| 192 | | |
| | 192 | ---- |
| | 193 | |
| | 194 | == DoS Attack Detection & DoS Attacks Exploiting WiMAX System Parameters == |
| | 195 | |
| | 196 | Ilker Ozcelik, Holcombe Department of Electrical & Computer Engineering [[br]] |
| | 197 | Lu Yu, Clemson University |
| | 198 | |
| | 199 | '''Abstract''' |
| | 200 | |
| | 201 | The poster comprises two parts. In part one, we are collecting the Internet traffic signature on !OpenFlow to use as backgroutnd traffic. By using the real background traffic, we are investigating the effectiveness of theoretical DDoS attack detection techniques on GENI. We are also trying to evaluate our proposed equation of necessary traffic for DDoS attack. Part two focuses on analyzing DoS attacks that exploit WiMAX system parameter settings. We concentrate on parameters concerning bandwidth contention resolution in IEEE 802.16 standards. We use analysis of variance (ANOVA) to find how parameter settings affect the ability of DoS attackers to monopolize network bandwidth. We are carrying out a DoS attack against WiMAX on GENI ORBIT and collecting the data used for ANOVA. |
| | 202 | |
| | 203 | ---- |
| | 204 | |
| | 205 | == Evaluating Schemes for Adapting to Cloud Dynamics using GENI == |
| | 206 | |
| | 207 | Ashiwan Sivakumar, Purdue University [[br]] |
| | 208 | Shankaranarayanan PN, Purdue University [[br]] |
| | 209 | Mohammad Hajjat, Purdue University [[br]] |
| | 210 | Dr. Sanjay Rao, Purdue University [[br]] |
| | 211 | |
| | 212 | '''Abstract''' |
| | 213 | Enterprises are increasingly deploying their applications in the cloud given the cost-saving advantages, and the potential to geo-distribute applications to ensure resilience and better service experience. Latency and availability are critical with such performance sensitive applications. A key problem then is to meet the stringent response time requirements of enterprise applications in the cloud. We build a system that we term Dealer which for each component, dynamically splits transactions among its replicas in different data-centers. It adapts to sudden changes in delay across components and routes requests to replicas of the components in a different data-center. In doing so, Dealer seeks to minimize user response times, and takes component performance, as well as intra-data- center and inter-data-center communication latencies into account. We have integrated the system with a performance sensitive trading application called Daytrader in GENI. [[br]] |
| | 214 | Our approach to evaluate the system makes use of the controlled and repeatable environment provided by GENI. The experiments that we have conducted on GENI aim at emulating sudden spikes in delay between components. We have studied the dynamic response time of Dealer by subjecting it to a Step Up input reference waveform. We have also compared the user response times in a Multi cloud environment on ProtoGENI with and without dealer. We present the results of the evaluation experiments conducted on GENI. |
| | 215 | |
| | 216 | More Information: [[br]] |
| | 217 | http://docs.lib.purdue.edu/cgi/viewcontent.cgi?article=1415&context=ecetr |
