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The successive over-relaxation (SOR) algorithm is an important method to solve the linear equations in the numerical marine forecasting model, but it is not appropriate for parallelism. In this paper, the red-black ordering and the method to avoid cost of communication are used to implement the parallelism of the SOR algorithm and improve the performance of it. The experiments show that the performance...
Big-data applications are being increasingly used in today's large-scale data enters for a large variety of purposes, such as solving scientific problems, running enterprise services, and computing data-intensive tasks. Due to the growing scale of these systems and the complexity of running applications, jobs running in big-data systems experience unsuccessful terminations of different nature. While...
Managing and optimising cloud services is one of the main challenges faced by industry and academia. A possible solution is resorting to self-management, as fostered by autonomic computing. However, the abstraction layer provided by cloud computing obfuscates several details of the provided services, which, in turn, hinders the effectiveness of autonomic managers. Data-driven approaches, particularly...
Due to fierce competition, cloud providers need to run their data-centers efficiently. One of the issues is to increase data-center utilization while maintaining applications' performance targets. Achieving high data-center utilization while meeting applications' performance is difficult, as data-center overload may lead to poor performance of hosted services. Service differentiation has been proposed...
Data enters are at the core of a wide variety of daily ICT utilities, ranging from scientific computing to online gaming. Due to the scale of today's data enters, the failure of computing resources is a common occurrence that may disrupt the availability of ICT services, leading to revenue loss. Although many high availability (HA) techniques have been proposed to mask resource failures, datacenter...
Presents the introductory welcome message from the conference proceedings. May include the conference officers' congratulations to all involved with the conference event and publication of the proceedings record.
Nowadays massive log streams are generated from many Internet and cloud services. Storing log streams consumes a large amount of disk space and incurs high cost. Traditional compression methods can be applied to reduce storage cost, but are inefficient for log analysis, because fetching relevant log entries from compressed data often requires retrieval and decompression of large blocks of data. We...
Presents the introductory welcome message from the conference proceedings. May include the conference officers' congratulations to all involved with the conference event and publication of the proceedings record.
The information provided by commonly used code-oriented profilers can be complemented by means of data-oriented profiling techniques. Based on a data-oriented approach, in this study we leverage techniques developed in previous papers to analyze the data access characteristics of a range of U.S. Department of Energy applications representative of different application domains. By analyzing object-differentiated...
Fault tolerance of hardware failure is a challenging work for parallel programming in massively parallel processing environment. However, traditional rollback-recovery techniques, which an be classified into checkpoint-based and log-based, would introduce extra overhead for recording an overall snapshot of an application. For a specialized programming model, a private recovery technique is valuable...
Existing Big Data streams coming from social and other connected sensor networks exhibit intrinsic inter-dependency enabling unique challenges to scalable graph analytics. Data from these graphs is usually collected in different geographically located data servers making it suitable for distributed processing on clouds. While numerous solutions for large scale static graph analysis have been proposed,...
Workflows are important computational tools in many branches of science, and because of the dependencies among their tasks and their widely different characteristics, scheduling them is a difficult problem. Most research on scheduling workflows has focused on the offline problem of minimizing the make span of single workflows with known task runtimes. The problem of scheduling multiple workflows has...
Data replication enhances data availability and thereby improves the system reliability and efficiency while reduces access latency and communication cost. A critical issue in data replication is to wisely place data replicas which involves identifying the best possible nodes to duplicate data according to user requests. In this paper, we address the problem of replica placement and update in tree...
In some applications of wireless sensor networks (WSN), sensor nodes are mobile while the sinks are static. In such dynamic environment, situations may arise where many sensor nodes are forwarding data through the same sink node resulting in sink overloading. One of the obvious effects of sink overloading is packet loss. It also indirectly affects the network lifetime in loss-sensitive WSN applications...
Clinical Sleep Research is an inherent multidisciplinary field, as many health issues may affect a person's sleep conditions and sleep disorders may cause several health problems. Many patients with chronic sleep disorders suffer from different further medical conditions -- called multimorbidity. Due to the high variety of the reasons and the courses of sleep disorders, individual cases are difficult...
With the arrival of cloud computing and Big Data, many scientific applications with large amount of data can be abstracted as scientific workflows and running on a cloud environment. Distributing these datasets intelligently can decrease data transfers efficiently during the workflow's execution. In this paper, we proposed a 2- stage data placement strategy. In the initial stage, we cluster the datasets...
We propose Cloud-based machine learning tools for enhanced Big Data applications, where the main idea is that of predicting the "next" workload occurring against the target Cloud infrastructure via an innovative ensemble-based approach that combine the effectiveness of different well-known classifiers in order to enhance the whole accuracy of the final classification, which is very relevant...
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