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Field-programmable gate arrays (FPGAs) can offer invaluable computational performance for many compute-intensive algorithms. However, to justify their purchase and administration costs it is necessary to maximize resource utilization over their expected lifetime. Making FPGAs available in a cloud environment would make them attractive to new types of users and applications and help democratize this...
Cloud datacenters are compute facilities formed by hundreds and thousands of heterogeneous servers requiring significant power requirements to operate effectively. Servers are composed by multiple interacting sub-systems including applications, microelectronic processors, and cooling which reflect their respective power profiles via different parameters. What is presently unknown is how to accurately...
The need for energy-awareness in current data centers has encouraged the use of power modeling to estimate their power consumption. However, existing models present noticeable limitations, which make them application-dependent, platform-dependent, inaccurate, or computationally complex. In this paper, we propose a platform-and application-agnostic methodology for full-system power modeling in heterogeneous...
Modern applications are increasingly being composed from multiple components that require and consume services at dierent layers of the cloud stack. The diverse, dynamic and unpredictable nature of both cloud services and application workloads makes quality-assured provision of such cloud service-based applications (CSBAs) a major challenge. While elasticity and autoscaling gives CSBA providers the...
A major challenge in Cloud computing is resource provisioning for computational tasks. Not surprisingly, previous work has established a number of solutions to provide Cloud resources in an efficient manner. However, in order to realize a holistic resource provisioning model, a prediction of the future resource consumption of upcoming computational tasks is necessary. Nevertheless, the topic of prediction...
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