ABSTRACT As computational simulations become complex and the amount of processed data grows, executing scientific workflows in High‐Performance Computing (HPC) environments is increasingly essential. However, accurately estimating the required computational resources for such executions presents a significant challenge, requiring a thorough examination of the workflow structure and the characteristics of the computational environment. This manuscript introduces the GraspCC‐LB heuristic, based on the Greedy Randomized Adaptive Search Procedure (GRASP), for estimating the necessary resources for executing scientific workflows in HPC environments. Unlike existing methods, GraspCC‐LB incorporates the layered structure of workflows into its estimation process. The proposed approach was evaluated using real traces of workflows from the fields of bioinformatics and astronomy. The resource estimations produced by GraspCC‐LB were compared against the actual resource usage in a real‐world HPC environment to evaluate its effectiveness. The results demonstrate the effectiveness of GraspCC‐LB as a robust approach for resource optimization in the context of large‐scale scientific workflows that require HPC capabilities.

Abstract Given an edge‐colored graph G, a cycle with all its edges with different colors is called a rainbow cycle. The rainbow cycle cover (RCC) problem consists of finding the minimum number of disjoint rainbow cycles covering G. We present an integer linear programming model for the RCC problem and a reduction process for decreasing the dimensions of the graph, resulting in a more efficient method that was able to find new optimal solutions for instances that were unsolved.

Cloud computing establishes a new computing model where a wide range of computing resources are provided to several types of users. Especially for bioinformatics experiments modeled as scientific workflows, clouds provide several types of resources as virtual machines (VM), storage, databases and computing power that can be combined for empowering the scientific workflow execution. These workflows usually require high performance environments and parallelism techniques since their activities are data and computing intensive and can execute for a long time. There are then some Scientific Workflow Management Systems (SWfMS) that already manage the parallel execution of scientific workflows in clouds. Most of them instantiate a virtual cluster for the execution. However, they rely on the user to estimate the amount of VMs to be instantiated to create this virtual cluster. Estimating the amount of VMs to instantiate is then a crucial task to avoid negative impacts on the workflow performance with under or over estimations. This dimensioning also is not a trivial task in clouds due to the large number of VM types to choose in a cloud provider. Previously proposed approach named GraspCC already provides a near optimal estimation of the amount of VM for general applications, not scientific workflows. In this paper, we coupled the GraspCC to SciCumulus (Cloud-based Parallel Engine for Scientific Workflows) engine to estimate the necessary amount of VMs for bioinformatics workflows. We have evaluated GraspCC by comparing the estimative with real executions of a set of large-scale comparative genomics workflows. It showed the suitability of GraspCC to estimate the amount of VMs in real bioinformatics cloud workflows.

Abstract Let G = (V, E, Q) be a undirected graph, where V is the set of vertices, E is the set of edges, and Q = {Q1,…,Qq} is a partition of V into q subsets. We refer to Q1,…,Qq as the components of the partition. The partition coloring problem (PCP) consists of finding a subset V′ of V with exactly one vertex from each component Q1,…,Qq and such that the chromatic number of the graph induced in G by V′ is minimum. This problem is a generalization of the graph coloring problem. This work presents a branch‐and‐cut algorithm proposed for PCP. An integer programing formulation and valid inequalities are proposed. A tabu search heuristic is used for providing primal bounds. Computational experiments are reported for random graphs and for PCP instances originating from the problem of routing and wavelength assignment in all‐optical WDM networks. © 2009 Wiley Periodicals, Inc. NETWORKS, 2010