Günter Haring

Congestion Avoidance and Energy Efficient Routing Protocol for Wireless Sensor Networks with aMobile Sink

Congestion severely affects the performance of a wireless sensor network in two aspects: increased data loss and reduced lifetime. This paper addresses these problems by introducing a mobile sink based routing scheme for congestion avoidance and energy efficient routing in wireless sensor networks. The proposed scheme utilizes the sink mobility and an in-network storage model that is used to set up mini-sinks along the mobility trajectory of the sink. Mini-sinks are responsible for collecting data from the sensor nodes located in their vicinity, thus avoiding data flow to a single data collection point, e.g., a static sink that is the major cause of congestion, data loss and reduced lifetime of the sensor network. Also, in the proposed scheme data only has to travel a limited number of hops to reach the nearest mini-sink which helps to improve the energy consumption of the sensor nodes. Through simulation we show the effectiveness of the given routing scheme in terms of congestion avoidance and increased lifetime of the wireless sensor network.

PAPS: the parallel program performance prediction toolset

This paper describes the PAPS parallel program performance prediction toolset which is currently based on an initial set of Petri net driven performance prediction tools. Flexibility, extendability and automation were the general goals in the design of the toolset. Parallel systems are described by acyclic task graphs representing typical workloads of parallel programs, by processor graphs describing MIMD distributed memory multiprocessor hardware, and a mapping function. The three specifications, program, resource and mapping, are separated to be able to change them independently from each other. Methods to make this separation feasible are described. The PAPS toolset is logically divided into four layers: The specification layer, containing tools for writing scalable, mapping independent specifications, the transformation layer, consisting of tools which generate performance models, the evaluation layer, that contains performance analysis tools, and the presentation layer, which consists of tools for the visualization and depiction of performance evaluation results.

Generative networkload models for a single server environment

Any performance evaluation study requires a concise description of the workload under which the performance of the system is to be evaluated. Also, the repeatability of the experiments for different workload profiles, requires that the workload models generate the workload profiles parametrically. Such a model, should preferably be time-invariant, consistent and generative. We view the networkload as a sequence that can be generated from the rules of a Context Free Grammar (CFG). Our approach combines the established practice of viewing the workload as “consisting of a hierarchy” and the CFG description , to produce a generative networkload model. The networkload model is applied to a SingleServer-MultipleClients network by deriving the networkload model parameters from an operational SingleServer network of personal computers. The time-invariance and generative nature are verified experimentally. The usefulness of such a description of the networkload to study the resource management problems of a network, like the optimal allocation of clients to servers, is explored by using the generative model as input descriptor to a queueing network model of SingleServer network.

Mean value analysis for computer systems with variabilities in workload

When evaluating the performance of computer systems, often uncertainties or variabilities in service demands may be observed. Applying well known mean valve analysis (MVA) for single- or multiclass queueing network models of such systems is inappropriate and ineffective, because these models fail to represent variations within a class. This paper proposes to use histograms for characterizing model parameters that are associated with uncertainty or variability and presents an adaptation of the single class MVA algorithm, which traditionally accepts single (mean) values for service demands, so that one or more input parameters can be specified as a histogram. The adapted algorithm generates a histogram output for the performance measures, thus providing a more detailed information (e.g. percentile values) than the mean valves obtained from conventional MVA. The proposed technique is demonstrated on selected examples in different problem domains. It is shown, that the computational complexity is reasonable given that the number of parameters specified as histograms is not too high. Although the algorithm produces accurate results in many situations inaccuracies have been observed for certain systems. A technique called interval splitting that can be used for controlling such inaccuracies is described.

Data-driven Human Mobility Modeling: A Survey and Engineering Guidance for Mobile Networking

Over the last decades, modeling of user mobility has become increasingly important in mobile networking research and development. This has led to the adoption of modeling techniques from other disciplines such as kinetic theory or urban planning. Yet these techniques generate movement behavior that is often perceived as not “realistic” for humans or provides only a macroscopic view on mobility. More recent approaches infer mobility models from real traces provided by positioning technologies or by the marks the mobile users leave in the wireless network. However, there is no common framework for assessing and comparing mobility models. In an attempt to provide a solid foundation for realistic mobility modeling in mobile networking research, we take an engineering approach and thoroughly discuss the required steps of model creation and validation. In this context, we survey how and to what extent existing mobility modeling approaches implement the proposed steps. This also summarizes helpful information for readers who do not want to develop a new model, but rather intend to choose among existing ones.

In-Network Storage Model for Data Persistence under Congestion in Wireless Sensor Network

Congestion in wireless sensor networks leads to the degradation of communication links that result in decreased throughput and waste of energy which is one of the scarcest resources of a sensor node. Various techniques have been proposed to cope with data congestion, such as data aggregation techniques, multi hop routing techniques and flow control techniques. Although these techniques help to avoid or control congestion, they do not address the issue of data persistency effectively. This paper presents an adaptive self configuring in-network storage model for data persistency in wireless sensor networks. The model is built on a clustered sensor field where we have utilized dense node deployment in the vicinity of the routing nodes to act as data buffers during congestion periods in order to avoid data loss. We show that the in-network storage model can be used in combination with congestion avoidance/control techniques to develop data persistent congestion avoidance/control strategies

Mean value analysis for queueing network models with intervals as input parameters

Mean value analysis (MVA) is a well-known solution technique for separable closed queueing networks used in performance modeling of computer and communication systems. In many cases, like for sensitivity analysis or with inaccurate model input parameters, intervals are more appropriate as model inputs than single values. This paper presents a version of the MVA algorithm for separable closed queueing networks with one customer class consisting of load-independent queueing centers as well as delay devices, which accepts both single values and intervals as input parameters in an arbitrary combination. Monotonicity of the model outputs with respect to all input parameters is proved and these monotonicity properties are used to construct a low cost interval-version of the MVA algorithm providing exact output intervals as results. Thus, dependency problems commonly arising with the interval evaluation of arithmetic expressions are avoided without significant increase in computation costs. Additionally, asymptotic results for the performance measure intervals obtained through interval-based analysis and corresponding bottleneck analysis are presented.

Performance bounds for distributed systems with workload variabilities and uncertainties

Abstract Bounding techniques for queuing network models used to analyze the performance of parallel and distributed computer systems accept single values as model inputs. Uncertainties or variabilities in service demands may exist in many types of systems. Using models with a single aggregate mean value for each parameter for such systems can lead to inaccurate or even incorrect results. This paper proposes to use histograms for characterizing model parameters that are associated with uncertainty and/or variability. The adaptation of the well-known asymptotic bounds as well as balanced job bounds for single class queuing networks to histogram parameters is presented in the paper.

Performance assessment on ambient intelligent applications through ontologies

This paper brings together the performance assessment of ambient intelligence architectures systems with ontology engineering. Thus, firstly appropriate description methods for distributed intelligent applications are summarized. Derived from the system characterization, typical software performance engineering techniques are based on the augmented description of the model regarding performance annotations. However, these annotations are only related with the syntactical view of the software architecture. In the next generation of performance assessment tools for ambient intelligent systems, the description of the system would be capable of reasoning and acquiring knowledge about performance. Having an appropriate architectural description including performance aspects, any possible design options for intelligent distributed applications can be evaluated according to their performance impact. Therefore, we propose the use of an ontology with performance-related information not only to possible evaluate the architecture through the common off-line procedure but also the first step to build a broker that assesses the performance of the system during its execution.

Parallel programming with CAPSE-a case study

The CAPSE environment for Computer Aided Parallel Software Engineering is intended to assist the developer in the crucial task of parallel programming. The methodology of CAPSE is based on direct manipulative graphical creation and editing of scalable workload characterizations of MIMD algorithms. This paper presents the basic concepts of this methodology and an example of a parallel Poisson solver. The workload characterization representing the computation and communication behavior of the algorithm is based on directed acyclic task graphs, which achieve scalability by composing the task graph of scalable basic patterns instead of single node and arcs. The composition and the usage of these basic patterns is described in the light of designing the Poisson solver algorithm. The resulting task graph is used to predict the programs performance on a nCUBE 2 distributed memory machine and the PAPS simulator.