We study an online model for the maximum k-coverage problem, where given a universe of elements E = {e 1 , e 2 ,. .. , e m }, a collection of subsets of E, S = {S 1 , S 2 ,. .. , S n }, and an integer k, we ask for a subcollection A ⊆ S, such that |A| = k and the number of elements of E covered by A is maximized. In our model, at each step i, a new set S i is revealed, and we have to decide whether we will keep it or discard it. At any time of the process, only k sets can be kept in memory; if at some point the current solution already contains k sets, any inclusion of any new set in the solution must entail the irremediable deletion of one set of the current solution (a set not kept when revealed is irremediably deleted). We first propose an algorithm that improves upon former results for the same model. We next settle a graph-version of the problem, called maximum k-vertex coverage problem. Here also we propose non-trivial improvements of the competitive ratio for natural classes of graphs (mainly regular and bipartite).

We propose a unifying framework based on configuration linear programs and randomized rounding, for different energy optimization problems in the dynamic speedscaling setting. We apply our framework to various scheduling and routing problems in heterogeneous computing and networking environments. We first consider the energy minimization problem of scheduling a set of jobs on a set of parallel speed scalable processors in a fully heterogeneous setting. For both the preemptive-non-migratory and the preemptive-migratory variants, our approach allows us to obtain solutions of almost the same quality as for the homogeneous environment. By exploiting the result for the preemptive-non-migratory variant, we are able to improve the best known approximation ratio for the single processor non-preemptive problem. Furthermore, we show that our approach allows to obtain a constant-factor approximation algorithm for the power-aware preemptive job shop scheduling problem. Finally, we consider the min-power routing problem where we are given a network modeled by an undirected graph and a set of uniform demands that have to be routed on integral routes from their sources to their destinations so that the energy consumption is minimized. We improve the best known approximation ratio for this problem.

We study the problem of executing an application represented by a precedence task graph on a multi-core machine composed of standard computing cores and accelerators. Contrary to most existing approaches, we distinguish the allocation and the scheduling phases and we mainly focus on the allocation part of the problem: choose the more appropriate type of computing unit for each task. We address both off-line and on-line settings. In the first case, we establish strong lower bounds on the worst-case performance of a known approach based on Linear Programming for solving the allocation problem. Then, we refine the scheduling phase and we replace the greedy list scheduling policy used in this approach by a better ordering of the tasks. Although this modification leads to the same approximability guarantees, it performs much better in practice. We also extend this algorithm to more types of heterogeneous cores, achieving an approximation ratio which depends on the number of different types. In the online case, we assume that the tasks arrive in any, not known in advance, order which respects the precedence relations and the scheduler has to take irrevocable decisions about their allocation and execution. In this setting, we propose the first scheduling algorithm with precedences based on adequate rules for selecting the type of processor where to allocate the tasks. This algorithm achieves a constant factor approximation guarantee if the ratio of the number of CPUs over the number of GPUs is bounded. Finally, all the previous algorithms have been experimented on a large number of simulations built upon actual libraries. These simulations assess the good practical behavior of the algorithms with respect to the state-of-the-art solutions whenever these exist or baseline algorithms.

Abstract-The routing and spectrum assignment problem has emerged as the key design and control problem in elastic optical networks. In this work, we show that the spectrum assignment (SA) problem in mesh networks transforms to the problem of scheduling multiprocessor tasks on dedicated processors. Based on this new perspective, we show that the SA problem in chain (linear) networks is NP-hard for four or more links, but is solvable in polynomial time for three links. We also develop new constant-ratio approximation algorithms for the SA problem in chains when the number of links is fixed. Finally, we present several list scheduling algorithms that are computationally efficient and simple to implement, yet produce solutions that, on average, are within 1%-5% of the lower bound.

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We consider a single machine, a set of unit-time jobs, and a set of unit-time errors. We assume that the time-slot at which each error will occur is not known in advance but, for every error, there exists an uncertainty area during which the error will take place. In order to find if the error occurs in a specific time-slot, it is necessary to issue a query to it. In this work, we study two problems: (i) the error-query scheduling problem, whose aim is to reveal enough error-free slots with the minimum number of queries, and (ii) the lexicographic error-query scheduling problem where we seek the earliest error-free slots with the minimum number of queries. We consider both the off-line and the on-line versions of the above problems. In the former, the whole instance and its characteristics are known in advance and we give a polynomial-time algorithm for the error-query scheduling problem. In the latter, the adversary has the power to decide, in an on-line way, the time-slot of appearance for each error. We propose then both lower bounds and algorithms whose competitive ratios asymptotically match these lower bounds.

We study online scheduling problems on a single processor that can be viewed as extensions of the well-studied problem of minimizing total weighted flow time. In particular, we provide a framework of analysis that is derived by duality properties, does not rely on potential functions and is applicable to a variety of scheduling problems. A key ingredient in our approach is bypassing the need for "black-box" rounding of fractional solutions, which yields improved competitive ratios.We begin with an interpretation of Highest-Density-First (HDF) as a primal-dual algorithm, and a corresponding proof that HDF is optimal for total fractional weighted flow time (and thus scalable for the integral objective). Building upon the salient ideas of the proof, we show how to apply and extend this analysis to the more general problem of minimizing j w j g(F j ), where w j is the job weight, F j is the flow time and g is a non-decreasing cost function. Among other results, we present improved competitive ratios for the setting in which g is a concave function, and the setting of same-density jobs but general cost functions. We further apply our framework of analysis to online weighted completion time with general cost functions as well as scheduling under polyhedral constraints.

We study the following generalization of the classical edge coloring problem: Given a weighted graph, find a partition of its edges into matchings (colors), each one of weight equal to the maximum weight of its edges, so that the total weight of the partition is minimized. We explore the frontier between polynomial and NP-hard variants of the problem as well as the approximability of the NP-hard variants.