High intensive computation applications can usually take days to months to nish an execution. During this time, it is common to have variations of the available resources when considering that such hardware is usually shared among a plurality of researchers departments within an organization. On the other hand, High Performance Clusters can take advantage of Cloud Computing bursting techniques for the execution of applications together with on-premise resources. In order to meet deadlines, high intensive computational applications can use the Cloud to boost their performance when they are data and task parallel. This article presents an ongoing work towards the use of extended resources of an HPC execution platform together with Cloud. We propose an unied view of such heterogeneous environments and a method that monitors, predicts the application execution time, and dynamically shifts part of the domain previously running in local HPC hardware to be computed in the Cloud, meeting then a specic deadline. The method is exemplied along with a seismic application that, at runtime, adapts itself to move part of the processing to the Cloud (in a movement called bursting) and also auto-scales (the moved part) over cloud nodes. Our preliminary results show that there is an expected overhead for performing this movement and for synchronizing results, but the outcomes demonstrate it is an important feature for meeting deadlines in the case an on-premise cluster is overloaded or cannot provide the capacity needed for a particular project.
More recently, @cite_7 evaluated a computational fluid dynamics application over a heterogeneous environment of a cluster and the EC2 cloud. The results indicated that there is a need to adjust the CPU power (configuration) and workload by means of load-balancing. We are in line with this study and went further with the present work -- a dynamic self-adaptive method for application load-balancing over a hybrid platform composed of cluster and cloud.
@cite_7
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In this paper, we report on the experimental results of running a large, tightly coupled, distributed multiscale computation over a hybrid High Performance Computing (HPC) infrastructures. We connected EC2 based cloud clusters located in USA to university clusters located in Switzerland. We ran a concurrent multiscale MPI based application on this infrastructure and measured the overhead induced by extending our HPC clusters with EC2 resources. Our results indicate that accommodating some parts of the multiscale computation on cloud resources can lead to low performance without a proper adjustment of CPUs power and workload. However, by enforcing a load-balancing strategy one can benefit from the extra Cloud resources. We connect an EC2-cloud cluster to a university cluster located in Switzerland.We run a distributed multiscale CFD computation over this extended infrastructure.We evaluate and compare the distributed execution to a local execution.We describe our experience of running parallel CFD application on hybrid platforms.Multiscale computation on cloud requires an adjustment of CPUs power and workload. Today's heterogeneous architectures bring together multiple general-purpose CPUs and multiple domain-specific GPUs and FPGAs to provide dramatic speedup for many applications. However, the challenge lies in utilizing these heterogeneous processors to optimize overall application performance by minimizing workload completion time. Operating system and application development for these systems is in their infancy. In this article, we propose a new scheduling and workload balancing scheme, HDSS, for execution of loops having dependent or independent iterations on heterogeneous multiprocessor systems. The new algorithm dynamically learns the computational power of each processor during an adaptive phase and then schedules the remainder of the workload using a weighted self-scheduling scheme during the completion phase. Different from previous studies, our scheme uniquely considers the runtime effects of block sizes on the performance for heterogeneous multiprocessors. It finds the right trade-off between large and small block sizes to maintain balanced workload while keeping the accelerator utilization at maximum. Our algorithm does not require offline training or architecture-specific parameters. We have evaluated our scheme on two different heterogeneous architectures: AMD 64-core Bulldozer system with nVidia Fermi C2050 GPU and Intel Xeon 32-core SGI Altix 4700 supercomputer with Xilinx Virtex 4 FPGAs. The experimental results show that our new scheduling algorithm can achieve performance improvements up to over 200p when compared to the closest existing load balancing scheme. Our algorithm also achieves full processor utilization with all processors completing at nearly the same time which is significantly better than alternative current approaches. Online Internet applications see dynamic workloads that fluctuate over multiple time scales. This paper argues that the non-stationarity in Internet application workloads, which causes the request mix to change over time, can have a significant impact on the overall processing demands imposed on data center servers. We propose a novel mix-aware dynamic provisioning technique that handles both the non-stationarity in the workload as well as changes in request volumes when allocating server capacity in Internet data centers. Our technique employs the k-means clustering algorithm to automatically determine the workload mix and a queuing model to predict the server capacity for a given workload mix. We implement a prototype provisioning system that incorporates our technique and experimentally evaluate its efficacy on a laboratory Linux data center running the TPC-W web benchmark. Our results show that our k-means clustering technique accurately captures workload mix changes in Internet applications. We also demonstrate that mix-aware dynamic provisioning eliminates SLA violations due to under-provisioning with non-stationary web workloads, and that it offers a better resource usage by reducing over-provisioning when compared to a baseline provisioning approach that only reacts to workload volume changes. We also present a case study of our provisioning approach on Amazon's EC2 cloud platform. Testing and executing large-scale computational applications in public clouds is becoming prevalent due to cost saving, elasticity, and scalability. However, how to increase the reliability and reduce the cost to run large-scale applications in public clouds is still a big challenge. In this paper, we analyzed the pricing schemes of Amazon Elastic Compute Cloud (EC2) and found the disturbance effect that the price of the spot instances can be heavily affected due to the large number of spot instances required. We proposed a dynamic approach which schedules and runs large-scale computational applications on a dynamic pool of cloud computational instances. We use hybrid instances, including both on-demand instances for high priority tasks and backup, and spot instances for normal computational tasks so as to further reduce the cost without significantly increasing the completion time. Our proposed method takes the dynamic pricing of cloud instances into consideration, and it reduces the cost and tolerates the failures for running large-scale applications in public clouds. We conducted experimental tests and an agent based Scalable complex System modeling for Sustainable city (S3) application is used to evaluate the scalability, reliability and cost saving. The results show that our proposed method is robust and highly flexible for researchers and users to further reduce cost in real practice.