S. No. | IEEE TITLE | ABSTRACT | IEEE YEAR |
1 | Application-Level Optimization of Big Data Transfers through Pipelining, Parallelism and Concurrency | In end-to-end data transfers, there are several factors affecting the data transfer throughput, such as the network characteristics (e.g., network bandwidth, round-trip-time, background traffic); end-system characteristics (e.g., NIC capacity, number of CPU cores and their clock rate, number of disk drives and their I/O rate); and the dataset characteristics (e.g., average file size, dataset size, file size distribution). Optimization of big data transfers over inter-cloud and intra-cloud networks is a challenging task that requires joint-consideration of all of these parameters. This optimization task becomes even more challenging when transferring datasets comprised of heterogeneous file sizes (i.e., large files and small files mixed). Previous work in this area only focuses on the end-system and network characteristics however does not provide models regarding the dataset characteristics. In this study, we analyze the effects of the three most important transfer parameters that are used to enhance data transfer throughput: pipelining, parallelism and concurrency. We provide models and guidelines to set the best values for these parameters and present two different transfer optimization algorithms that use the models developed. The tests conducted over high-speed networking and cloud testbeds show that our algorithms outperform the most popular data transfer tools like Globus Online and UDT in majority of the cases. | 2016 |
2 | Dynamic and Fault-Tolerant Clustering for Scientific Workflows | Task clustering has proven to be an effective method to reduce execution overhead and to improve the computational granularity of scientific workflow tasks executing on distributed resources. However, a job composed of multiple tasks may have a higher risk of suffering from failures than a single task job. In this paper, we conduct a theoretical analysis of the impact of transient failures on the runtime performance of scientific workflow executions. We propose a general task failure modeling framework that uses a maximum likelihood estimation-based parameter estimation process to model workflow performance. We further propose three faulttolerant clustering strategies to improve the runtime performance of workflow executions in faulty execution environments. Experimental results show that failures can have significant impact on executions where task clustering policies are not fault-tolerant, and that our solutions yield makespan improvements in such scenarios. In addition, we propose a dynamic task clustering strategy to optimize the workflow’s makespan by dynamically adjusting the clustering granularity when failures arise. A trace-based simulation of five real workflows shows that our dynamic method is able to adapt to unexpected behaviors, and yields better makespans when compared to static methods. | 2016 |
3 | Monetary Cost Optimizations for Hosting Workflow-as-a-Service in IaaS Clouds | Recently, we have witnessed workflows from science and other data-intensive applications emerging on Infrastructureas-a-Service (IaaS) clouds, and many workflow service providers offering workflow-as-a-service (WaaS). The major concern of WaaS providers is to minimize the monetary cost of executing workflows in the IaaS clouds. The selection of virtual machines (instances) types significantly affects the monetary cost and performance of running a workflow. Moreover, IaaS cloud environment is dynamic, with high performance dynamics caused by the interference from concurrent executions and price dynamics like spot prices offered by Amazon EC2. Therefore, we argue that WaaS providers should have the notion of offering probabilistic performance guarantees for individual workflows to explicitly expose the performance and cost dynamics of IaaS clouds to users. We develop a scheduling system called Dyna to minimize the expected monetary cost given the user-specified probabilistic deadline guarantees. Dyna includes an A$ -based instance configuration method for performance dynamics, and a hybrid instance configuration refinement for using spot instances. Experimental results with three scientific workflow applications on Amazon EC2 and a cloud simulator demonstrate (1) the ability of Dyna on satisfying the probabilistic deadline guarantees required by the users; (2) the effectiveness on reducing monetary cost in comparison with the existing approaches. | 2016 |
4 | OverFlow: Multi-Site Aware Big Data Management for Scientific Workflows on Clouds | The global deployment of cloud datacenters is enabling large scale scientific workflows to improve performance and deliver fast responses. This unprecedented geographical distribution of the computation is doubled by an increase in the scale of the data handled by such applications, bringing new challenges related to the efficient data management across sites. High throughput, low latencies or cost-related trade-offs are just a few concerns for both cloud providers and users when it comes to handling data across datacenters. Existing solutions are limited to cloud-provided storage, which offers low performance based on rigid cost schemes. In turn, workflow engines need to improvise substitutes, achieving performance at the cost of complex system configurations, maintenance overheads, reduced reliability and reusability. In this paper, we introduce OverFlow, a uniform data management system for scientific workflows running across geographically distributed sites, aiming to reap economic benefits from this geo-diversity. Our solution is environment-aware, as it monitors and models the global cloud infrastructure, offering high and predictable data handling performance for transfer cost and time, within and across sites. OverFlow proposes a set of pluggable services, grouped in a data scientist cloud kit. They provide the applications with the possibility to monitor the underlying infrastructure, to exploit smart data compression, deduplication and geo-replication, to evaluate data management costs, to set a tradeoff between money and time, and optimize the transfer strategy accordingly. The system was validated on the Microsoft Azure cloud across its 6 EU and US datacenters. The experiments were conducted on hundreds of nodes using synthetic benchmarks and real-life bio-informatics applications (A-Brain, BLAST). The results show that our system is able to model accurately the cloud performance and to leverage this for efficient data dissemination, being able to reduce the monetary costs and transfer time by up to three times. | 2016 |