Abstract-While virtual machine (VM) migration is allowing data centers to rebalance workloads across physical machines, the promise of a maximally utilized infrastructure is yet to be realized. Part of the challenge is due to the inherent dependencies between VMs comprising a multi-tier application, which introduce complex load interactions between the underlying physical servers. For example, simply moving an overloaded VM to a (random) underloaded physical machine can inadvertently overload the network. We introduce AppAware-a novel, computationally efficient scheme for incorporating (1) inter-VM dependencies and (2) the underlying network topology into VM migration decisions. Using simulations, we show that our proposed method decreases network traffic by up to 81% compared to a well known alternative VM migration method that is not application-aware.

With the intense competition between cloud providers, oversubscription is increasingly important to maintain profitability. Oversubscribing physical resources is not without consequences: it increases the likelihood of overload. Memory overload is particularly damaging. Contrary to traditional views, we analyze current data center logs and realistic Web workloads to show that overload is largely transient: up to 88.1% of overloads last for less than 2 minutes. Regarding overload as a continuum that includes both transient and sustained overloads of various durations points us to consider mitigation approaches also as a continuum, complete with tradeoffs with respect to application performance and data center overhead. In particular, heavyweight techniques, like VM migration, are better suited to sustained overloads, whereas lightweight approaches, like network memory, are better suited to transient overloads. We present Overdriver, a system that adaptively takes advantage of these tradeoffs, mitigating all overloads within 8% of well-provisioned performance. Furthermore, under reasonable oversubscription ratios, where transient overload constitutes the vast majority of overloads, Overdriver requires 15% of the excess space and generates a factor of four less network traffic than a migration-only approach.

The recent explosion of the Internet and the World Wide Web has made digital libraries popular. Easy access to a digital library is provided by commercially available Web browsers, which provide a user‐friendly interface. To retrieve documents of interest, the user is provided with a search interface that may only consist of one input field and one push button. Most users type in a single keyword, click the button, and hope for the best. The result of a query using this kind of search interface can consist of a large unordered set of documents, or a ranked list of documents based on the frequency of the keywords. Both lists can contain articles unrelated to the user's inquiry unless a sophisticated search was performed and the user knows exactly what to look for. More sophisticated algorithms for ranking the search results according to how well they meet the users' needs as expressed in the search input may help. However, what is desperately needed are software tools that can analyze the search result and manipulate large hierarchies of data graphically. In this article we describe the design of a language‐independent document classification system being developed to help users of the Florida Center for Library Automation analyze search query results. Easy access through the Web is provided, as well as a graphical user interface to display the classification results. We also describe the use of this system to retrieve and analyze sets of documents from public Web sites.

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