In this paper we survey computational models for Grid scheduling problems and their resolution using heuristic and meta-heuristic approaches. Scheduling problems are at the heart of any Grid-like computational system. Different types of scheduling based on different criteria, such as static versus dynamic environment, multi-objectivity, adaptivity, etc., are identified. Then, heuristic and meta-heuristic methods for scheduling in Grids are presented. The paper reveals the complexity of the scheduling problem in Computational Grids when compared to scheduling in classical parallel and distributed systems and shows the usefulness of heuristic and meta-heuristic approaches for the design of efficient Grid schedulers. We also discuss on requirements for a modular Grid scheduling and its integration with Grid architecture

With the fast development in wireless technologies and wireless devices, wireless mesh networks (WMN) are becoming and important networking infrastructure, especially due to their low cost of deployment and maintenance. A main issue in deploying WMNs is the optimal placement of mesh routers to achieve network connectivity and stability through the maximization of the size of the giant component in the network and user coverage. In this work we evaluate ad hoc and neighborhood search methods for placement of mesh routers in WMNs. Routers are assumed having their own coverage area, oscillating between minimum and maximum values. Given a deployment area where to distribute the mesh router nodes and a number of fixed clients a priori distributed in the given area, ad hoc methods explore different topologies such as placement in diagonals, in corners of the area, in hotspots, etc. and the resulting network connectivity and user coverage are measured. We have experimentally evaluated the considered ad hoc methods through a benchmark of generated instances as stand alone methods and as initializing methods of evolutionary algorithms. For each ad hoc method, different distributions of mesh clients (normal, exponential and Weibull), are considered and the size of the giant component and user coverage parameters are reported. Further, we considered neighborhood search methods for optimal placement of mesh routers as more powerful methods for achieving near optimal placements of mesh router nodes. The experimental evaluation showed the good performance of a swap-based movement neighborhood search, which achieved good connectivity of the network in few phases of neighborhood search exploration.

Recently there has been a growing interest in mission operations scheduling problem. The problem, in a variety of formulations, arises in management of satellite/space missions requiring efficient allocation of user requests to make possible the communication between operations teams and spacecraft systems. Not only large space agencies, such as ESA (European Space Agency) and NASA, but also smaller research institutions and universities can establish nowadays their satellite mission, and thus need intelligent systems to automate the allocation of ground station services to space missions. In this paper, we present some relevant formulations of the satellite scheduling viewed as a family of problems and identify various forms of optimization objectives. The main complexities, due highly constrained nature, windows accessibility and visibility, multi-objectives and conflicting objectives are examined. Then, we discuss the resolution of the problem through different heuristic methods. In particular, we focus on the version of ground station scheduling, for which we present computational results obtained with Genetic Algorithms using the STK simulation toolkit.

One of the key advantages of Wireless Mesh Networks (WMNs) is their importance for providing cost-efficient broadband connectivity. There are issues for achieving the network connectivity and user coverage, which are related with the node placement problem. In this work, we compare Hill Climbing (HC), Simulated Annealing (SA) and Genetic Algorithm (GA) by simulations for node placement problem. We want to find the optimal distribution of router nodes in order to provide the best network connectivity and provide the best coverage in a set of randomly distributed clients. From the simulation results, all algorithms converge to the maximum size of Giant Component (GC). However, according to the number of covered mesh clients, HC and SA converge faster.