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Heckmüller, Stephan; Wolfinger, Bernd E.
doi: 10.1177/0037549709341071pmid: N/A
In this paper the innovative approach of load transformation is used to derive valid load models for secondary loads in computer networks, as they can be observed at lower-layer interfaces of protocol stacks. We apply the technique of load transformation onto a load represented by batch Markovian arrival process (BMAP) models and choose as concrete load transformation the practically relevant token bucket mechanism, which is widely used in current networks. We are able to determine closed-form analytical solutions to describe the transformed load. Investigations by means of simulations of the quality of our analytical models reflecting token-bucket-based load transformations underline the high level of accuracy and validity of the predicted secondary load. We also present a tool implementing different transformation functionalities which can be used for the specification of arrival processes for simulative and analytic performance evaluation.
Haggarty, Oliver J.; Knottenbelt, William J.; Bradley, Jeremy T.
doi: 10.1177/0037549709340785pmid: N/A
Generalized stochastic Petri nets (GSPNs) are widely used in the performance analysis of computer and communications systems. Response time densities and quantiles are often key outputs of such analysis. These can be extracted from a GSPN’s underlying semi-Markov process using a method based on numerical Laplace transform inversion. This method typically requires the solution of thousands of systems of complex linear equations, each of rank n, where n is the number of states in the model. For large models substantial processing power is needed and the computation must therefore be distributed. In this paper we describe the implementation of a response time analysis module for the Platform Independent Petri net Editor (PIPE2) which interfaces with Hadoop, an open-source implementation of Google’s MapReduce distributed programming environment, to provide distributed calculation of response time densities in GSPN models. The software is validated with results calculated analytically as well as simulated results for larger models. Excellent scalability is shown.
Rahman, Ashikur; Ahmed, Mahmuda; Zerin, Shobnom
doi: 10.1177/0037549709340731pmid: N/A
We consider ad-hoc wireless networks and the topology control problem defined as minimizing the amount of power needed to maintain connectivity. The issue boils down to selecting the optimum transmission power level at each node based on the position information of reachable nodes. Local decisions regarding the transmission power level induce a subgraph of the maximum powered graph Gmax in which edges represent direct reachability at maximum power. In this paper we propose an analysis for constructing minimum-energy path-preserving subgraphs of Gmax, i.e. subgraphs minimizing the energy consumption between node pairs. We also propose an algorithm for constructing subgraph of Gmax based on one-hop neighbor information. By presenting experimental results we show the effectiveness of our proposed algorithm.
Stavrinides, Georgios L.; Karatza, Helen D.
doi: 10.1177/0037549709340729pmid: N/A
Distributed real-time systems play an increasingly vital role in our society. The most important aspect of such systems is the scheduling algorithm, which must guarantee that every job in the system will meet its deadline, providing high-quality (precise) results. In this paper we evaluate by simulation the performance of strategies for the scheduling of parallel jobs (gangs) in a homogeneous distributed real-time system with possible software faults. For each scheduling policy we provide an alternative version which allows imprecise computations. We propose a performance metric applicable to our problem, which takes into account the number of jobs guaranteed, as well as the precision of the results of each guaranteed job. The simulation results show that the alternative versions of the algorithms outperform their respective counterparts. To the best of our knowledge, a real-time gang scheduling approach that utilizes imprecise computations has never been discussed in the literature before.
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