The Teleportation Design Pattern for Hardware Transactional Memory

We identify a design pattern for concurrent data structures, called teleportation, that uses best- effort hardware transactional memory to speed up certain kinds of legacy concurrent data struc- tures. Teleportation unifies and explains several existing data structure designs, and it serves as the basis for novel approaches to reducing the memory traffic associated with fine-grained locking, and with hazard pointer management for memory reclamation

Understanding and Optimizing Flash-based Key-value Systems in Data Centers

Flash-based key-value systems are widely deployed in today’s data centers for providing high-speed data processing services. These systems deploy flash-friendly data structures, such as slab and Log Structured Merge(LSM) tree, on flash-based Solid State Drives(SSDs) and provide efficient solutions in caching and storage scenarios. With the rapid evolution of data centers, there appear plenty of challenges and opportunities for future optimizations. In this dissertation, we focus on understanding and optimizing flash-based key-value systems from the perspective of workloads, software, and hardware as data centers evolve. We first propose an on-line compression scheme, called SlimCache, considering the unique characteristics of key-value workloads, to virtually enlarge the cache space, increase the hit ratio, and improve the cache performance. Furthermore, to appropriately configure increasingly complex modern key-value data systems, which can have more than 50 parameters with additional hardware and system settings, we quantitatively study and compare five multi-objective optimization methods for auto-tuning the performance of an LSM-tree based key-value store in terms of throughput, the 99th percentile tail latency, convergence time, real-time system throughput, and the iteration process, etc. Last but not least, we conduct an in-depth, comprehensive measurement work on flash-optimized key-value stores with recently emerging 3D XPoint SSDs. We reveal several unexpected bottlenecks in the current key-value store design and present three exemplary case studies to showcase the efficacy of removing these bottlenecks with simple methods on 3D XPoint SSDs. Our experimental results show that our proposed solutions significantly outperform traditional methods. Our study also contributes to providing system implications for auto-tuning the key-value system on flash-based SSDs and optimizing it on revolutionary 3D XPoint based SSDs

Leveraging Processor Features for System Security

Errors in hardware and software lead to vulnerabilities that can be exploited by attackers. Proposed exploit mitigation techniques can be broadly categorized into two: software-only techniques and techniques that propose specialized hardware extensions. Software-only techniques can be implemented on existing hardware, but typically suffer from impractically high overheads. On the other hand, specialized hardware extensions, while improving performance, in practice require a long time to be incorporated into production hardware. In this dissertation, we propose adapting existing processor features to provide novel and low-overhead security solutions. In the first part of the dissertation, we show how modern hardware features can be used to provide efficient memory safety. One component of memory safety that has become important in recent years is temporal memory safety. Temporal memory safety techniques are used to detect memory errors such as use-after-free errors. This dissertation proposes a temporal memory safety technique that takes advantage of pointer authentication hardware to significantly reduce the memory and runtime overhead of traditional temporal safety techniques. Providing complete memory safety on resource constrained devices is expensive, therefore we propose software-based fault isolation (sandboxing) as an efficient alternative to constrain attackers’ access to code and data in embedded systems. We show how we can use the memory protection unit (MPU) hardware available in many embedded devices along with a small trusted runtime to build a low-overhead sandboxing mechanism. In the second part of the dissertation, we show how hardware performance counters in modern processors can be used to detect rowhammer attacks. Our technique detects rowhammer attacks by monitoring for high locality memory accesses out of the last-level cache using hardware performance counters. The technique accurately detects rowhammer attacks with a low performance overhead and without requiring hardware modifications.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/149852/1/zaweke_1.pd

Enabling Data Security and Privacy for Database Services in the Cloud

Substantial advances in cloud technologies have made outsourcing data to the cloud highly beneficial today (e.g., costs savings, scalability, provisioning time). However, strong concerns from private companies and public institutions about the security of the outsourced data still hamper the adoption of cloud solutions. This reluctance is fed by frequent massive data breaches either caused by external attacks against cloud service providers or by negligent or opaque practices from the service provider itself. For broader adoption of cloud services, this dissertation addresses the data security and privacy concerns in the cloud setting. The goal is to ensure security and privacy of outsourced data while maintaining the ability to execute queries efficiently. Security/privacy comes at a cost of functionality/performance. Therefore, we seek for a proper balance in the space of security, privacy, functionality, and performance. This dissertation works the problems of range query execution over encrypted data, privacy preserving data mining in the context of environmental sustainability studies, and access privacy in the cloud. To enable efficient and secure range query processing over traditional databases, we introduce PINED-RQ, a highly efficient and differentially private range query execution framework that constructs a novel differentially private index over an outsourced database. Second, this dissertation presents a comprehensive study of the environmental sustainability metrics. Our contributions in this context are twofold: 1) to better evaluate the environmental impacts of the industrial processes privately, we formally define privacy preserving certification paradigm and develop a framework that enables untrusted third party to certify parties based on a well agreed upon set of criteria. 2) to explore the privacy concerns over publicizing the industrial activities in the form of life cycle assessment (LCA) computations, which is a standard way of evaluating an impact of a product and service. This dissertation initiates a study to explore privacy and security challenges that prevent organizations from making public disclosures about their activities. Finally, this dissertation explores access privacy in the cloud setting. We design and develop TaoStore, a highly efficient and practical cloud data store, which secures data confidentiality and hides access patterns from adversaries. Additionally, we propose a new ORAM security model, called aaob-security, which considers completely asynchronous network communication and concurrent processing of requests. This dissertation shows that it is possible to deliver practical and high-performance data services in the cloud without sacrificing securityand privacy if the requirements of each application are analyzed correctly and a correct balance is found in the space of security, privacy, functionality, and performance