Shared microarchitectural state is a target for side-channel attacks that leverage timing measurements to leak information across security domains. These attacks are further enhanced by speculative execution, which transiently distorts the control and data flow of applications, and by untrusted environments, where the attacker may have complete control over the victim program. Under these conditions, microarchitectural attacks can bypass software isolation mechanisms, and hence they threaten the security of virtually any application running in a shared environment. Numerous approaches have been proposed to defend against microarchitectural attacks, but we lack the means to test them and ensure their effectiveness. The users cannot test them manually because the effects of the defences are not visible to software. Testing the defences by attempting attacks is also suboptimal because the attacks are inherently unstable, and a failed attack is not always an indicator of a successful defence. Moreover, some classes of defences can be disabled at runtime. Hence, we need automated tools that would check the effectiveness of defences, both at design time and at runtime. Yet, as it is common in security, the existing solutions lag behind the developments in attacks. In this thesis, we propose three techniques that check the effectiveness of defences against modern microarchitectural attacks. Revizor is an approach to automatically detect microarchitectural information leakage in commercial black-box CPUs. SpecFuzz is a technique for dynamic testing of applications to find instances of speculative vulnerabilities. Varys is an approach to runtime monitoring of system defences against microarchitectural attacks. We show that with these techniques, we can successfully detect microarchitectural vulnerabilities in hardware and flaws in defences against them; find unpatched instances of speculative vulnerabilities in software; and detect attempts to invalidate system defences.

With the shifting landscape of computing hardware architectures and the emergence of new computing environments (e.g., large main-memory systems, hundreds of CPUs, distributed and virtualized cloud-based resources), state-of-the-art designs of transaction processing systems that rely on conventional wisdom suffer from lost performance optimization opportunities. This dissertation challenges conventional wisdom to rethink the design and implementation of transaction processing systems for modern computing environments.<div><br></div><div>We start by tackling the vertical hardware scaling challenge, and propose a deterministic approach to transaction processing on emerging multi-sockets, many-core, shared memory architecture to harness its unprecedented available parallelism. Our proposed priority-based queue-oriented transaction processing architecture eliminates the transaction contention footprint and uses speculative execution to improve the throughput of centralized deterministic transaction processing systems. We build QueCC and demonstrate up to two orders of magnitude better performance over the state-of-the-art.<br></div><div><br></div><div>We further tackle the horizontal scaling challenge and propose a distributed queue-oriented transaction processing engine that relies on queue-oriented communication to eliminate the traditional overhead of commitment protocols for multi-partition transactions. We build Q-Store, and demonstrate up to 22x improvement in system throughput over the state-of-the-art deterministic transaction processing systems.<br></div><div><br></div><div>Finally, we propose a generalized framework for designing distributed and replicated deterministic transaction processing systems. We introduce the concept of speculative replication to hide the latency overhead of replication. We prototype the speculative replication protocol in QR-Store and perform an extensive experimental evaluation using standard benchmarks. We show that QR-Store can achieve a throughput of 1.9 million replicated transactions per second in under 200 milliseconds and a replication overhead of 8%-25%compared to non-replicated configurations.<br></div>