As energy consumption is becoming critical in Cloud data centers, Cloud providers are adopting energy-efficient virtual machines management systems. These systems essentially rely on "what-if" analysis to determine what the consequence of their actions would be and to choose the best one according to a number of metrics. However, modeling energy consumption of simple operations such as starting a new VM or live-migrating is complicated by the fact that multiple phenomena occur. It is therefore important to identify which factors influence energy consumption before proposing any new model. We claim in this paper that one critical parameter is the host configuration, characterized by the number of VMs it is currently executing. Based on this observation, we present an energy model that provides energy estimation associated with VM management operations, such as VMs placement, VM start up and VM migration. The average relative estimation error is lower than 10% using the transactional web benchmark TPC-W, making it a good candidate for driving the actions of future energy-aware cloud management systems.

No abstract

No abstract

An effective Distributed Denial of Service (DDoS) defense mechanism must guarantee legitimate users access to an Internet service masking the effects of possible attacks. That is, it must be able to detect threats and discard malicious packets in a online fashion. Given that emerging data streaming technology can enable such mitigation in an effective manner, in this paper we present STONE, a streambased DDoS defense framework, which integrates anomalybased DDoS detection and mitigation with scalable data streaming technology.With STONE, the traffic of potential targets is analyzed via continuous data streaming queries maintaining information used for both attack detection and mitigation. STONE provides minimal degradation of legitimate users traffic during DDoS attacks and it also faces effectively flash crowds. Our preliminary evaluation based on an implemented prototype and conducted with real legitimate and malicious traffic traces shows that STONE is able to provide fast detection and precise mitigation of DDoS attacks leveraging scalable data streaming technology.

No abstract