Cloud computing becomes increasingly prevalent for outsourcing IT functions. The basic feature of offering virtual data center slices to customers has been in use for some time now. So far, customers only get the raw resources, with only little insight and control of their resources. But to let customers build reliable services on top of the rented infrastructure, they need adequate monitoring and control capabilities. In the future, we expect operators to offer such functions to their customers. In this paper, we introduce our approach towards offering a holistic monitoring system to data center customers. It offers generic monitoring information propagation and storage covering various types of resources (network, servers, and applications), all kinds of monitoring information, and all tenants. As virtualized data centers are usually large and multitenant, our solution is built with these properties in mind.

Several traffic monitoring applications may benefit from the availability of efficient mechanisms for approximately tracking smoothed time averages rather than raw counts. This paper provides two contributions in this direction. First, our analysis of Time-decaying Bloom filters, formerly proposed data structures devised to perform approximate Exponentially Weighted Moving Averages on streaming data, reveals two major shortcomings: biased estimation when measurements are read in arbitrary time instants, and slow operation resulting from the need to periodically update all the filter's counters at once. We thus propose a new construction, called On-demand Time-decaying Bloom filter, which relies on a continuous-time operation to overcome the accuracy/performance limitations of the original window-based approach. Second, we show how this new technique can be exploited in the design of high performance stream-based monitoring applications, by developing VoIPSTREAM, a proof-of-concept real-time analysis version of a formerly proposed system for telemarketing call detection. Our validation results, carried out over real telephony data, show how VoIPSTREAM closely mimics the feature extraction process and traffic analysis techniques implemented in the offline system, at a significantly higher processing speed, and without requiring any storage of per-user call detail records.

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In recent times, SIP-based communication systems have become more and more popular (e.g., in open networks, NGN, IMS, etc.). With continuously dropping cost for the usage of such systems (e.g., VoIP, IM, IPTV), many researchers anticipate the amount of unsolicited communication within the network to reach an alarming high level in the near future.Thus, protection of such systems is needed to counter this threat. We present a holistic protection framework for SIP based infrastructures and describe the most recent enhancements of the system.