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When: 3:00pm--4:00pm, Feb 8
Where: 3105 EB
Who: Jingshu Chen
Abstract
Existing approaches for verifying self-stabilization with a symbolic model checker have relied on the use of weak fairness. We point out that this approach has limited scalability. To overcome this limitation, first, we show that if self stabilization is possible without fairness then cost of verifying self-stabilization is substantially lower. In fact, we observe from several case studies that cost of verification under weak fairness is more than 1000 times that of the cost without fairness. For the case where weak fairness is essential for self-stabilization, we identify two approaches for improving scalability: (1) decomposition and (2) utilizing the weaker version of self-stabilization, namely weak stabilization.
When: 3:00pm--4:00pm, Feb 22
Where: 3105 EB
Who: Andres Ramirez
Abstract
When: 3:00pm--4:00pm, Mar 22
Where: 3105 EB
Who: Daniel Couvertier
Abstract
Group selection, although controversial in biology, has been shown to be very effective in evolving cooperative behavior in artificial systems. A key issue in cooperative task completion is team composition. Prior studies have addressed two ends of a spectrum, with homogeneous teams on one end and heterogeneous teams on the other. In this paper we explore a space in between. In biased group selection, subpopulations compete against one another with respect to a cooperative task, but an external bias favors the genes of those individuals actually participating in the task. We evaluate this selection model on a cooperative predation task in digital organisms, where feasible solutions can be carried out by either homogeneous or heterogeneous teams. Our results show that, consistent with earlier studies, homogeneous teams tend to find better overall solutions than their heterogeneous counterparts. However, we also observed that populations comprising teams with some degree of heterogeneity found solutions more frequently than in the homogeneous case. Effectively, while evolution pushed heterogeneous teams toward functional homogeneity for this particular task, heterogeneity with a selection bias proved more effective at exploring the search space.
When: 3:00pm--4:00pm, Apr 5
Where: 3105 EB
Who: Fei Chen
Abstract
Firewalls have been widely deployed on the Internet for securing private networks. A firewall checks each incoming or outgoing packet to decide whether to accept or discard the packet based on its policy. Optimizing firewall policies is crucial for improving network performance. Prior work on firewall optimization focuses on either intra-firewall or inter-firewall optimization within one administrative domain where the privacy of firewall policies is not a concern. This paper explores inter-firewall optimization across administrative domains for the first time. The key technical challenge is that firewall policies cannot be shared across domains because a firewall policy contains confidential information and even potential security holes, which can be exploited by attackers. In this paper, we propose the first cross-domain privacy-preserving cooperative firewall policy optimization protocol. Specifically, for any two adjacent firewalls belonging to two different administrative domains, our protocol can identify in each firewall the rules that can be removed because of the other firewall. The optimization process involves cooperative computation between the two firewalls without any party disclosing its policy to the other. We implemented our protocol and conducted extensive experiments. The results on real firewall policies show that our protocol can remove as many as 49% of the rules in a firewall whereas the average is 19.4%. The communication cost is less than a few hundred KBs. Our protocol incurs no extra online packet processing overhead and the offline processing time is less than a few hundred seconds.
When: 3:00pm--4:00pm, Apr 19
Where: 3105 EB
Who: Amir Khakpour
Abstract
When: 3:00pm--4:00pm, May 3
Where: 3105 EB
Who: TO be confirmed
Abstract