Development of an internet supportive platform for online teaching and learning of construction planning and scheduling

Construction Planning and Scheduling is taught for the fIrst time ill Semester 2, 2004 in the School of Architecture and Building, Deakin University. During the unit development process and the implementation of teaching activities, several issues arose in relation to implementing computer-aided construction scheduling and unit delivery in a unitary environment. Although various types of construction planning and scheduling software have been developed and applied, none of them can be run inside an online teaching software package, which provides powerful functions in administration. This research aims to explore the strategies to connect a project planning and scheduling software package and an online ~aching and learning software package by a Web-based support platform so that both the lecturer and students can draw up and communicate a construction plan or schedule with tables and fIgures. The key techniques of this supportive platfonn are idt;nlifies and they include a web-based graphically user-interfaced, dynamic and distributed multimedia data acquisition mechanism, which accepts users' drawings and retrieval information from canvas and stores the multimedia data ona server for further usage. This paper demonstrates the techniques and principals needed to construct such a multimedia data acquisition tool. This. research will fill the gap.in the literature in respect to an online pedagogical solution to an existing problem.

Parallel I/O scheduling in the presence of data duplication on multiprogrammed cluster computing systems

The widespread adoption of cluster computing as a high performance computing platform has seen the growth of data intensive scientific, engineering and commercial applications such as digital libraries, climate modeling, computational chemistry, computational fluid dynamics and image repositories. However, I/O subsystem performance has not been keeping pace with processor and memory performance, and is fast becoming the dominant factor in overall system performance.  Thus, parallel I/O has become a necessity in the face of performance improvements in other areas of computing systems. This paper addresses the problem of parallel I/O scheduling on cluster computing systems in the presence of data replication.  We propose two new I/O scheduling algorithms and evaluate the relative performance of the proposed policies against two existing approaches.  Simulation results show that the proposed policies perform substantially better than the baseline policies.

Data collection for global scheduling in the GENESIS system

In order to schedule parallel processes on available computers there is a need to collect information about different cluster resources. Currently, global scheduling of parallel processes of applications takes into consideration mainly processor load and only in a small number of projects memory utilization. Communication costs, which are high in clusters, are practically neglected. The aim of this paper is to report on our study into cluster parameter measurements, in particular those, which characterize communication costs, the development of the resource discovery server and its initial testing through the study of the influence of communication costs on parallel application performance.

Evaluating the EASY-backfill job scheduling of static workloads on clusters

This research aims at improving our understanding of backfilling job scheduling algorithms. The most frequently used algorithm, EASY-backfilling, was selected for a performance evaluation by scheduling static workloads of parallel jobs on a computer cluster. To achieve the aim, we have developed a batch job scheduler for Linux clusters, implemented several scheduling algorithms including ARCA and EASY-Backfilling, and carried out their performance evaluation by running well known MPI applications on a real cluster. Our performance evaluation carried out for EASY-Backfilling serves two purposes. First, the performance results obtained from our evaluation can be used to validate other researcherspsila results generated by simulation, and second, the methodology used in our evaluation has alleviated many problems existed in the simulations presented in the current literature.

Sensor node activity scheduling approach

Sensor networks, once deployed, are expected to continue to function unmanned. However, energy of the sensors is typically unrenewable thus making it a very scarce resource. Therefore, in order to extend the life of the sensor networks for the duration of a particular mission, energy has to be managed wisely. In this paper, we consider the problem of energy conservation for grid-based sensor networks. A wireless sensor network architecture that is based on mobile base station is presented. Based on this architecture, we propose an asynchronous duty-cycle scheduling scheme for extending the lifetime of the grid-based wireless sensor networks.

The impact of under-estimated length of jobs on EASY-backfill scheduling

The issue of under-estimated length of jobs (parallel applications) on backfill-based scheduling is ignored in the current literature because users want to avoid their jobs to be killed when the requested time expires. Therefore, users prefer to over-estimate the length of their jobs. This paper shows the impact of underestimated length of jobs on their execution performance in an EASY-backfill scheduling-based system. We have developed a batch job scheduler for Linux clusters that implements an enhanced EASY- backfilling algorithm in such a way that a job with an under-estimated execution time would not be killed unless it would delay other jobs. We have carried out performance evaluation by scheduling static workloads of well known MPI parallel applications on a real cluster. Our results show that most of the jobs do not have to be aborted even though their job lengths are under-estimated whereas the slowdown of jobs and the throughput of the system are only slightly degraded.

Where and when to revegetate : a quantitative method for scheduling landscape reconstruction

Restoration of native vegetation is required in many regions of the world, but determining priority locations for revegetation is a complex problem. We consider the problem of determining spatial and temporal priorities for revegetation to maximize habitat for 62 bird species within a heavily cleared agricultural region, 11 000 km2 in area. We show how a reserve-selection framework can be applied to a complex, large-scale restoration-planning problem to account for multi-species objectives and connectivity requirements at a spatial extent and resolution relevant to management. Our approach explicitly accounts for time lags in planting and development of habitat resources, which is intended to avoid future population bottlenecks caused by delayed provision of critical resources, such as tree hollows. We coupled species-specific models of expected habitat quality and fragmentation effects with the dynamics of habitat suitability following replanting to produce species-specific maps for future times. Spatial priorities for restoration were determined by ranking locations (150-m grid cells) by their expected contribution to species habitat through time using the conservation planning tool, ‘‘Zonation.’’ We evaluated solutions by calculating expected trajectories of habitat availability for each species. We produced a spatially explicit revegetation schedule for the region that resulted in a balanced increase in habitat for all species. Priority areas for revegetation generally were clustered around existing vegetation, although not always. Areas on richer soils and with high rainfall were more highly ranked, reflecting their potential to support high-quality habitats that have been disproportionately cleared for agriculture. Accounting for delayed development of habitat resources altered the rank-order of locations in the derived revegetation plan and led to improved expected outcomes for fragmentation-sensitive species. This work demonstrates the potential for systematic restoration planning at large scales that accounts for multiple objectives, which is urgently needed by land and natural resource managers.

Scheduling of a parallel computation-bound application and sequential applications executing concurrently on a cluster - a case study.

Studies have shown that most of the computers in a non-dedicated cluster are often idle or lightly loaded. The underutilized computers in a non-dedicated cluster can be employed to execute parallel applications. The aim of this study is to learn how concurrent execution of a computation-bound and sequential applications influence their execution performance and cluster utilization. The result of the study has demonstrated that a computation-bound parallel application benefits from load balancing, and at the same time sequential applications suffer only an insignificant slowdown of execution. Overall, the utilization of a non-dedicated cluster is improved.

Agent-based heuristics to solve NP-hard scheduling problems

A novel agent-driven heuristic approach was developed to control the operational scheduling for a local manufacturer. This approach outperformed the traditional kanban control mechanism under numerous simulated benchmarking tests. Using this approach, the individual machine loading was reduced by, on average, 28%, with the loading spread reduced by 85%

Adaptive hierarchical scheduling policy for enterprise grid computing systems

In an enterprise grid computing environments, users have access to multiple resources that may be distributed geographically. Thus, resource allocation and scheduling is a fundamental issue in achieving high performance on enterprise grid computing. Most of current job scheduling systems for enterprise grid computing provide batch queuing support and focused solely on the allocation of processors to jobs. However, since I/O is also a critical resource for many jobs, the allocation of processor and I/O resources must be coordinated to allow the system to operate most effectively. To this end, we present a hierarchical scheduling policy paying special attention to I/O and service-demands of parallel jobs in homogeneous and heterogeneous systems with background workload. The performance of the proposed scheduling policy is studied under various system and workload parameters through simulation. We also compare performance of the proposed policy with a static space–time sharing policy. The results show that the proposed policy performs substantially better than the static space–time sharing policy.

An efficient adaptive scheduling policy for high-performance computing

The advent of commodity-based high-performance clusters has raised parallel and distributed computing to a new level. However, in order to achieve the best possible performance improvements for large-scale computing problems as well as good resource utilization, efficient resource management and scheduling is required. This paper proposes a new two-level adaptive space-sharing scheduling policy for non-dedicated heterogeneous commodity-based high-performance clusters. Using trace-driven simulation, the performance of the proposed scheduling policy is compared with existing adaptive space-sharing policies. Results of the simulation show that the proposed policy performs substantially better than the existing policies.