We consider the problem of generating a connected identifying code for an arbitrary graph. After a brief motivation, we show that the decision problem regarding the existence of such a code is NP-complete, and we propose a novel polynomial-time approximation ConnectID that transforms any identifying code into a connected version of at most twice the size, thus leading to an asymptotically optimal approximation bound. When the input identifying code to is robust to graph distortions, we show that the size of the resulting connected code is related to the best error-correcting code of a given minimum distance, permitting the use of known coding bounds. In addition, we show that the size of the input and output codes converge for increasing robustness, meaning that highly robust identifying codes are almost connected. Finally, we evaluate the performance ConnectID of on various random graphs. Simulations for Erdos-Rényi random graphs show that the connected codes generated are actually at most 25% larger than their unconnected counterparts, while simulations with robust input identifying codes confirm that robustness often provides connectivity for free.

Identifying codes have been proposed as an abstraction for implementing monitoring tasks such as indoor localization using wireless sensor networks. In this approach, sensors' radio coverage overlaps in unique ways over each identifiable region, according to the codewords of an identifying code. While connectivity of the underlying identifying code is necessary for routing data to a sink, existing algorithms that produce identifying codes do not guarantee such a property. As such, we propose a novel polynomial-time algorithm called ConnectID that transforms any identifying code into a connected version that is also an identifying code and is provably at most twice the size of the original. We evaluate the performance of ConnectID on various random graphs, and our simulations show that the connected codes generated are actually at most 25% larger than their non-connected counterparts.

In response to the high throughput needs of grid and cloud computing applications, several production networks have recently started to support advance reservation of dedicated circuits. An important open problem within this context is to devise advance reservation algorithms that can provide provable throughput performance guarantees independently of the specific network topology and arrival pattern of reservation requests. In this paper, we first show that the throughput performance of greedy approaches, which return the earliest possible completion time for each incoming request, can be arbitrarily worse than optimal. Next, we introduce two new online, polynomial-time algorithms for advance reservation, called BatchAll and BatchLim. Both algorithms are shown to be throughput-optimal through the derivation of delay bounds for 1 + ε bandwidth augmented networks. The BatchLim algorithm has the advantage of returning the completion time of a connection immediately as a request is placed, but at the expense of looser delay performance than BatchAll. We then propose a simple approach that limits path dispersion, i.e., the number of parallel paths used by the algorithms, while provably bounding the maximum reduction factor in the transmission throughput. We prove that the number of paths needed to approximate any flow is quite small and never exceeds the total number of edges in the network. Through simulation for various topologies and traffic parameters, we show that the proposed algorithms achieve reasonable delay performance, even at request arrival rates close to capacity bounds, and that three to five parallel paths are sufficient to achieve near-optimal performance.

New architectures have recently been proposed and deployed to support end-to-end advance reservation of network resources. These architectures rely on the use a centralized scheduler, which may be unpractical in large or administratively heterogeneous networks. In this work, we explore and demonstrate the feasibility of implementing distributed solutions for advance reservation. We introduce a new distributed, distance-vector algorithm, called Distributed Advance Reservation (DAR), that provably returns the earliest time possible for setting up a connection between any two nodes. Our main findings in this context are the following: (i) we prove that widest path routing and path switching (i.e, allowing a connection to switch between different paths) are necessary to guarantee earliest scheduling; (ii) we propose a novel approach for loop-free distributed widest path routing, leveraging the recently proposed DIV framework. Our routing results directly extend to on-demand QoS routing problems.

We introduce a new algorithmic framework for advanced channel reservation in ultra high speed networks, called Graded Channel Reservation (GCR). GCR allows users to specify minimum bandwidth and duration requirements for their connections. GCR returns the highest graded path, selected according to a general, multi-criteria optimization objective. In particular, if the optimization criterion is delay, we prove that GCR returns the earliest time available to establish the connection. The computational complexity is polynomial in the size of the graph and the number of pending requests. We introduce a number of variants to GCR, including one that that provides the capability to switch between different paths during a connection. We present practical methods for minimizing or limiting the number of path switches. Through extensive simulations, we evaluate the performance of GCR and its variants under various topological settings and applications workload. Our results show that, for certain traffic parameters, optimized path selection combined with path switching can reduce the average delay of requests by an order of magnitude and increase the saturation throughput by as much as 50%. I.