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Pelletization of the loose rice straw is an attractive option to produce renewable fuels. In this paper, we focus on the problem of how to improve this pelletization process, especially reduce energy consumption and improve product quality. In detail, we pretreat rice straw and investigate the densification characteristics of the pretreated materials. Pretreatment methods of the materials include...
Hypervisor-based virtualization technology has been successfully used to deploy high-performance and scalable infrastructure for Hadoop, and now Spark applications. Container-based virtualization techniques are becoming an important option, which is increasingly used due to their lightweight operation and better scaling when compared to Virtual Machines (VM). With containerization techniques such...
Due to the flexibility of data operations and scalability of in- memory cache, Spark has revealed the potential to become the standard distributed framework to replace Hadoop for data-intensive processing in both industry and academia. However, we observe that the built-in scheduling algorithms in Spark (i.e., FIFO and FAIR) are not optimized for the applications with multiple parallel and independent...
Predicting performance of an application running on high performance computing (HPC) platforms in a cloud environment is increasingly becoming important because of its influence on development time and resource management. However, predicting the performance with respect to parallel processes is complex for iterative, multi-stage applications. This research proposes a performance approximation approach...
Docker containers [2] are becoming the mainstay for deploying applications in cloud platforms, having many desirable features like ease of deployment, developer friendliness and lightweight virtualization. Meanwhile, solid state disks (SSDs) have witnessed tremendous performance boost through recent innovations in industry such as Non-Volatile Memory Express (NVMe) standards [3], [4]. However, the...
Predicting performance of an application running on parallel computing platforms is increasingly becoming important due to the long development time of an application and the high resource management cost of parallel computing platforms. However, predicting overall performance is complex and must take into account both parallel calculation time and communication time. Difficulty in accurate performance...
Our cloud-based IT world is founded on hyper-visors and containers. Containers are becoming an important cornerstone, which is increasingly used day-by-day. Among different available frameworks, docker has become one of the major adoptees to use containerized platform in data centers and enterprise servers, due to its ease of deploying and scaling. Further more, the performance benefits of a lightweight...
In a shared virtualized storage system that runs VMs with heterogeneous IO demands, it becomes a problem for the hypervisor to cost-effectively partition and allocate SSD resources among multiple VMs. There are two straightforward approaches to solving this problem: equally assigning SSDs to each VM or managing SSD resources in a fair competition mode. Unfortunately, neither of these approaches can...
Use of accelerators such as GPUs is increasing, but efficient use of GPUs requires making good design choices. Such design choices include type of memory allocation and overlapping concurrency of data transfer with parallel computation. Performance varies with the application, hardware version such as generation of GPU, and software version including programming language drivers. This large number...
K-Means clustering is a popular unsupervised machine learning method which has been used in diverse applications including image processing, information retrieval, social sciences and weather forecasting. However, clustering is computationally expensive especially when applied to large datasets. In this paper, we explore accelerating the performance of K-means clustering using three approaches: 1)...
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