Lúcia Maria de A. Drummond

A parallel heuristic for the Vehicle Routing Problem with Simultaneous Pickup and Delivery

This paper presents a parallel approach for solving the Vehicle Routing Problem with Simultaneous Pickup and Delivery (VRPSPD). The parallel algorithm is embedded with a multi-start heuristic which consists of a variable neighborhood descent procedure, with a random neighborhood ordering (RVND), integrated in an iterated local search (ILS) framework. The experiments were performed in a cluster with a multi-core architecture using up to 256 cores. The results obtained on the benchmark problems, available in the literature, show that the proposed algorithm not only improved several of the known solutions, but also presented a very satisfying scalability.

A parallel evolutionary algorithm for the vehicle routing problem with heterogeneous fleet

Nowadays genetic algorithms stand as a trend to solve NPcomplete and NP-hard problems. In this paper, we present a new hybrid metaheuristic which uses Parallel Genetic Algorithms and Scatter Search coupled with a decomposition-into-petals procedure for solving a class of Vehicle Routing and Scheduling Problems. The parallel genetic algorithm presented is based on the island model and was run on a cluster of workstations. Its performance is evaluated for a heterogeneous fleet problem, which is considered a problem much harder to solve than the homogeneous vehicle routing problem.

An asynchronous parallel metaheuristic for the period vehicle routing problem

This paper presents an asynchronous parallel metaheuristic for the period vehicle routing problem (PVRP). The PVRP generalizes the classical vehicle routing problem by extending the planning period from a single day to M days. The algorithm proposed is based on concepts used in parallel genetic algorithms and local search heuristics. The algorithm employs the Island model in which the migration frequency must not be very high. The results of computational experiments carried out on problems taken from the literature indicate that the proposed approach outperforms existing heuristics in most cases.

Anthill: a scalable run-time environment for data mining applications

Data mining techniques are becoming increasingly more popular as a reasonable means to collect summaries from the rapidly growing datasets in many areas. However, as the size of the raw data increases, parallel data mining algorithms are becoming a necessity. In this paper, we present a run-time support system that was designed to allow the efficient implementation of data-mining algorithms on heterogeneous distributed environments. We believe that the runtime framework is suitable for a broader class of applications, beyond data mining. We also present a parallelization strategy that is supported by the run-time system. We show scalability results of three different data-mining algorithms that were parallelized using our approach and our run-time support. All applications scale almost linearly up to a large number of nodes.

Optimizing virtual machine allocation for parallel scientific workflows in federated clouds

Cloud computing has established itself as an interesting computational model that provides a wide range of resources such as storage, databases and computing power for several types of users. Recently, the concept of cloud computing was extended with the concept of federated clouds where several resources from different cloud providers are inter-connected to perform a common action (e.g. execute a scientific workflow). Users can benefit from both single-provider and federated cloud environment to execute their scientific workflows since they can get the necessary amount of resources on demand. In several of these workflows, there is a demand for high performance and parallelism techniques since many activities are data and computing intensive and can execute for hours, days or even weeks. There are some Scientific Workflow Management Systems (SWfMS) that already provide parallelism capabilities for scientific workflows in single-provider cloud. Most of them rely on creating a virtual cluster to execute the workflow in parallel. However, they also rely on the user to estimate the amount of virtual machines to be allocated to create this virtual cluster. Most SWfMS use this initial virtual cluster configuration made by the user for the entire workflow execution. Dimensioning the virtual cluster to execute the workflow in parallel is then a top priority task since if the virtual cluster is under or over dimensioned it can impact on the workflow performance or increase (unnecessarily) financial costs. This dimensioning is far from trivial in a single-provider cloud and specially in federated clouds due to the huge number of virtual machine types to choose in each location and provider. In this article, we propose an approach named GraspCC-fed to produce the optimal (or near-optimal) estimation of the amount of virtual machines to allocate for each workflow. GraspCC-fed extends a previously proposed heuristic based on GRASP for executing standalone applications to consider scientific workflows executed in both single-provider and federated clouds. For the experiments, GraspCC-fed was coupled to an adapted version of SciCumulus workflow engine for federated clouds. This way, we believe that GraspCC-fed can be an important decision support tool for users and it can help determining an optimal configuration for the virtual cluster for parallel cloud-based scientific workflows. We introduce an estimation of the amount of VMs to allocate in scientific workflows.The GraspCC-fed heuristic based on GRASP is proposed.GraspCC-fed considers scientific workflows in single-provider and federated clouds.An evaluation of GraspCC-fed is provided using SciPhylomics and adapted SciCumulus.GraspCC-fed is suitable to determine an optimal configuration for virtual clusters.

An Evolutionary Hybrid Metaheuristic for Solving the Vehicle Routing Problem with Heterogeneous Fleet

Nowadays genetic algorithms stand as a trend to solve NP-complete and NP-hard problems. In this paper, we present a new hybrid metaheuristic which combines Genetic Algorithms and Scatter Search coupled with a decomposition-into-petals procedure for solving a class of Vehicle Routing and Scheduling Problems. Its performance is evaluated for a heterogeneous fleet model, which is considered a problem much harder to solve than the homogeneous vehicle routing problem.

Solving Replica Placement and Request Distribution in Content Distribution Networks

Abstract A Content Distribution Network (CDN) is an overlay network where servers replicate contents and distribute clients requests with the aim at reducing delay, server load and network congestion, hence improving the quality of service (QoS) perceived by end clients. Because of server constraints and costs involved in the replication process, it is not reasonable to replicate the contents over the entire set of servers. In this work, exact and heuristic approaches are proposed to solve a dynamic and online problem that appears in CDN management, called the Replica Placement and Request Distribution Problem. The overall objective is to find the best servers to keep the replicas and to handle requests so that the traffic cost in the network is minimized without violating server and QoS constraints.

A Parallel Hybrid Evolutionary Metaheuristic for the Period Vehicle Routing Problem

This paper presents a Parallel Hybrid Evolutionary Metaheuristic for the Period Vehicle Routing Problem (PVRP). The PRVP generalizes the classical Vehicle Routing Problem by extending the planning period from a single day to M days. The algorithm proposed is based on concepts used in Parallel Genetic Algorithms and Local Search Heuristics. The algorithm employs the island model in which the migration frequency must not be very high. The results of computational experiments carried out on problems taken from the literature indicate that the proposed approach outperforms existing heuristics in most cases.

Combining an evolutionary algorithm with data mining to solve a single-vehicle routing problem

The aim of this work is to present some alternatives to improve the performance of an Evolutionary Algorithm applied to the problem known as the Oil Collecting Vehicle Routing Problem. Some proposals based on the insertion of Local Search and Data Mining modules in a Genetic Algorithm (GA) are presented. Four algorithms were developed: a Genetic Algorithm, a Genetic Algorithm with a Local Search procedure, a Genetic Algorithm including a Data Mining module and a Genetic Algorithm including Local Search and Data Mining. Experimental results demonstrate that the incorporation of Data Mining and Local Search modules in GA can improve the solution quality produced by this method.

Distributed Breakpoint Detection in Message-Passing Programs

The ability to set breakpoints stands, along with the possibility of deterministic reexecution, as one of the most important issues in the debugging of message-passing programs. We consider in this paper the design of fully distributed algorithms for the detection of breakpoints in such programs, and provide four algorithms, one for each different type of breakpoint. One of the algorithms detects the occurrence of unconditional breakpoints, while the other three detect the occurrence of breakpoints on disjunctive predicates, stable conjunctive predicates, and generic conjunctive predicates. All the algorithms we present detect breakpoints in the form of earliest global states with respect to the particular property involved. In the case of unconditional breakpoints, such an earliest global state must coincide exactly with the requested local unconditional breakpoints for the processes that do actually participate in the breakpoint. In the case of the other (conditional) breakpoints, what is detected is the earliest global state at which either the disjunctive or the conjunctive predicate under consideration is true. In order to actually halt the computation at the exact global state the algorithms detect, we suggest as a first approach the use of checkpointing and rollback-recovery techniques.