Benchmarking and comparison of software project human resource allocation optimization approaches

For the Staffing and Scheduling a Software Project (SSSP), one has to find an allocation of resources to tasks while considering parameters such skills and availability to identify the optimal delivery of the project. Many approaches have been proposed that solve SSSP tasks by representing them as optimization problems and applying optimization techniques and heuristics. However, these approaches tend to vary in the parameters they consider, such as skill and availability, as well as the optimization techniques, which means their accuracy, performance, and applicability can vastly differ, making it difficult to select the most suitable approach for the problem at hand. The fundamental reason for this lack of comparative material lies in the absence of a systematic evaluation method that uses a validation dataset to benchmark SSSP approaches. We introduce an evaluation process for SSSP approaches together with benchmark data to address this problem. In addition, we present the initial evaluation of five SSSP approaches. The results shows that SSSP approaches solving identical challenges can differ in their computational time, preciseness of results and that our approach is capable of quantifying these differences. In addition, the results highlight that focused approaches generally outperform more sophisticated approaches for identical SSSP problems

Communication-Aware Scheduling of Precedence-Constrained Tasks

Jobs in large-scale machine learning platforms are expressed using a computational graph of tasks with precedence constraints. To handle such precedence-constrained tasks that have machine-dependent communication demands in settings with heterogeneous service rates and communication times, we propose a new scheduling framework, Generalized Earliest Time First (GETF), that improves upon stateof- the-art results in the area. Specifically, we provide the first provable, worst-case approximation guarantee for the goal of minimizing the makespan of tasks with precedence constraints on related machines with machine-dependent communication times

Distributed data mining in grid computing environments

The official published version of this article can be found at the link below.The computing-intensive data mining for inherently Internet-wide distributed data, referred to as Distributed Data Mining (DDM), calls for the support of a powerful Grid with an effective scheduling framework. DDM often shares the computing paradigm of local processing and global synthesizing. It involves every phase of Data Mining (DM) processes, which makes the workflow of DDM very complex and can be modelled only by a Directed Acyclic Graph (DAG) with multiple data entries. Motivated by the need for a practical solution of the Grid scheduling problem for the DDM workflow, this paper proposes a novel two-phase scheduling framework, including External Scheduling and Internal Scheduling, on a two-level Grid architecture (InterGrid, IntraGrid). Currently a DM IntraGrid, named DMGCE (Data Mining Grid Computing Environment), has been developed with a dynamic scheduling framework for competitive DAGs in a heterogeneous computing environment. This system is implemented in an established Multi-Agent System (MAS) environment, in which the reuse of existing DM algorithms is achieved by encapsulating them into agents. Practical classification problems from oil well logging analysis are used to measure the system performance. The detailed experiment procedure and result analysis are also discussed in this paper

Autonomous resource-aware scheduling of large-scale media workflows

The media processing and distribution industry generally requires considerable resources to be able to execute the various tasks and workflows that constitute their business processes. The latter processes are often tied to critical constraints such as strict deadlines. A key issue herein is how to efficiently use the available computational, storage and network resources to be able to cope with the high work load. Optimizing resource usage is not only vital to scalability, but also to the level of QoS (e.g. responsiveness or prioritization) that can be provided. We designed an autonomous platform for scheduling and workflow-to-resource assignment, taking into account the different requirements and constraints. This paper presents the workflow scheduling algorithms, which consider the state and characteristics of the resources (computational, network and storage). The performance of these algorithms is presented in detail in the context of a European media processing and distribution use-case

Performance Comparison Of Bnp Scheduling Algorithms In Homogeneous Environment

Static Scheduling is the mapping of a program to the resources of a parallel system in order to minimize the execution time. This paper presents static scheduling algorithms that schedule an edge-weighted directed acyclic graph (DAG) to a set of homogeneous processors. The aim is to evaluate and compare the performance of different algorithms and select the best algorithm amongst them. Various BNP algorithms are analyzed and classified into four groups - Highest Level First Estimated Time (HLFET), Dynamic Level Scheduling (DLS), Modified Critical Path (MCP) and Earliest Time First (ETF). Based upon their performance considering various factors, best algorithm is determined

Bounds on series-parallel slowdown

We use activity networks (task graphs) to model parallel programs and consider series-parallel extensions of these networks. Our motivation is two-fold: the benefits of series-parallel activity networks and the modelling of programming constructs, such as those imposed by current parallel computing environments. Series-parallelisation adds precedence constraints to an activity network, usually increasing its makespan (execution time). The slowdown ratio describes how additional constraints affect the makespan. We disprove an existing conjecture positing a bound of two on the slowdown when workload is not considered. Where workload is known, we conjecture that 4/3 slowdown is always achievable, and prove our conjecture for small networks using max-plus algebra. We analyse a polynomial-time algorithm showing that achieving 4/3 slowdown is in exp-APX. Finally, we discuss the implications of our results.Comment: 12 pages, 4 figure

Protein Structure Prediction with Parallel Algorithms Orthogonal to Parallel Platforms

The problem of Protein Structure Prediction (PSP) is known to be computationally expensive, which calls for the application of high performance techniques. In this project, parallel PSP algorithms found in the literature are being accelerated and ported to different parallel platforms, producing a set of algorithms that it is diverse in terms of the parallel architectures and parallel programming models used. The algorithms are intended to help other research projects and they have also been made publicly available so as to support the development of more elaborate prediction algorithms. We have thus far produced a set of 16 algorithms (mixing CUDA, OpenMP, MPI and/or complexity reduction optimizations); during its development, two algorithms that promote high performance were proposed, and they have been written in an article that was accepted in the International Conference on Computational Science (ICCS)