13,555 research outputs found
Exploring Task Mappings on Heterogeneous MPSoCs using a Bias-Elitist Genetic Algorithm
Exploration of task mappings plays a crucial role in achieving high
performance in heterogeneous multi-processor system-on-chip (MPSoC) platforms.
The problem of optimally mapping a set of tasks onto a set of given
heterogeneous processors for maximal throughput has been known, in general, to
be NP-complete. The problem is further exacerbated when multiple applications
(i.e., bigger task sets) and the communication between tasks are also
considered. Previous research has shown that Genetic Algorithms (GA) typically
are a good choice to solve this problem when the solution space is relatively
small. However, when the size of the problem space increases, classic genetic
algorithms still suffer from the problem of long evolution times. To address
this problem, this paper proposes a novel bias-elitist genetic algorithm that
is guided by domain-specific heuristics to speed up the evolution process.
Experimental results reveal that our proposed algorithm is able to handle large
scale task mapping problems and produces high-quality mapping solutions in only
a short time period.Comment: 9 pages, 11 figures, uses algorithm2e.st
A Novel SAT-Based Approach to the Task Graph Cost-Optimal Scheduling Problem
The Task Graph Cost-Optimal Scheduling Problem consists in scheduling a certain number of interdependent tasks onto a set of heterogeneous processors (characterized by idle and running rates per time unit), minimizing the cost of the entire process. This paper provides a novel formulation for this scheduling puzzle, in which an optimal solution is computed through a sequence of Binate Covering Problems, hinged within a Bounded Model Checking paradigm. In this approach, each covering instance, providing a min-cost trace for a given schedule depth, can be solved with several strategies, resorting to Minimum-Cost Satisfiability solvers or Pseudo-Boolean Optimization tools. Unfortunately, all direct resolution methods show very low efficiency and scalability. As a consequence, we introduce a specialized method to solve the same sequence of problems, based on a traditional all-solution SAT solver. This approach follows the "circuit cofactoring" strategy, as it exploits a powerful technique to capture a large set of solutions for any new SAT counter-example. The overall method is completed with a branch-and-bound heuristic which evaluates lower and upper bounds of the schedule length, to reduce the state space that has to be visited. Our results show that the proposed strategy significantly improves the blind binate covering schema, and it outperforms general purpose state-of-the-art tool
Bio-Inspired Resource Allocation for Relay-Aided Device-to-Device Communications
The Device-to-Device (D2D) communication principle is a key enabler of direct
localized communication between mobile nodes and is expected to propel a
plethora of novel multimedia services. However, even though it offers a wide
set of capabilities mainly due to the proximity and resource reuse gains,
interference must be carefully controlled to maximize the achievable rate for
coexisting cellular and D2D users. The scope of this work is to provide an
interference-aware real-time resource allocation (RA) framework for relay-aided
D2D communications that underlay cellular networks. The main objective is to
maximize the overall network throughput by guaranteeing a minimum rate
threshold for cellular and D2D links. To this direction, genetic algorithms
(GAs) are proven to be powerful and versatile methodologies that account for
not only enhanced performance but also reduced computational complexity in
emerging wireless networks. Numerical investigations highlight the performance
gains compared to baseline RA methods and especially in highly dense scenarios
which will be the case in future 5G networks.Comment: 6 pages, 6 figure
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Primal-dual variable neighborhood search for the simple plant-location problem
Copyright @ 2007 INFORMSThe variable neighborhood search metaheuristic is applied to the primal simple plant-location problem and to a reduced dual obtained by exploiting the complementary slackness conditions. This leads to (i) heuristic resolution of (metric) instances with uniform fixed costs, up to n = 15,000 users, and m = n potential locations for facilities with an error not exceeding 0.04%; (ii) exact solution of such instances with up to m = n = 7,000; and (iii) exact solutions of instances with variable fixed costs and up to m = n = 15, 000.This work is supported by NSERC Grant 105574-02; NSERC Grant OGP205041; and partly by the Serbian Ministry of Science, Project 1583
On critical service recovery after massive network failures
This paper addresses the problem of efficiently restoring sufficient resources in a communications network to support the demand of mission critical services after a large-scale disruption. We give a formulation of the problem as a mixed integer linear programming and show that it is NP-hard. We propose a polynomial time heuristic, called iterative split and prune (ISP) that decomposes the original problem recursively into smaller problems, until it determines the set of network components to be restored. ISP's decisions are guided by the use of a new notion of demand-based centrality of nodes. We performed extensive simulations by varying the topologies, the demand intensity, the number of critical services, and the disruption model. Compared with several greedy approaches, ISP performs better in terms of total cost of repaired components, and does not result in any demand loss. It performs very close to the optimal when the demand is low with respect to the supply network capacities, thanks to the ability of the algorithm to maximize sharing of repaired resources
A Domain-Independent Algorithm for Plan Adaptation
The paradigms of transformational planning, case-based planning, and plan
debugging all involve a process known as plan adaptation - modifying or
repairing an old plan so it solves a new problem. In this paper we provide a
domain-independent algorithm for plan adaptation, demonstrate that it is sound,
complete, and systematic, and compare it to other adaptation algorithms in the
literature. Our approach is based on a view of planning as searching a graph of
partial plans. Generative planning starts at the graph's root and moves from
node to node using plan-refinement operators. In planning by adaptation, a
library plan - an arbitrary node in the plan graph - is the starting point for
the search, and the plan-adaptation algorithm can apply both the same
refinement operators available to a generative planner and can also retract
constraints and steps from the plan. Our algorithm's completeness ensures that
the adaptation algorithm will eventually search the entire graph and its
systematicity ensures that it will do so without redundantly searching any
parts of the graph.Comment: See http://www.jair.org/ for any accompanying file
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