time for detecting the missing tags by an order of magnitude when compared to existing protocols.

Scalable request routing with next-neighbor load sharing in multi-server environments

Load balancing for distributed servers is a common issue in many applications and has been extensively studied. Several distributed load balancing schemes have been proposed that proactively route individual requests to appropriate servers to best balance the load and shorten request response time. These schemes do not require a centralized load balancer. Instead, each server is responsible for determining, for each request it receives from a client, to which server in the pool the request should be forwarded for processing. We propose a new request routing scheme that is more scalable to increasing number of servers and request load than the existing schemes. The method combines random server selection and next-neighbor load sharing techniques that together prevent the staleness of load information from building up when the number of servers increases. Our simulation shows that it outperforms existing schemes under a piggyback-based load update model.

Capacity-aware multicast algorithms on heterogeneous overlay networks

The global deployment of IP multicast has been slow due to the difficulties related to heterogeneity, scalability, manageability, and lack of a robust interdomain multicast routing protocol. Application-level multicast becomes a promising alternative. Many overlay multicast systems have been proposed in recent years. However, they are insufficient in supporting applications that require any-source multicast with varied host capacities and dynamic membership. In this paper, we propose two capacity-aware multicast systems that focus on host heterogeneity, any source multicast, dynamic membership, and scalability. We extend Chord and Koorde to be capacity-aware. We then embed implicit degree-varying multicast trees on top of the overlay network and develop multicast routines that automatically follow the trees to disseminate multicast messages. The implicit trees are well balanced with the workload evenly spread across the network. We rigorously analyze the expected performance of multisource capacity-aware multicasting, which was not thoroughly addressed in any previous work. We also perform extensive simulations to evaluate the proposed multicast systems.

On Optimal Deadlock Detection Scheduling

Deadlock detection scheduling is an important, yet often overlooked problem that can significantly affect the overall performance of deadlock handling. Excessive initiation of deadlock detection increases overall message usage, resulting in degraded system performance in the absence of deadlocks, while insufficient initiation of deadlock detection increases the deadlock persistence time, resulting in an increased deadlock resolution cost in the presence of deadlocks. The investigation of this performance trade-off, however, is missing in the literature. This paper studies the impact of deadlock detection scheduling on the overall performance of deadlock handling. In particular, we show that there exists an optimal deadlock detection frequency that yields the minimum long-run mean average cost, which is determined by the message complexities of the deadlock detection and resolution algorithms being used, as well as the rate of deadlock formation, denoted as lambda. For the best known deadlock detection and resolution algorithms, we show that the asymptotically optimal frequency of deadlock detection scheduling that minimizes the overall message overhead is O((lambdan)1/3) when the total number n of processes is sufficiently large. Furthermore, we show that, in general, fully distributed (uncoordinated) deadlock detection scheduling cannot be performed as efficiently as centralized (coordinated) deadlock detection scheduling

Resilient Capacity-Aware Multicast Based on Overlay Networks

The global deployment of IP multicast has been slow due to the difficulties related to heterogeneity, scalability, manageability, and lack of a robust inter-domain multicast routing protocol. Application-level multicast becomes a promising alternative. Many overlay multicast systems have been proposed in recent years. However, they are insufficient in supporting applications that require large-scale any-source multicast with highly varied host capacities and highly dynamic membership. In this paper, we propose two capacity-aware multicast systems that focus on host heterogeneity, dynamic membership, scalability, and any source multicast. We extend Chord and Koorde to be capacity-aware. We then embed implicit degree-varying multicast trees on top of the overlay network and develop multicast routines that automatically follow the trees to disseminate multicast messages. The implicit trees are well balanced with workload evenly spread across the network. We also perform extensive simulations to evaluate the proposed multicast systems

State aggregation of large network domains

Many important network functions (e.g., QoS provision, admission control, traffic engineering, resource management) rely on the availability and the accuracy of network state information. It is impractical to maintain the complete state information of a large internetwork at a single location. Large networks are often hierarchically structured, with each domain advertising its aggregated state. To achieve scalability, a delicate tradeoff has to be made between minimizing the size and maximizing the accuracy of the aggregated state. Given certain space limitation, inaccuracy introduced by different aggregation methods varies greatly. This paper gives a unified account of state aggregation based on approximation curves. The existing aggregation methods are special cases in the solution space under this model. New aggregation methods based on polynomial curves, cubic splines, and polylines are proposed, and their accuracy/space tradeoffs are studied. Extensive simulations show that these new methods approximate the network state far more accurately than the existing ones. In particular, the polylines achieve the best accuracy/space tradeoff.

On quantification of anchor placement

This paper attempts to answer a question: for a given traversal area, how to quantify the geometric impact of anchor placement on localization performance. We present a theoretical framework for quantifying the anchor placement impact. An experimental study, as well as the field test using a UWB ranging technology, is presented. These experimental results validate the theoretical analysis. As a byproduct, we propose a two-phase localization method (TPLM) and show that TPLM outperforms the least-square method in localization accuracy by a huge margin. TPLM performs much faster than the gradient descent method and slightly better than the gradient descent method in localization accuracy. Our field test suggests that TPLM is more robust against noise than the least-square and gradient descent methods.