MOTIVATION: Targeted enrichment via capture probes, also known as baits, is a promising complementary procedure for next-generation sequencing methods. This technique uses short biotinylated oligonucleotide probes that hybridize with complementary genetic material in a sample. Following hybridization, the target fragments can be easily isolated and processed with minimal contamination from irrelevant material. Designing an efficient set of baits for a set of target sequences, however, is an NP-hard problem. RESULTS: We develop a novel heuristic algorithm that leverages the similarities between the characteristics of the Minimum Bait Cover and the Closest String problems to reduce the number of baits to cover a given target sequence. Our results on real and synthetic datasets demonstrate that our algorithm, OLTA produces fewest baits for nearly all experimental settings and datasets. On average, it produces 6% and 11% fewer baits than the next best state-of-the-art methods for two major real datasets, AIV and MEGARES. Also, its bait set has the highest utilization and the minimum redundancy. AVAILABILITY AND IMPLEMENTATION: Our algorithm is available at github.com/FuelTheBurn/OLTA-Optimizing-bait-seLection-for-TArgeted-sequencing. Test data and other software are archived at doi.org/10.5281/zenodo.15086636.

Probing is a technique used for network monitoring and measurement applications, including detection of silent failures and performance analysis to verify compliance with service-level agreements. Many existing approaches to probing-path selection seek to minimize the number of probes (an NP-hard problem) or to minimize probing delay, but they fail to properly assess the network overhead imposed by the probing strategy. In this paper, we present an approximation algorithm for probing-path selection that simultaneously considers two types of overhead-minimization objectives. Our approach can be customized to achieve a desired balance between the number of probes used overall and the effect on individual network components such as network devices and links. Through analysis and simulation on various topologies and under a variety of settings, we assess the effect of customization on a number of network-overhead measures. Compared to previous work, our approach efficiently computes a set of end-to-end probing paths fully covering a network without unduly overloading it.

We present a framework for the design and analysis of probing methods to monitor network performance, an important technique for collecting measurements in tasks such as fault detection. We use this framework to study the interaction among numerous, possibly conflicting, optimization goals in the design of a probing algorithm. We present a rigorous definition of a probing-algorithm design problem that can apply broadly to network-measurement scenarios. We also present several metrics relevant to the analysis of probing algorithms, including probing frequency and network coverage, communication and computational overhead, and the amount of algorithm state required. We show inherent tradeoffs among optimization goals and give hardness results for achieving some combinations of optimization goals. We also consider the possibility of developing approximation algorithms for achieving some of the goals and describe a randomized approach as an alternative, evaluating it using our framework. Our work aids future development of low-overhead probing techniques and introduces principles from IP-based networking to theoretically grounded approaches for concurrent path-selection problems.

Autonomous routing algorithms, such as BGP, are intended to reach a globally consistent set of routes after nodes iteratively and independently collect, process, and share network information. Generally, the important role of the mechanism used to share information has been overlooked in previous analyses of these algorithms. In this paper, we explicitly study how the network-communication model affects algorithm convergence. To do this, we consider a variety of factors, including channel reliability, how much information is processed from channels, and how many channels are processed simultaneously. Using these factors, we define a taxonomy of communication models and identify particular models of interest, including those used in previous theoretical work, those that most closely model real-world implementations of BGP, and those of potential interest for the design of future routing algorithms. We characterize an extensive set of relationships among models in our taxonomy and show that convergence depends on the communication model in nontrivial ways. These results highlight that certain models are best for proving conditions that guarantee convergence, while other models are best for characterizing conditions that might permit nonconvergence.

We study situations in which autonomous systems (ASes) may have incentives to send BGP announcements differing from the AS-level paths that packets traverse in the data plane. Prior work on this issue assumed that ASes seek only to obtain the best possible outgoing path for their traffic. In reality, other factors can influence a rational AS's behavior. Here we consider a more natural model, in which an AS is also interested in attracting incoming traffic (e.g., because other ASes pay it to carry their traffic). We ask what combinations of BGP enhancements and restrictions on routing policies can ensure that ASes have no incentive to lie about their data-plane paths. We find that protocols like S-BGP alone are insufficient, but that S-BGP does suffice if coupled with additional (quite unrealistic) restrictions on routing policies. Our game-theoretic analysis illustrates the high cost of ensuring that the ASes honestly announce data-plane paths in their BGP path announcements.

The Border Gateway Protocol (BGP) allows each autonomous system (AS) to select routes to destinations based on semantically rich and locally determined policies. This autonomously exercised policy freedom can cause instability, where unresolvable policy-based disputes in the network result in interdomain route oscillations. Several recent works have established that such instabilities can only be eliminated by enforcing a globally accepted preference ordering on routes (such as shortest path). To resolve this conflict between policy autonomy and system stability, we propose a distributed mechanism that enforces a preference ordering only when disputes resulting in oscillations exist. This preserves policy freedom when possible, and imposes stability when required.

Some commonly used inter-domain-routing policies-e.g., those using BGP's MED attribute for cold-potato routing-are beyond the scope of routing theory developed to date. This is because these policies cannot be expressed as a linear preference ranking of available routes at each node. Existing characterizations of well-behaved path-vector routing, however, critically depend on this linear ranking and do not naturally extend to more complex policies. In this paper, we present a framework that is able to model these more general policies. We use it to give the broadest-known sufficient condition for robust convergence of path-vector protocols, even when complex policies are used. In doing so, we present a new, unified notion of order on policies; this reduces to earlier results in the case of restricted policies, but it allows us to analyze the practically useful but inconsistent policies that could not be directly modeled before. As an application, we rigorously analyze (and improve) various robust protocol-design proposals.

The routing of traffic between Internet domains, or Autonomous Systems (ASes), a task known as interdomain routing, is currently handled by the Border Gateway Protocol (BGP) [17]. Using BGP, autonomous systems can apply semantically rich routing policies to choose interdomain routes in a distributed fashion. This expressiveness in routing-policy choice supports domains' autonomy in network operations and in business decisions, but it comes at a price: The interaction of locally defined routing policies can lead to unexpected global anomalies, including route oscillations or overall protocol divergence (see, e.g., [20]). Networking researchers have addressed this problem by devising constraints on policies that guarantee BGP convergence without unduly limiting expressiveness and autonomy (see, e.g., [7, 8]).In addition to taking this engineering or "protocol-design" approach, researchers have approached interdomain routing from an economic or "mechanism-design" point of view. It is known that lowest-cost-path (LCP) routing can be implemented in a truthful, BGP-compatible manner [3] but that several other natural classes of routing policies cannot [2, 5]. In this paper, we present a natural class of interdomain-routing policies that is more realistic than LCP routing and admits incentive-compatible, BGP-compatible implementation. We also present several positive steps toward a general theory of incentive-compatible interdomain routing.

This paper unifies two independently developed formalisms for path-vector routing protocols such as the border gateway protocol (BGP), the standard inter-domain routing protocol for the Internet. Sobrinho (2003) and Griffin, Jaggard, and Ramachandran (2003) proved conditions for guaranteed protocol convergence, but as these works operate at different levels of abstraction in modeling the protocols, the relationship between them is not obvious. Here we provide a rigorous translation between these two frameworks and use it to connect the convergence results, yielding a more complete set of analysis tools than in either framework alone. We motivate our discussion by presenting an example of applying both frameworks to analyze a set of protocols; in doing so, we show how the models, in conjunction, give important guidelines for protocol design.

Griffin, Jaggard, and Ramachandran [2004] introduced a framework for studying design principles for path-vector protocols, such as the border gateway protocol (BGP) used for inter-domain routing in the Internet. They outlined how their framework could describe hierarchical-BGP-like systems in which routing at a node is determined by the relationship with the next-hop node on a path (e.g., an ISP-peering relationship) and some additional scoping rules (e.g., the use of backup routes). The robustness of these class-based path-vector systems depends on the presence of a global constraint on the system, but an adequate constraint has not yet been given in general. In This work, we give the best-known sufficient constraint that guarantees robust convergence. We show how to generate this constraint from the design specification of the path-vector system. We also give centralized and distributed algorithms to enforce this constraint, discuss applications of these algorithms, and compare them to algorithms given in previous work on path-vector protocol design.

BGP is unique among IP-routing protocols in that routing is determined using semantically rich routing policies. However this expressiveness has come with hidden risks. The interaction of locally defined routing policies can lead to unexpected global routing anomalies, which can be very difficult to identify and correct in the decentralized and competitive Internet environment. These risks increase as the complexity of local policies increase. which is precisely the current trend. BGP policy languages have evolved in a rather organic fashion with little effort to avoid policy-interaction problems. We believe that researchers should start to consider how to design policy languages for path-vector protocols in order to avoid routing anomalies while obtaining desirable protocol properties. We take a few steps in this direction by identifying the important dimensions of this design space and characterizing some of the inherent design trade-offs. We do this in a general way that is not constrained by the details of BGP.

A packet scheduling scheme is an important component of a network node. The choice of a scheme dictates the allocation of network resources among contending flows of the network. In this paper we study packet scheduling in the context of active networks. In an active network, intermediate nodes, called active nodes, can perform customized computations on every packet. Programmability in active networks is achieved by having active packets carry either programs or references to programs that are injected into the active nodes offline. As each packet arrives at an active node, the node executes the program associated with the packet and then routes the packet to the next node. Traditionally, packet scheduling schemes are used to allocate a single resource fairly. This cannot be directly applied to active nodes because active nodes contain multiple resources, such as CPU and bandwidth. Moreover, these resources are inter-dependent. Hence, allocating one resource fairly does not entail allocating the other resources fairly. We describe a simple packet scheduling algorithm to allocate multiple resources fairly in an active node. Using simulations, we show that the algorithm effectively allocates both CPU and bandwidth resources fairly among the contending flows.