Memory safety remains at risk for programs written in unsafe languages like C. Pointer-checking schemes provide memory safety protection by attaching metadata for each pointer and checking them before dereference. Previously, sanitizers maintaining large per-pointer metadata (e.g., pointer bounds) were stuck with shadow memory for metadata storage, which incurs high overhead. Although fat pointers (i.e., instrumenting programs to inline metadata with pointers) incur less overhead, they introduce incompatibility issues to the instrumented programs, and are thus not considered by software-only sanitizers yet. In this paper, we push the status quo on adopting fat pointers for software-only pointer checking schemes and evaluate the benefit of this approach. We present PSan (short for “Pointer Sanitizer”), the first memory safety sanitizer that enables both inline and shadow memory metadata simultaneously in the same program. To reduce the overhead from shadow memory, PSan uses whole-program analysis and transformation to inline the metadata whenever possible, while using shadow memory only when necessary for compatibility. PSan-instrumented programs preserve binary compatibility with third-party uninstrumented code. In addition, PSan's framework decouples metadata management from checking, facilitating its augmentation with additional checkers. We evaluate the benefit of metadata inlining and observe that PSan's hybrid scheme reduces the runtime and memory overhead. Specifically, PSan incurs 40% lower overhead than popular memory checker SoftBoundCETS, which utilizes only shadow memory. Predictably, using inline metadata has a higher performance improvement when it can be applied to the majority of pointers in the program.

Bounds compression for fat pointers can reduce the memory and performance overhead of maintaining pointer bounds and is necessary for efficient hardware implementation. However, compression can introduce inaccuracy to the bounds, making certain out-of-bounds accesses undetectable. Although the security threat can be mitigated by padding the objects, no known mitigations can detect these out-of-bounds accesses deterministically.

Programming languages like C and C++ are not memory-safe because they provide programmers with low-level pointer manipulation primitives. The incorrect use of these primitives can result in bugs and security vulnerabilities: for example, spatial memory safety errors can be caused by dereferencing pointers outside the legitimate address range belonging to the corresponding object. While a range of schemes to provide protection against these vulnerabilities have been proposed, they all suffer from the lack of one or more of low performance overhead, compatibility with legacy code, or comprehensive protection for all objects and subobjects.

Multicast supporting non-uniform receiving rates is an effective means of data dissemination to receivers with diversified bandwidth availability. Designing efficient rate control, routing and capacity allocation to achieve optimal multirate multicast has been a difficult problem in fixed wireline networks, let alone wireless networks with random channel fading and volatile node mobility. The challenge escalates if we consider also the selfishness of users who prefer to relay data for others with strong social ties. Such social selfishness of users is a new constraint in network protocol design. Its impact on efficient multicast in wireless networks has yet to be explored especially when multiple receiving rates are allowed. In this paper, we design an efficient, social-aware multirate multicast scheme that can maximize the overall utility of socially selfish users in a wireless network, and its distributed implementation. We model social preferences of users as differentiated costs for packet relay, which are weighted by the strength of social tie between the relay and the destination. Stochastic Lyapunov optimization techniques are utilized to design optimal scheduling of multicast transmissions, which are combined with multi-resolution coding and random linear network coding. With rigorous theoretical analysis, we study the optimality, stability, and complexity of our algorithm, as well as the impact of social preferences. Empirical studies further confirm the superiority of our algorithm under different social selfishness patterns.

In cognitive radio networks, the occupation patterns of the primary users can be very dynamic, which makes optimization (e.g., utility maximization) of data dissemination among secondary users difficult. Even under the assumption that all secondary users are fully collaborative, the optimization requires cross-layer decision making which is challenging. The challenge escalates if users are socially selfish, who prefer to relay data only to those other users with whom there are social ties. Such social selfishness of users translates into new constraints on network protocol design. There has been no study so far on the impact of social selfishness on data dissemination in cognitive radio networks. In this paper, we consider social selfishness of secondary users, and propose the design of a joint end-to-end rate control, routing, and channel allocation protocol which can maximize the overall throughput utility of multi-session unicast in cognitive radio networks. We give a distributed implementation of the protocol. Based on a Lyapunov optimization framework, we address social preferences of users using differentiated buffer sizes and relay rates for different data sessions, and apply back-pressure based transmission scheduling to achieve guaranteed utility optimality. A unique contribution of our Lyapunov optimization is that only a finite-sized buffer is required at each user node, which sets our design apart from other designs in existing literature where they assume infinite buffers. We investigate the the optimality of our protocol and the impact of user social selfishness using both theoretical analysis and extensive simulations.

The recent surge of large-scale peer-to-peer (P2P) applications has brought huge amounts of P2P traffic, which significantly changes the Internet traffic pattern and increases the traffic-relay cost at the Internet Service Providers (ISPs). To alleviate the stress on networks, localized peer selection has been proposed that advocates neighbor selection within the same network (AS or ISP) to reduce the cross-ISP traffic. Nevertheless, localized peer selection may potentially lead to the downgrade of downloading speed at the peers, rendering a non-negligible tradeoff between the downloading performance and traffic localization in the P2P system. Aiming at effective peer selection strategies that achieve any desired Pareto optimum in face of the tradeoff, in this paper, we characterize the performance and locality tradeoff as a multi-objective b-matching optimization problem. In particular, we first present a generic maximum weight b-matching model that characterizes the tit-for-tat in BitTorrent-like peer selection. We then introduce multiple optimization objectives into the model, which effectively characterize the performance and locality tradeoff using simultaneous objectives to optimize. We also design fully distributed peer selection algorithms that can effectively achieve any desired Pareto optimum of the global multi-objective optimization, that represents a desired tradeoff point between performance and locality in the entire system. Our models and algorithms are supported by rigorous analysis and extensive simulations.