2459398
On increasing end-to-end throughput in wireless ad hoc networks
One of the main characteristics of wireless ad hoc networks is their node-centric broadcast nature of communication, leading to interferences and spatial contention between adjacent wireless links. Due to such interferences, pessimistic concerns have been recently raised with respect to the decreasing network capacity in wireless ad hoc networks when the number of nodes scales to several orders of magnitude higher. In this paper, we argue that in all cases of end-to-end data communications - including one-to-k unicast and multicast data dissemination as well as k-to-one data aggregation - the maximum achievable end-to-end data throughput (measured on the sources) heavily depends on the strategy of arranging the topology of transmission between sources and destinations, as well as possible per-node operations such as coding. An optimal strategy achieves better end-to-end throughput than an arbitrary one. We present theoretical studies and critical insights with respect to how these strategies may be designed so that end-to-end throughput may be increased
5878041
sFlow: towards resource-efficient and agile service federation in service overlay networks
Existing research work towards the composition of complex federated services has assumed that service requests and deliveries flow through a particular service path or tree. Here, we extend such a service model to a directed acyclic graph, allowing services to be delivered via parallel paths and interleaved with each other. Such an assumption of the service flow model has apparently introduced complexities towards the development of a distributed algorithm to federate existing services, as well as the provisioning of the required quality in the most resource-efficient fashion. To this end, we propose sFlow, a fully distributed algorithm to be executed on all service nodes, such that the federated service flow graph is resource efficient, performs well, and meets the demands of service consumers.
6792202
Scalable streaming for heterogeneous clients
Periodic broadcast protocols enable the efficient streaming of highly popular media files to large numbers of concurrent clients. Most previous periodic broadcast protocols, however, assume that all clients can receive at the same rate, and also assume that available bandwidth is not time-varying. In this paper, we first develop a new periodic broadcast protocol, Optimized Heterogeneous Periodic Broadcast (OHPB), that can be optimized for a given population of clients with heterogeneous reception bandwidths and quality-of-service requirements. The OHPB protocol utilizes an optimized segment size progression determined by solving a linear optimization model that takes as input the client population characteristics and an objective function such as mean client startup delay. We then propose complementary client protocols employing work-ahead buffering of data during playback, so as to enable more uniform playback quality when the available bandwidth is time-varying.
15951062
Probabilistic Power Management for Wireless Ad Hoc Networks
Extending system lifetime by effectively managing power on participating nodes is critical in wireless ad hoc networks. Recent work has shown that, by appropriately powering off nodes, energy may be significantly saved up to a factor of two, especially when node density is high. Such approaches rely on the selection of a virtual backbone (i.e., a connected dominating set) of the topology to forward ongoing traffic, coupled with algorithms to manually and periodically recompute such a backbone for load balancing purposes. The common drawback of such schemes is the need to involve periodic message exchanges and to make additional restrictive assumptions. This paper presents Odds1, an integrated set of energy-efficient and fully distributed algorithms for power management in wireless ad hoc networks. Odds build on the observation that explicit and periodic re-computation of the backbone topology is costly with respect to its additional bandwidth overhead, especially when nodes are densely populated or highly mobile. Building on a fully probabilistic approach, Odds seek to make a minimum overhead, perfectly balanced, and fully localized decision on each node with respect to when and how long it needs to enter standby mode to conserve energy. Such a decision does not rely on periodic message broadcasts in the local neighborhood, so that Odds are scalable as node density increases. Detailed mathematical analysis, discussions and simulation results have shown that Odds are indeed able to achieve our objectives while operating in a wide range of density and traffic loads.
9219226
iFlow: Middleware-assisted Rendezvous-based Information Access for Mobile Ad Hoc Applications
Due to node mobility and limitations on bandwidth availability in wireless channels, there exist unique challenges towards achieving efficient and effective information access in wireless ad hoc networks with mobile nodes. In this paper, we address two critical questions: (1) How may information be accessed with the highest degree of bandwidth efficiency? and (2) How should algorithms be designed so that node mobility contributes positively towards high performance and efficiency? We present iFlow, a middleware-based framework for bandwidth-efficient and delay-aware information access for mobile ad hoc applications. We present the case of information rendezvous, where the demands for information are satisfied by the supplies in a fully distributed fashion, across third-party nodes beyond information suppliers and consumers. Such rendezvous is achieved via controlled diffusion of information from the suppliers, matched by the gleaning process on the consumers. We validate our claims using simulation and experimental results.
1327847
On achieving maximum multicast throughput in undirected networks
The transmission of information within a data network is constrained by the network topology and link capacities. In this paper, we study the fundamental upper bound of information dissemination rates with these constraints in undirected networks, given the unique replicable and encodable properties of information flows. Based on recent advances in network coding and classical modeling techniques in flow networks, we provide a natural linear programming formulation of the maximum multicast rate problem. By applying Lagrangian relaxation on the primal and the dual linear programs (LPs), respectively, we derive a) a necessary and sufficient condition characterizing multicast rate feasibility, and b) an efficient and distributed subgradient algorithm for computing the maximum multicast rate. We also extend our discussions to multiple communication sessions, as well as to overlay and ad hoc network models. Both our theoretical and simulation results conclude that, network coding may not be instrumental to achieve better maximum multicast rates in most cases; rather, it facilitates the design of significantly more efficient algorithms to achieve such optimality.
12116647
Data locality enhancement by memory reduction
In this paper, we propose memory reduction as a new approach to data locality enhancement. Under this approach, we use the compiler to reduce the size of the data repeatedly referenced in a collection of nested loops. Between their reuses, the data will more likely remain in higher-speed memory devices, such as the cache. Specifically, we present an optimal algorithm to combine loop shifting, loop fusion and array contraction to reduce the temporary array storage required to execute a collection of loops. When applied to 20 benchmark programs, our technique reduces the memory requirement, counting both the data and the code, by 51% on average. The transformed programs gain a speedup of 1.40 on average, due to the reduced footprint and, consequently, the improved data locality.
10771836
Detection and repair of software errors in hierarchical sensor networks
Sensor networks are being increasingly deployed for collecting critical data in various applications. Once deployed, a sensor network may experience faults at the individual node level or at an aggregate network level due to design errors in the protocol, implementation errors, or deployment conditions that are significantly different from the target environment. In many applications, the deployed system may fail to collect data in an accurate, complete, and timely manner due to such errors. If the network produces incorrect data, the resulting decisions on the data may be incorrect, and negatively impact the application. Hence, it is important to detect and diagnose these faults through run-time observation. Existing technologies face difficulty with wireless sensor networks due to the large scale of the networks, the resource constraints of bandwidth and energy on the sensing nodes, and the unreliability of the observation channels for recording the behavior. This paper presents a semi-automatic approach named H-SEND (hierarchical sensor network debugging) to observe the health of a sensor network and to remotely repair errors by reprogramming through the wireless network. In H-SEND, a programmer specifies correctness properties of the protocol ("invariants"). These invariants are associated with conditions (the "observed variables") of individual nodes or the network. The compiler automatically inserts checking code to ensure that the observed variables satisfy the invariants. The checking can be done locally or remotely, depending on the nature of the invariant. In the latter case, messages are generated automatically. If an error is detected at run-time, the logs of the observed variables are examined to analyze and correct the error. After errors are corrected, new programs or patches can be uploaded to the nodes through the wireless network. We construct a prototype to demonstrate the benefit of run-time detection and correction
11024266
Energy-Aware Scheduling for Real-Time Multiprocessor Systems with Uncertain Task Execution Time
This paper presents an energy-aware method to schedule multiple real-time tasks in multiprocessor systems that support dynamic voltage scaling (DVS). The key difference from existing approaches is that we consider the probabilistic distributions of the tasks' execution time to partition the workload for better energy reduction. We analyze the problem of energy-aware scheduling for multiprocessor with probabilistic workload information and derive its mathematical formulation. As the problem is NP-hard, we present a polynomial-time heuristic method to transform the problem into a probability-based load balancing problem that is then solved with worst-fit decreasing bin-packing heuristic. Simulation results with synthetic, multimedia, and stereo- vision tasks show that our method saves significantly more energy than existing methods.
15936335
A divide-and-conquer 2.5-D floorplanning algorithm based on statistical wirelength estimation
An efficient and effective divide-and conquer 2.5D floorplanning algorithm is proposed for wirelength optimization. Modules are pre-partitioned into different dies with respect to the statistical wirelength estimation result. Then a floorplan is generated on each die for wirelength optimization. The new partitioning method successfully solves the conflict between wirelength minimization and inter-die via constraints. Experimental results show that our algorithm could provide noticeable improvement in the total wirelength compared to both 2D design and the previous 2.5D floorplanning algorithm.
13162337
A circuit level fault model for resistive opens and bridges
Delay faults are an increasingly important test challenge. Traditional open and bridge fault models are incomplete because only the functional fault or a subset of delay fault are modeled. In this paper, we propose a circuit level model for resistive open and bridge faults. All possible fault behaviors are illustrated and a general resistive bridge delay calculation method is proposed. The new models are practical and easy to use. Fault simulation results show that the new models help the delay test to catch more bridge faults.