Abstract

International audienceA key issue for Cloud Computing data-centers is to maximize their profits by minimizing power consumption and SLA violations of hosted applications. In this paper, we propose a resource management framework combining a utility-based dynamic Virtual Machine provisioning manager and a dynamic VM placement manager. Both problems are modeled as constraint satisfaction problems. The VM provisioning process aims at maximizing a global utility capturing both the performance of the hosted applications with regard to their SLAs and the energy- related operational cost of the cloud computing infrastructure. We show several experiments how our system can be controlled through high level handles to make different trade-off between application performance and energy consumption or to arbitrate resource allocations in case of contention

No abstract

Abstract

The Domain Name System (DNS) is an essential infrastructure service on the internet. It provides a worldwide mapping between easily memorizable domain names and numerical IP addresses. Today, legitimate users and malicious applications use this service to locate content on the internet. Yet botnets increasingly rely on DNS to connect to their command and control servers. A widespread approach to detect bot infections inside corporate networks is to inspect DNS traffic using domain C&C blacklists. These are built using a wide range of techniques including passive DNS analysis, malware sandboxing and web content filtering. Using DNS to detect botnets is still an error-prone process; and current blacklist generation algorithms often add innocuous domains that lead to a large number of false positives during detection. This paper presents a new system called Mentor. It implements a scalable, positive DNS reputation system that automatically removes benign entries within a blacklist of botnet C&C domains. Mentor embeds a crawler system that collects statistical features about a suspect domain name, including both web content and DNS properties. It applies supervised learning to a labeled set of known benign and malicious domain names, using its features set in order to build a DNS pruning model. It further processes domain blacklists using this model in order to skim-off benign domains and keep only true malicious domains for detection. We tested our system against a wide set of public botnet blacklists. Experimental results prove the ability of this system to efficiently detect and remove benign domain names with a very low false positives rate.