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Infrastructure as a Service (IaaS) clouds couple applications tightly with the underlying infrastructures and services. This vendor lock-in problem forces users to apply ad-hoc deployment strategies in order to tolerate cloud failures, and limits the ability of doing virtual machine (VM) migration and resource scaling across different clouds. This paper(More)
In this paper we introduce an active-probing approach that measures the eDonkey (eDonkey 2000) network by using an experimental client developed for this purpose. Our approach collects information from the eDonkey network by issuing queries for files. Based on this technique, we present our experiment and the results that evaluate the capacity of the(More)
Cloud computing allows business customers to scale up and down their resource usage based on needs. Many of the touted gains in the cloud model come from resource multiplexing through virtualization technology. In this paper, we present a system that uses virtualization technology to allocate data center resources dynamically based on application demands(More)
—Data center applications present significant opportunities for multiplexing server resources. Virtualization technology makes it easy to move running application across physical machines. In this paper, we present an approach that uses virtualization technology to allocate data center resources dynamically based on application demands and support green(More)
In this paper, we explore the use of live VM migration to take advantage of spot markets such as provided by Amazon and Google. These markets provide an exciting low cost alternative to regular VM instances, but the threats of price spikes and premature termination severely limit their usability. Migration can address these threats: spot market instances(More)
RESEARCH My research interest is in theoretical and applied machine learning, with a focus on adaptive and robust online learning algorithms, advanced boosting algorithms and their applications to game theory. A Drifting-Games Analysis for Online Learning and Applications to Boosting.
Many applications perform real-time analysis on data streams. We argue that existing solutions are poorly matched to the need, and introduce our new Freeze-Frame File System. Freeze-Frame FS is able to accept streams of updates while satisfying "temporal reads" on demand. The system is fast and accurate: we keep all update history in a memory-mapped log,(More)
Deterministic replay, which provides the ability to travel backward in time and reconstruct the past execution flow of a multiprocessor system, has many prominent applications. Prior research in this area can be classified into two categories: hardware-only schemes and software-only schemes. While hardware-only schemes deliver high performance, they require(More)
Global cloud services have to respond to workloads that shift geographically as a function of time-of-day or in response to special events. While many such services have support for adding nodes in one region and removing nodes in another, we demonstrate that such mechanisms can lead to significant performance degradation. Yet other services do not support(More)