Adaptive transaction routing in a heterogeneous database environment

A new deadlock prevention scheme for nonminimal adaptive wormhole routing

The adaptive routing approach has been expected as a promising way to improve network performance by utilizing available network bandwidth. Previous adaptive routing strategies in wormhole-routed multicomputer networks restrict the routing of messages by the routing algorithm to prevent deadlock. This results in low degree of adaptivity and low utilization of physical or virtual channels. In this paper, we examine the possibility of performing restriction-free adaptive routing in wormhole-routed networks as an approach to further improving the performance of these networks. A new flow control policy, called message cutting-in, is proposed, and two adaptive routing strategies are presented. Freedom of communication deadlock is achieved by the proposed flow control policy. The proposed adaptive routing strategies do not restrict routing and maximally utilize the physical and virtual channels. Simulation results show that the restriction-free adaptive routing approach is promising from the fact that it has the lowest latency and highest throughput depending on the number of virtual channels per physical channel and patterns of message traffic.

Adaptive Multicast Wormhole Routing in 2D Mesh Multicomputers

Digital microfluidic biochips (DMFBs) based on a micro-electrode-dot-array (MEDA) architecture provide fine-grained control and sensing of droplets in real-time. However, excessive actuation of microelectrodes in MEDA biochips can lead to charge trapping during bioassay execution, causing the failure of microelectrodes and erroneous bioassay outcomes. A recently proposed enhancement to MEDA allows run-time measurement of microelectrode health information, thereby enabling synthesis of adaptive routing strategies for droplets. However, existing synthesis solutions are computationally infeasible for large MEDA biochips that have been commercialized. In this paper, we propose a synthesis framework for adaptive droplet routing in MEDA biochips via deep reinforcement learning (DRL). The framework utilizes the real-time microelectrode health feedback to synthesize droplet routes that proactively minimize the likelihood of charge trapping. We show how the adaptive routing strategies can be synthesized using DRL. We implement the DRL agent, the MEDA simulation environment, and the bioassay scheduler using the OpenAI Gym environment. Our framework obtains adaptive routing policies efficiently for COVID-19 testing protocols on large arrays that reflect the sizes of commercial MEDA biochips available in the marketplace, significantly increasing probabilities of successful bioassay completion compared to existing methods.

The performance of interconnection networks can be improved drastically by using wormhole routing and virtual channel flow control. Adaptive routing strategies, which are superior to oblivious routing, supply a set of alternative output virtual channels associated with different physical links, when a message is routed. A selection function selects a free one from the set. In this paper, we have evaluated the performance of three adaptive wormhole routing algorithms with minimum paths in 3D mesh networks: Dynamic Routing proposed by Dally and Aoki, Negative Last routing developed by Glass and Ni based on their Turn Model and Full Dynamic Routing proposed by us based on Duato’s adaptive routing theory. Three different selection functions, namely Static, Random and Greedy have been compared.

Energy is scarce in mobile computing devices including wearable and implantable devices in a wireless body area network. In this paper, an adaptive routing protocol is developed and analyzed which minimizes the energy cost per bit of information by using the channel information to choose the best strategy to route data. In this approach, the source node will switch between direct and relayed communication based on the quality of the link and will use the relay only if the channel quality is below a certain threshold. The mathematical model is then validated through simulations which shows that the adaptive routing strategy can improve energy efficiency significantly compared with existing methods.

A communication structure for a binary n-cube is proposed, which consists of hierarchically ordered virtual networks corresponding to the cube. It is shown that the structure is deadlock-free and can support adaptive and fault-tolerant routing strategies. The software as well as the hardware support required for implementing wormhole routing are discussed. The applicability of the method for networks like k-ary n-cubes is also considered.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Mobile security is one of the most fundamental problems in Wireless Sensor Networks (WSNs). The data transmission path will be compromised for some disabled nodes. To construct a secure and reliable network, designing an adaptive route strategy which optimizes energy consumption and network lifetime of the aggregation cost is of great importance. In this paper, we address the reliable data aggregation route problem for WSNs. Firstly, to ensure nodes work properly, we propose a data aggregation route algorithm which improves the energy efficiency in the WSN. The construction process achieved through discrete particle swarm optimization (DPSO) saves node energy costs. Then, to balance the network load and establish a reliable network, an adaptive route algorithm with the minimal energy and the maximum lifetime is proposed. Since it is a non-linear constrained multi-objective optimization problem, in this paper we propose a DPSO with the multi-objective fitness function combined with the phenotype sharing function and penalty function to find available routes. Experimental results show that compared with other tree routing algorithms our algorithm can effectively reduce energy consumption and trade off energy consumption and network lifetime.

In this paper, we present and evaluate a novel routing scheme called DyAD which combines the advantages of both deterministic and adaptive routing schemes. More precisely, we envision a new routing technique which judiciously switches between deterministic and adaptive routing based on the network's congestion conditions. The simulation results show the effectiveness of DyAD by comparing it with purely deterministic and adaptive routing schemes under different traffic patterns. Moreover, a prototype router based on the DyAD idea has been designed and evaluated. Compared to purely adaptive routers, the overhead of implementing DyAD is negligible (less than 7%), while the performance is consistently better.

A multi-dual ring connected shuffle network is an optical multichannel multihop architecture proposed for wide area networks (WAN). With a simple fixed routing algorithm, this network architecture can achieve better performance than the Perfect ShuffleNet. In this paper, we propose a simple adaptive routing scheme which can achieve an even better performance. The adaptive routing scheme can quickly disperse packets away from congested portions of the network. Unlike centralized routing algorithms, the distributed routing algorithm uses only local state information and does not require a priori knowledge of the traffic patterns. Also, in contrast with some adaptive routing algorithms proposed for the Perfect ShuffleNet, the routing algorithm can distribute some of the traffic over less busy channels without increasing the length of the path. Moreover, in the case of network link failure, the adaptive routing scheme can direct the traffic around the 'trouble' area, which makes the network survivable. Since the whole idea of the scheme is to distribute traffic evenly among all the channels as much as possible, it can reduce the maximum traffic intensity on each channel, thus decreasing the size of pre-allocated buffer. All these characters make it very suitable for optical network architectures. The deloading factor is used in assessing the performance. Static simulations are performed under the worst condition and the most likely scenarios. The results support the preceding statements. The ideas presented here may be used for other optical WAN architectures, as long as each metropolitan area network is multiconnected in a ring topology.© (1996) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

What kind of metadata can be used for expressing the multiplicity of data models and the data transformation and integration processes in data warehousing environments? How can this metadata be further used for supporting other data warehouse activities? We examine how these questions are addressed by AutoMed, a system for expressing data transformation and integration processes in heterogeneous database environments.

A distributed adaptive opportunistic routing scheme for multihop wireless ad hoc networks is proposed. The proposed scheme utilizes a reinforcement learning framework to opportunistically route the packets even in the absence of reliable knowledge about channel statistics and network model. This scheme is shown to be optimal with respect to an expected average per-packet reward criterion. The proposed routing scheme jointly addresses the issues of learning and routing in an opportunistic context, where the network structure is characterized by the transmission success probabilities. In particular, this learning framework leads to a stochastic routing scheme that optimally explores and exploits the opportunities in the network.

Software-Defined Vehicular Networks (SDVNs) technology has been attracting significant attention as it can make Vehicular Ad Hoc Network (VANET) more efficient and intelligent. SDVN provides a flexible architecture which can decouple the network management from data transmission. Compared to centralized SDVN, hybrid SDVN is even more flexible and has less overhead. This hybrid technology can eliminate the burden on the central controller by moving regional routing tasks from the central controller to local controllers or vehicular nodes. In the literature, different routing protocols have been reported for SDVNs. However, these existing routing protocols lack flexibility and adaptive approaches to deal with changing and dynamic traffic conditions. Thus, this paper proposes a new software-defined routing method, namely, Novel Adaptive Routing and Switching Scheme (NARSS), deployed in the controller. This adaptive method can dynamically select routing schemes for a specific traffic scenario. To achieve this, this paper firstly presents a method for collecting road network information to describe traffic condition where the method extracts the feature data used to generate the routing scheme switching model. Secondly, we train the feature data through an artificial neural network with high training speed and accuracy. Finally, we use the model as a basis for establishing the NARSS and deploy it in the controller. Simulation results show that the proposed scheme outperforms the single traditional routing protocol in terms of both packet delivery ratio and end-to-end delay.

A general framework for efficient geographic routing in wireless networks

Reducing the shipping sector's contribution to climate change requires urgent emission reductions this decade. Both weather routing and wind propulsion offer immediate solutions, where combining sails with efficient routing amplifies the performance of each technology. However, while large emission savings are theoretically available, the impact of stochastic uncertainty from wind forecasts is unknown. Here, we present a novel approach that exploits fuel consumption calculations from over a thousand departures across three routes to characterise stochastic uncertainty. We show that routes with ideal wind conditions and long voyage times are most sensitive to uncertain forecast inputs, reducing savings from Flettner rotors and weather routing by up to 44% when a priori weather routing strategies are used. This paper further shows how an adaptive weather routing strategy can be used as an accurate prediction tool to reduce uncertainty on all routes investigated, reliably amplifying carbon savings from Flettner rotors by between 1.16 and 2.48 times typical great circle route savings. Overall, this paper provides greater assurance around the previously estimated carbon savings that serves to strengthen confidence in a wind-assisted decarbonisation strategy and its potential to provide essential emission reductions this decade.