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Abstract interpretation: A semanticsbased tool for program analysis
 In Handbook of Logic in Computer Science
, 1995
"... 1.2 Relation to Program Verification and Transformation.... 9 ..."
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Cited by 94 (14 self)
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1.2 Relation to Program Verification and Transformation.... 9
Report from Dagstuhl Seminar 15112 Network Calculus Edited by
"... This report documents the program and the outcomes of Dagstuhl Seminar 15112 “Network Calculus”. At the seminar, about 30 invited researchers from academia and industry discussed the promises, approaches, and open challenges of the Network Calculus. This report gives a general overview of the presen ..."
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This report documents the program and the outcomes of Dagstuhl Seminar 15112 “Network Calculus”. At the seminar, about 30 invited researchers from academia and industry discussed the promises, approaches, and open challenges of the Network Calculus. This report gives a general overview of the presentations and outcomes of discussions of the seminar.
Pay Bursts Only Once Holds for (Some) NonFIFO Systems
 In IEEE INFOCOM
, 2011
"... Abstract—NonFIFO processing of flows by network nodes is not a rare phenomenon. Unfortunately, the stateoftheart analytical tool for the computation of performance bounds in packetswitched networks, network calculus, cannot deal well with nonFIFO systems. The problem lies in its conventional s ..."
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Abstract—NonFIFO processing of flows by network nodes is not a rare phenomenon. Unfortunately, the stateoftheart analytical tool for the computation of performance bounds in packetswitched networks, network calculus, cannot deal well with nonFIFO systems. The problem lies in its conventional service curve definitions. Either the definition is too strict to allow for a concatenation and consequent beneficial endtoend analysis, or it is too loose and results in infinite delay bounds. Hence, in this paper, we propose a new service curve definition and demonstrate its strength with respect to achieving both finite delay bounds and a concatenation of systems resulting in a favorable endtoend delay analysis. In particular, we show that the celebrated pay bursts only once phenomenon is retained even without any assumptions on the processing order of packets. This seems to contradict previous work [15]; the reasons for this are discussed.
Experimental Design and Analysis of Transmission Properties in an Indoor Wireless Sensor Network
, 2010
"... Abstract—In this paper, we systematically investigate different factors and their effects on the wireless transmission properties using a fullfactorial experimental design of a wireless sensor network in a realworld indoor environment. We quantify the impact of primary factors such as the wireless ..."
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Abstract—In this paper, we systematically investigate different factors and their effects on the wireless transmission properties using a fullfactorial experimental design of a wireless sensor network in a realworld indoor environment. We quantify the impact of primary factors such as the wireless channel, physical position, transmission power, and their interactions on the received signal strength (RSS). While some of our results support conventional assumptions, this study also shows that there are many properties which are in contrast to existing findings. For example, there is no significant correlation in the measured RSS between different but equallydistant transmitters, yet the correlation coefficient between two transmitters is above 94%. In addition, changing the wireless channel even in a static network scenario results in highly unpredictable interaction with other factors and significantly influences the measured RSS. Since the analyzed network only consists of lowcost, commodity transmitters, the results of this experimental analysis can serve as valuable insights in planning and deploying wireless sensor networks in different application scenarios. I.
Calculating Accurate EndtoEnd Delay Bounds – You Better Know Your CrossTraffic
"... Bounds on the endtoend delay of data flows play a crucial role in di↵erent areas, ranging from certification of hard realtime communication capabilities to quality of experience assurance for end users. Deterministic Network Calculus (DNC) allows to derive worstcase delay bounds; for instanc ..."
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Bounds on the endtoend delay of data flows play a crucial role in di↵erent areas, ranging from certification of hard realtime communication capabilities to quality of experience assurance for end users. Deterministic Network Calculus (DNC) allows to derive worstcase delay bounds; for instance, DNC is applied by the avionics industry to formally verify aircraft networks against strict delay requirements. Calculating tight endtoend delays, however, was proven to be NPhard. As a result, analyses focus on deriving fairly accurate bounds with feasible e↵ort. Previous work constantly improved on capturing flow scheduling and crosstrac multiplexing e↵ects on the analyzed flow’s path. In contrast, we present an enhanced analysis of the crosstrac itself to decrease the bound on its worstcase data arrivals that interfere with the analyzed flow. This improvement is beneficial for both of e↵ects, scheduling and multiplexing. By replacing the currently used procedure to bound crosstrac arrivals with our new method, we can improve network calculus accuracy considerably – we demonstrate improvements that reduce the worstcase delay bound by more than factor 6.
On the Potential to Improve Accuracy of Network Calculus Analyses
"... Abstract The continuous evolution of network calculus led to a set of different analyses. Among them, there is a single analysis that can derive tights bounds in arbitrary feedforward server graphs. However, the approach it takes results in the analysis being NPhard and no efficient analysis algo ..."
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Abstract The continuous evolution of network calculus led to a set of different analyses. Among them, there is a single analysis that can derive tights bounds in arbitrary feedforward server graphs. However, the approach it takes results in the analysis being NPhard and no efficient analysis algorithm is known. Therefore, the authors propose to confine to a less complex analysis based on their approach instead. Like previous network calculus analyses, it derives tight bounds for some networks and valid bounds with varying accuracy for any other network. In this paper, we examine the work on accurate network calculus analyses regarding their relative accuracy and the potential to improve these analyses. 1 Introduction to Network Calculus We start with an indepth introduction to network calculus that allows to derive the delay bounds used for accuracy evaluation. 1.1 The System Description Network calculus was built around a simple system description [17] consisting of
Boosting Sensor Network Calculus by Thoroughly Bounding CrossTraffic
"... framework for worstcase analysis of wireless sensor networks. The analysis proceeds in two steps: For a given flow, (1) the network is reduced to a tandem of nodes by computing the arrival bounds of crosstraffic; (2) the flow is separated from the crosstraffic by subtracting crossflows and conca ..."
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framework for worstcase analysis of wireless sensor networks. The analysis proceeds in two steps: For a given flow, (1) the network is reduced to a tandem of nodes by computing the arrival bounds of crosstraffic; (2) the flow is separated from the crosstraffic by subtracting crossflows and concatenating nodes on its path. While the second step has seen much treatment, the first step has not at all. This is in sharp contrast to the fact that arrival bounding takes roughly 80 % of the total analysis time and is equally crucial for the tightness of the bounds. Therefore, we turn our attention to this first SensorNC analysis step with the goal to boost the performance and applicability of the overall framework. The main technical contribution is a generalized version of the concatenation theorem within the SensorNC setting. This generalization is instrumental in simplifying and streamlining the crosstraffic arrival bound computations such that run times can be reduced by more than a factor of 5. Even more important, it enables a localization of the information necessary to execute the calculations at the node level, thus enabling a distribution of the SensorNC analysis within a selfmodeling WSN. I.
A Three Syntactic Theories for Combinatory Graph Reduction
"... We present a purely syntactic theory of graph reduction for the canonical combinators S, K, and I, where graph vertices are represented with evaluation contexts and let expressions. We express this first syntactic theory as a storeless reduction semantics of combinatory terms. We then factor out the ..."
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We present a purely syntactic theory of graph reduction for the canonical combinators S, K, and I, where graph vertices are represented with evaluation contexts and let expressions. We express this first syntactic theory as a storeless reduction semantics of combinatory terms. We then factor out the introduction of let expressions to denote as many graph vertices as possible upfront instead of on demand. The factored terms can be interpreted as term graphs in the sense of Barendregt et al. We express this second syntactic theory, which we prove equivalent to the first, as a storeless reduction semantics of combinatory term graphs. We then recast let bindings as bindings in a global store, thus shifting, in Strachey’s words, from denotable entities to storable entities. The storebased terms can still be interpreted as term graphs. We express this third syntactic theory, which we prove equivalent to the second, as a storebased reduction semantics of machine. The architecture of this storebased abstract machine coincides with that of Turner’s original reduction machine. The three syntactic theories presented here therefore properly account for combinatory graph reduction As We Know It. These three syntactic theories scale to handling the Y combinator. This article therefore illustrates the scientific consensus of theoreticians and implementors about graph reduction: it is the same combinatory
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