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Cloud platforms make available a virtually infinite amount of computing resources, which are managed by third parties and are accessed by users on demand in a pay-per-use manner, with Quality of Service guarantees. This enables computing infrastructures to be scaled up and down accordingly to the amount of data to be processed. MapReduce is among the most popular models for development of Cloud applications...
In this paper, we propose a taxonomy that characterizes and classifies different components of autonomic application management in Grids. We also survey several representative Grid systems developed by various projects world-wide to demonstrate the comprehensiveness of the taxonomy. The taxonomy not only highlights the similarities and differences of state-of-the-art technologies utilized in autonomic...
Microarray technology allows for the simultaneous monitoring of thousands of genes expressions per sample. Unfortunately, the classification of these samples into distinct classes is often difficult as the number of genes (features) greatly exceeds the number of samples. Consequently, there is a need to investigate new, robust machine learning techniques capable of accurately classifying microarray...
Computing is being transformed to a model consisting of services that are commoditised and delivered in a manner similar to utilities such as water, electricity, gas, and telephony. In such a model, users access services based on their requirements without regard to where the services are hosted. Several computing paradigms have promised to deliver this utility computing vision and they include Grid...
In this paper, we present a framework that enables scientists to steer computations executing over large-scale grid computing environments. By using computational steering, users can dynamically control their simulations or computations to reach expected results more efficiently. The framework supports steerable applications by introducing an asynchronous iterative MapReduce programming model that...
Scientific computing often requires the availability of a massive number of computers for performing large scale experiments. Traditionally, these needs have been addressed by using high-performance computing solutions and installed facilities such as clusters and super computers, which are difficult to setup, maintain, and operate. Cloud computing provides scientists with a completely new model of...
Peering of Content Delivery Networks (CDNs) allow providers to rapidly scale-out to meet both flash crowds and anticipated increases in demand. Recent trends foster the need for a utility model for content delivery services to provide transparency, high availability, reduced investment cost, and improved content delivery performance. Analysis of prior work reveals only a modest progress in evaluating...
To ensure Quality of Service (QoS) for data centers, it is critical to enforce a fair share of storage resources between competing users. Interposed schedulers are one of the most practical methods for performance isolation. Most fair queuing-based proportional sharing algorithms for existing interposed scheduler are variants of counterparts designed for network routers and may result in breaking...
Efficient scheduling is a key concern for the effectual execution of performance driven Grid applications, such as workflows. Many list heuristics have been developed for scheduling workflows in centralized Grid environment. However, in this paper, we present a distributed list heuristic for decentralized scheduling of workflow applications in global Grids. The simulation results show that the proposed...
In this paper, a reputation-based Grid workflow scheduling algorithm is proposed to counter the effect of inherent unreliability and temporal characteristics of computing resources in large scale, decentralized Grid overlays. The proposed approach builds upon structured peer-to-peer indexing and overlay networking techniques to create a scalable wide-area networking of Grid sites for supporting dependable...
Distributed system emulators provide a paramount platform for testing of network protocols and distributed applications in clusters and networks of workstations. However, to allow testers to benefit from these systems, it is necessary an efficient and automatic mapping of hundreds, or even thousands, of virtual nodes to physical hosts-and the mapping of the virtual links between guests to physical...
Computing is being transformed to a model consisting of services that are commoditised and delivered in a manner similar to utilities such as water, electricity, gas, and telephony. In such a model, users access services based on their requirements without regard to where the services are hosted. Several computing paradigms have promised to deliver this utility computing vision and they include Grid...
Distributed systems emulators built with the aid of virtualization tools allow testing of systems in a testbed whose number of real elements are orders of magnitude smaller than the number of virtual elements being tested. However, to allow testers to benefit from these systems, operation of the virtual environment should be hidden from them and performed automatically by the emulator. Moreover, testers...
Cloud resource providers in a market face dynamic and unpredictable consumer behavior. The way, how prices are set in a dynamic environment, can influence the demand behavior of price sensitive customers. A cloud resource provider has to decide on how to allocate his scarce resources in order to maximize his profit. The application of bid price control for evaluating incoming service requests is a...
Cloud computing aims to power the next generation data centers and enables application service providers to lease data center capabilities for deploying applications depending on user QoS (Quality of Service) requirements. Cloud applications have different composition, configuration, and deployment requirements. Quantifying the performance of resource allocation policies and application scheduling...
Scientific applications like neuroscience data analysis are usually compute and data-intensive. With the use of globally distributed resources and suitable middlewares, we can achieve much shorter execution time, distribute compute and storage load, and add greater flexibility to the execution of these scientific applications than we could ever achieve in a single compute resource.In this paper, we...
Metaschedulers can distribute parts of a bag-of-tasks (BoT) application among various resource providers in order to speed up its execution. When providers cannot disclose private information such as their load and computing power, which are usually heterogeneous, the metascheduler needs to make blind scheduling decisions. We propose three policies for composing resource offers to schedule deadline-constrained...
For an application in public-resource computing environments, providing reliable scheduling based on resource reliability evaluation is becoming increasingly important. Most existing reputation models used for reliability evaluation ignore the time influence. And very few works use a robust genetic algorithm to optimize both time and reliability for a workflow application. Hence, in this paper, we...
To optimize makespan and reliability for workflow applications, most existing works use list heuristics rather than genetic algorithms (GAs) which can usually give better solutions. In addition, most existing GAs evolve a scheduling solution randomly, which may give invalid solutions or lead to slow convergence of the algorithm. In this paper, we define three heuristics for GAs to decide the priorities...
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