Richard M. Wallace

Applications of neural-based spot market prediction for cloud computing

Advances in service-oriented architectures (SOA), virtualization, high-speed networks, and cloud computing has resulted in attractive pay-as-you-go services. Job scheduling on these systems results in commodity bidding for computing time. This bidding is institutionalized by Amazon for its Elastic Cloud Computing (EC2) environment and bidding methods exist for other cloud-computing vendors as well as multi-cloud and cluster computing brokers such as SpotCloud. Commodity bidding for computing has resulted in complex spot price models that have ad-hoc strategies to provide demand for excess capacity. In this paper we will discuss vendors who provide spot pricing and bidding and present a predictive model for future spot prices based on neural networking giving users a high confidence on future prices aiding bidding on commodity computing.

A multi-dimensional job scheduling

With the advent of new computing technologies, such as cloud computing and contemporary parallel processing systems, the building blocks of computing systems have become multi-dimensional. Traditional scheduling systems based on a single-resource optimization, like processors, fail to provide near optimal solutions. The efficient use of new computing systems depends on the efficient use of several resource dimensions. Thus, the scheduling systems have to fully use all resources. In this paper, we address the problem of multi-resource scheduling via multi-capacity bin-packing. We propose the application of multi-capacity-aware resource scheduling at host selection layer and queuing mechanism layer of a scheduling system. The experimental results demonstrate performance improvements of scheduling in terms of waittime and slowdown metrics. A proposal for scheduling problem based on multi-capacity bin-packing algorithms.A proposal for host selection and queuing based on multi-resource scheduling.Getting better waittime and slowdown metrics than the state of the art scheduling.

Autonomic resource contention-aware scheduling

The complexity of computing systems introduces a few issues and challenges such as poor performance and high energy consumption. In this paper, we first define and model resource contention metric for high performance computing workloads as a performance metric in scheduling algorithms and systems at the highest level of resource management stack to address the main issues in computing systems. Second, we propose a novel autonomic resource contention‐aware scheduling approach architected on various layers of the resource management stack. We establish the relationship between distributed resource management layers in order to optimize resource contention metric. The simulation results confirm the novelty of our approach.Copyright

A performance/cost model for a CUDA drug discovery application on physical and public cloud infrastructures

Virtual Screening (VS) methods can considerably aid drug discovery research, predicting how ligands interact with drug targets. BINDSURF is an efficient and fast blind VS methodology for the determination of protein binding sites, depending on the ligand, using the massively parallel architecture of graphics processing units(GPUs) for fast unbiased prescreening of large ligand databases. In this contribution, we provide a performance/cost model for the execution of this application on both local system and public cloud infrastructures. With our model, it is possible to determine which is the best infrastructure to use in terms of execution time and costs for any given problem to be solved by BINDSURF. Conclusions obtained from our study can be extrapolated to other GPU‐based VS methodologies.Copyright

A Multi-capacity Queuing Mechanism in Multi-dimensional Resource Scheduling

With the advent of new computing technologies, such as cloud computing and contemporary parallel processing systems, the building blocks of computing systems have become multi-dimensional. Traditional scheduling algorithms based on a single-resource optimization like processor fail to provide near optimal solutions. The efficient use of new computing systems depends on the efficient use of all resource dimensions. Thus, the scheduling algorithms have to fully use all resources. In this paper, we propose a queuing mechanism based on a multi-resource scheduling technique. For that, we model multi-resource scheduling as a multi-capacity bin-packing scheduling algorithm at the queue level to reorder the queue in order to improve the packing and as a result improve scheduling metrics. The experimental results demonstrate performance improvements in terms of waittime and slowdown metrics.