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the node ni V, Vcur to be scheduled next on processor procS (ni ) = P, it holds / that tdr,Sinsert,cur (ni , P) tdr,Send,cur (ni , P) tdr,S (ni , P), with the above argumentation With the end technique, ni is scheduled at the end of the last node already scheduled on P But this cannot be later than in S, because the nodes on P in Send,cur are the same nodes that are executed before ni on P in S, due to the schedule order of the nodes, and, according to the assumption, no node starts later than in S With the insertion technique, ni is scheduled in the worst case (ie, there is no idle period between two nodes already scheduled on P complying with Eq (61)) also at the end of the last node already scheduled on P Thus, ts,Sinsert,cur (ni , P) ts,Send,cur (ni , P) ts,S (ni , P) for node ni By induction this is true for all nodes of the schedule, in particular, the last node, which proves the theorem Theorem 51 of Section 51 establishes that an optimal schedule is de ned by the processor allocation and the nodes execution order List scheduling with the end technique can construct an optimal schedule from these inputs While this result is included in Theorem 61, it also establishes that the insertion technique might improve a given nonoptimal schedule Rescheduling a given schedule with list scheduling and the insertion technique, using the processor allocation and the node order of the original schedule, might improve the schedule length In particular, schedules produced with the end technique might be improved What rst sounds like a contradiction to Theorem 51, after all it states that the end technique is optimal given a processor allocation and the nodes execution order, becomes clear when one realizes that rescheduling with the insertion technique can only reduce the length of a schedule by reordering the nodes Inserting a node in an earlier slot changes the node order on the corresponding processor Complexity Regarding the complexity of list scheduling with the insertion technique, the second part of the corresponding Algorithm 9 is examined Determining the data ready time remains O(PE) for all nodes on all processors (see Section 51) What changes is the time complexity of the start time calculation In the worst case, it must be checked for every consecutive node pair on a processor, if the time period between them is large enough to accommodate the node to be scheduled At the time a node is scheduled, there are at most O(V) nodes scheduled on all processors So if the start time is determined for every processor as in the typical case of start time minimization this amortizes to O(max(V, P)), which is of course O(V), because it is meaningless to schedule on more processors than the task graph has nodes The start time is calculated for every node; thus, the nal complexity of the second part of simple list scheduling with start time minimization is O(V2 + PE) For comparison, with the end technique it is O( P(V + E)) (see Section 51) The insertion technique is employed n various scheduling heuristics, for example, ISH (insertion scheduling heuristic) by Kruatrachue [105], (E)CPFD ((economical) critical path fast duplication) by Ahmad and Kwok [4], BSA (bubble scheduling and allocation) by Kwok and Ahmad [114], and MD (mobility directed) by Wu and Gajski [207].

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This chapter describes two statistical anomaly detection techniques for cyber attack detection: the EWMA (Exponentially Weighted Moving Average) control chart which is a univariate Statistical Process Control (SPC) technique, and the Chi-Square Distance Monitoring method (CSDM) which is a multivariate SPC technique Many SPC techniques [1 2] have traditionally been developed and applied to monitor the quality of manufacturing processes SPC techniques rst build the statistical model of the process data obtained from an in-control process to contain only random variations of the process data SPC considers the process out of control with an assignable cause other than random causes of data variations if the process data shows a statistically signi cant deviation from the statistical in-control data model Consider that a computer and network system is in control if there are only normal use activities, but is out-of-control if there are also attack activities This makes the anomaly detection methodology for cyber attack detection similar to SPC techniques [3] in rst building the norm or in-control pro le of the process data and then using the norm pro le to detect a large deviation as anomaly or out-ofcontrol caused by an attack This chapter presents the application of a univariate SPC technique, the EWMA control chart, and a multivariate SPC technique, CSDM, to yber attack detection. Paint EAN 13 In VS .NET Using Barcode creator . 5 In .NET Framework Using Barcode encoder for ASP .Related: Print QR Code .NET , Printing Code 39 .NET , Code 128 Printing .NET

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of 100 events. Drawing Identcode In VS .NET .This chapter presents a stochastic process modeling method of building the norm pro le and using the stochastic model of the norm pro le for cyber attack detection through anomaly detection Speci cally, the Markov chain model of event transitions as the norm pro le and the evaluation of the probability in which an event sequence is observed under the normal use condition capture more information in an event sequence than the EWMA representation of the event frequency used in the multivariate statistical anomaly detection technique as described n 14 However, this anomaly detection using a more powerful, stochastic modeling method still suffers the drawback of the anomaly detection methodology in lack of handling the data mixture of attack and normal use activities and consequently the performance degradation as the mixture level of attack data and normal use data increases s 16 and 17 in Part VI. In Visual Studio .NET Using Barcode encoder for ASP .Related: .NET Codabar Generator , ITF-14 Generator .NET , Interleaved 2 of 5 Generation .NET

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