The problem presented in this paper is a generalization of the usual coupled-tasks scheduling problem in presence of compatibility constraints. The reason behind this study is the data acquisition problem for a submarine torpedo. We investigate a particular configuration for coupled tasks (any task is divided into two sub-tasks separated by an idle time), in which the idle time of a coupled task is equal to the sum of durations of its two sub-tasks. We prove @math -completeness of the minimization of the schedule length, we show that finding a solution to our problem amounts to solving a graph problem, which in itself is close to the minimum-disjoint-path cover (min-DCP) problem. We design a @math -approximation, where a and b (the processing time of the two sub-tasks) are two input data such as a>b>0, and that leads to a ratio between @math and @math . Using a polynomial-time algorithm developed for some class of graph of min-DCP, we show that the ratio decreases to @math .
The problem of coupled-tasks has been studied in regard to different conditions on the values of @math , @math , @math for @math , and precedence constraints @cite_5 @cite_20 @cite_13 @cite_18 . Note that, in the previous works, all tasks are compatible by considering a complete graph @cite_5 @cite_20 @cite_13 @cite_18 . Moreover, in presence of any compatibility graph, we find several complexity results @cite_14 @cite_7 @cite_8 , which are summarized in Table . The notation @math implies that for all @math , @math is equal to a constant @math . This notation can be extended to @math and @math with the constants @math and @math .
@cite_18 @cite_14 @cite_7 @cite_8 @cite_5 @cite_13 @cite_20
2606268922 2950469527 1971773339 1985321454
This paper introduces a scheduling problem with coupled-tasks in presence of a compatibility graph on a single processor. We investigate a specific configuration, in which the coupled-tasks possess an idle time equal to 2. The complexity of these problems will be studied according to the presence or absence of triangles in the compatibility graph. As an extended matching, we propose a polynomial-time algorithm which consists in minimizing the number of non-covered vertices, by covering vertices with edges or paths of length two in the compatibility graph. This type of covering will be denoted by 2-cover technique. According on the compatibility graph type, the 2-cover technique provides a polynomial-time rho-approximation algorithm with rho=13 12 (resp. rho=10 9) in absence (resp. presence) of triangles. We consider a system of @math servers inter-connected by some underlying graph topology @math . Tasks arrive at the various servers as independent Poisson processes of rate @math . Each incoming task is irrevocably assigned to whichever server has the smallest number of tasks among the one where it appears and its neighbors in @math . Tasks have unit-mean exponential service times and leave the system upon service completion. The above model has been extensively investigated in the case @math is a clique. Since the servers are exchangeable in that case, the queue length process is quite tractable, and it has been proved that for any @math , the fraction of servers with two or more tasks vanishes in the limit as @math . For an arbitrary graph @math , the lack of exchangeability severely complicates the analysis, and the queue length process tends to be worse than for a clique. Accordingly, a graph @math is said to be @math -optimal or @math -optimal when the occupancy process on @math is equivalent to that on a clique on an @math -scale or @math -scale, respectively. We prove that if @math is an Erd o s-R 'enyi random graph with average degree @math , then it is with high probability @math -optimal and @math -optimal if @math and @math as @math , respectively. This demonstrates that optimality can be maintained at @math -scale and @math -scale while reducing the number of connections by nearly a factor @math and @math compared to a clique, provided the topology is suitably random. It is further shown that if @math contains @math bounded-degree nodes, then it cannot be @math -optimal. In addition, we establish that an arbitrary graph @math is @math -optimal when its minimum degree is @math , and may not be @math -optimal even when its minimum degree is @math for any @math . Consider a network of @math n processes, where each process inputs a @math d-dimensional vector of reals. All processes can communicate directly with others via reliable FIFO channels. We discuss two problems. The multidimensional Byzantine consensus problem, for synchronous systems, requires processes to decide on a single @math d-dimensional vector @math v?Rd, inside the convex hull of @math d-dimensional vectors that were input by the non-faulty processes. Also, the multidimensional Byzantine approximate agreement (MBAA) problem, for asynchronous systems, requires processes to decide on multiple @math d-dimensional vectors in @math Rd, all within a fixed Euclidean distance @math ∈ of each other, and inside the convex hull of @math d-dimensional vectors that were input by the non-faulty processes. We obtain the following results for the problems above, while tolerating up to @math f Byzantine failures in systems with complete communication graphs: (1) In synchronous systems, @math n>max 3f,(d+1)f is necessary and sufficient to solve the multidimensional consensus problem. (2) In asynchronous systems, @math n>(d+2)f is necessary and sufficient to solve the multidimensional approximate agreement problem. Our sufficiency proofs are constructive, giving explicit protocols for the problems. In particular, for the MBAA problem, we give two protocols with strictly different properties and applications. The coupled tasks scheduling problem was originally introduced for modeling complex radar devices. It is still used for controlling such devices and applied in similar applications. This paper considers a problem of coupled tasks scheduling on a single processor, under the assumptions that all processing times are equal to 1, the gap has exact integer length L and the precedence constraints are strict. We prove that the general problem, when L is part of the input and the precedence constraints graph is a general graph, is NP-hard in the strong sense. We also show that the special case when L=2 and the precedence constraints graph is an in-tree or an out-tree, can be solved in O(n) time.