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As Clouds are complex, large-scale, and heterogeneous distributed systems, management of their resources is a challenging task. They need automated and integrated intelligent strategies for provisioning of resources to offer services that are secure, reliable, and cost-efficient. Hence, effective management of services becomes fundamental in software(More)
Effective scheduling is a key concern for the execution of performance driven applications, such as workflows in dynamic and cost driven environment including Cloud. The majority of existing scheduling techniques are based on meta-heuristics that produce good schedules with advance reservation given the current state of Cloud services or heuristics that are(More)
As more and more data can be generated at a faster-than-ever rate nowadays, it becomes a challenge to processing large volumes of data for complex data analysis. In order to address performance and cost issues of big data processing on clouds, we present a novel design of adaptive workflow management system which includes a data mining based prediction(More)
As part of a sleep monitoring project, we used actigraphy based on body-worn accelerometer sensors to remotely monitor and study the sleep-wake cycle of elderly staying at nursing homes. We have conducted a fifteen patient trial of a sleep activity pattern monitoring (SAPM) system at a local nursing home. The data was collected and stored in our server and(More)
Infrastructure-as-a-Service (IaaS) cloud computing provides the ability to dynamically acquire extra or release existing computing resources on-demand to adapt to dynamic application workloads. In this paper, we propose an extensible framework for on-demand cloud resource provisioning and adaptation. The core of the framework is a set of resource adaptation(More)
Traditionally, the "best effort, cost free" model of Supercomputers/Grids does not consider pricing. Clouds have progressed towards a service-oriented paradigm that enables a new way of service provisioning based on "pay-as-you-go" model. Large scale many-task workflow (MTW) may be suited for execution on Clouds due to its scale-* requirement (scale up,(More)
In this paper, we present a cloud framework to provide cloud clustering, workflow scheduling and management, fault tolerance and distributed data storage, data analytics and visualisation services. Using a practical case study, we show that in the process of analyzing multiscale climate data, typical problems plaguing data analysts are faced. These include(More)
Scheduling multiple large-scale parallel workflow applications on heterogeneous computing systems like hybrid clouds is a fundamental NP-complete problem that is critical to meeting various types of QoS (Quality of Service) requirements. This paper addresses the scheduling problem of large-scale applications inspired from real-world, characterized by a huge(More)
<i>High Level Architecture</i> (HLA)-based simulations employing optimistic synchronization allows federates to process event and to advance simulation time freely at the risk of over-optimistic execution and execution rollbacks. In this paper, an adaptive resource provisioning system is proposed to accelerate optimistic HLA-based simulations in <i>Virtual(More)