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Data grids provide services and infrastructures for data-intensive applications that need to access to huge amount of data stored at distributed locations around the world. The size of these data can reach hundreds of petabytes scale in many applications. Ensuring an efficient and fast access to such massive data is a challenge that must be addressed. Replication is a key technique used in data grids...
The improvement of file access performance is a great challenge in real-time cloud services. In this paper, we analyze preconditions of dealing with this problem considering the aspects of requirements, hardware, software, and network environments in the cloud. Then we describe the design and implementation of a novel distributed layered cache system built on the top of the Hadoop Distributed File...
The data-intensive scientific applications running on high-end computing system depend on parallel file systems for high-speed data input/output. In most parallel file systems, a file is partitioned into multiple subfiles with a view to allowing it to be accessed concurrently. An important factor in the file partition is the stripe size. However, while working well for certain applications, most existing...
Scheduling large-scale applications in heterogeneous Grid and Cloud systems is a fundamental NP-complete problem for obtaining good performance and execution costs. We address the problem of scheduling an important class of large-scale Grid applications inspired from real-world, characterised by a large number of homogeneous, concurrent, and computationally-intensive tasks that are the main sources...
A computational grid is a large scale federated infrastructure where users execute several types of applications with different submission rates. On the evaluation of solutions for grids, there are not much effort on using realistic workloads for experiments, and most of the time users' activities and applications are not well represented. In this work, we propose a user-based grid workload model...
In this paper, we investigate how MapReduce and Cloud computing can accelerate performance of applications and scale up the computing resources through a real data mining use case in the Biomedical Sciences. We have prototyped the data mining task using the MapReduce model and evaluated it in the Cloud. A performance evaluation model has been built for assessing the eff ciency of the prototype. The...
Automating the execution of workflows (or business processes) on computer resources has been the subject of much research. However, many workflow scenarios still require human involvement, which introduces additional authorization concerns. Role-Based Authorization Control (RBAC), under which the users are assigned to certain roles while the roles are associated with prescribed permissions, is a popular...
Quality-of-Service (QoS) aware service selectionproblems are a crucial issue in both Grids and distributed, service-oriented systems. When several implementations perservice exist, one has to be selected for each workflow step. Several heuristics have been proposed, including blackboardand genetic algorithms. Their applicability and performancehas already been assessed for static systems. In order...
MapReduce is emerging as an important programming model for data-intensive application. Adapting this model to desktop grid would allow taking advantage of the vast amount of computing power and distributed storage to execute new range of application able to process enormous amount of data. In 2010, we have presented the first implementation of MapReduce dedicated to Internet Desktop Grid based on...
Cloud computing is a novel computing paradigm that offers data, software, and hardware services in a manner similar to traditional utilities such as water, electricity, and telephony. Usually, in Cloud and Grid computing, contracts between traders are established using Service Level Agreements (SLAs), which include objectives of service usage. However, due to the rapidly growing number of service...
Workload consolidation, sharing physicalresources among multiple workloads, is a promisingtechnique to save cost and energy in cluster computingsystems. This paper highlights a number of challengesassociated with workload consolidation for Hadoop, as oneof the current state-of-the-art data-intensive clustercomputing systems. Through a systematic step-by-stepprocedure, we investigate challenges for...
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