Srivatsan Varadarajan

Resource Management for Ad-Hoc Wireless Networks with Cluster Organization

Boosted by technology advancements, government and commercial interest, ad-hoc wireless networks are emerging as a serious platform for distributed mission-critical applications. Guaranteeing QoS in this environment is a hard problem because several applications may share the same resources in the network, and mobile ad-hoc wireless networks (MANETs) typically exhibit high variability in network topology and communication quality. In this paper we introduce DYNAMIQUE, a resource management infrastructure for MANETs. We present a resource model for multi-application admission control that optimizes the application admission utility, defined as a combination of the QoS satisfaction ratio. A method based on external adaptation (shrinking QoS for existing applications and later QoS expansion) is introduced as a way to reduce computation complexity by reducing the search space. We designed an application admission protocol that uses a greedy heuristic to improve application utility. For this, the admission control considers network topology information from the routing layer. Specifically, the admission protocol takes benefit from a cluster network organization, as defined by ad-hoc routing protocols such as CBRP and LANMAR. Information on cluster membership and cluster head elections allows the admission protocol to minimize control signaling and to improve application quality by localizing task mapping.

TTEthernet Dataflow Concept

TTEthernet is a novel communication infrastructures that allows using a single physical communication infrastructure for distributed applications with mixed-criticality requirements, e.g. the command and control systems and audio/video systems. This is achieved via a fault-tolerant self-stabilizing synchronization strategy, which establishes temporal partitioning and, hence, ensures isolation of the critical dataflows from the non-critical dataflows.The focus in this paper is on the dataflow in TTEthernet. For this we take the synchronization as a given and discuss from a TTEthernet user perspective which communication options TTEthernet provides and how they are aligned and realized. While this paper uses TTEthernet as reference communication infrastructure, the methods and strategies presented are valid for any message-based network.

Automatic Learning-based MANET Cross-Layer Parameter Configuration

Mobile ad hoc networks (MANETs) operate in highly dynamic environments with limited resources. Current approaches to network configuration are static and ad-hoc, and therefore frequently perform extremely poorly. We describe our approach to network configuration control that relies on automatically learning the relationships among configuration parameters and maintains near-optimal configurations adaptively, even during highly dynamic missions. We present a case study demonstrating the feasibility of the approach.

Maximizing fault tolerance in a low-s WaP data network

The BRAIN (Braided Ring Availability/Integrity Network) is a radically different type of data network technology that uses a combination of a braided ring topology and high-integrity message propagation mechanisms. The BRAIN was originally designed to tolerate two passive failures or one passive and one active failure (including a Byzantine failure). In recent developments, the BRAINs fault tolerance has been increased to the level where it can tolerate two active failures (including two Byzantine failures), as long as the two failures are not colluding. A colluding failure is an active failure that supports one or more other active failures to cause a system failure. To be effective, these active failures must be syntactically correct - i.e., cannot be detected by inline error detection, such as CRCs, checksums, physical encoding (e.g. 8B/10B), protocol rules, or reasonableness checks. The probability of colluding failures happening is so low that this new BRAIN, for all practical purposes, is a two-fault tolerant network. This improvement in fault tolerance comes at no additional cost. That is, it uses exactly the same minimal amount of hardware as the original BRAIN. As an example comparison, this new version of the BRAIN requires less size, weight, and power (SWaP) than a typical two-channel AFDX network, while tolerating more faults and more types of faults. The nodes used by the BRAIN, are simplex (they require no redundancy in themselves for integrity) and the fault tolerance provided by the BRAIN can be made transparent to all application software. The BRAIN can check that redundant nodes (e.g. pair-wise adjacent nodes) produce bit-for-bit identical outputs, without resorting to clock-step self-checking pair processing that is rapidly becoming technologically infeasible due to the higher speeds of modern processors. The BRAIN also simplifies the creation of architectures with dissimilar redundancy. The design of these BRAIN improvements were guided by the use of the Symbolic Analysis Laboratory (SAL) model-checker in a novel use of formal methods for exploratory development early in the design cycle of a new protocol.