We present 3MileBeach, a tracing and fault injection platform designed for microservice-based architectures. 3Mile-Beach interposes on the message serialization libraries that are ubiquitous in this environment, avoiding the application code instrumentation that tracing and fault injection infrastructures typically require. 3MileBeach provides message-level distributed tracing at less than 50% of the overhead of the state-of-the-art tracing frameworks, and fault injection that allows higher precision experiments than existing solutions. We measure the overhead of 3MileBeach as a tracer and its efficacy as a fault injector. We qualitatively measure its promise as a platform for tuning and debugging by sharing concrete use cases in the context of bottleneck identification, performance tuning, and bug finding. Finally, we use 3MileBeach to perform a novel type of fault injection - Temporal Fault Injection (TFI), which more precisely controls individual inter-service message flow with temporal prerequisites, and makes it possible to catch an entirely new class of fault tolerance bugs.

As data becomes increasingly distributed, traditional RPC and data serialization limits performance, result in rigidity, and hamper expressivity. We believe that technology trends including high-density persistent memory, high-speed networks, and programmable switches make this the right time to revisit prior research on distributed shared memory, global addressing, and content-based networking. Our vision combines the code mobility of RPC with first-class data references in a global address space by co-designing the OS and the network around pervasive data identity. We have initial results showing the promise of the proposed co-design.

Byte-addressable, non-volatile memory (NVM) presents an opportunity to rethink the entire system stack. We present Twizzler, an operating system redesign for this near-future. Twizzler removes the kernel from the I/O path, provides programs with memory-style access to persistent data using small (64 bit), object-relative cross-object pointers, and enables simple and efficient long-term sharing of data both between applications and between runs of an application. Twizzler provides a clean-slate programming model for persistent data, realizing the vision of Unix in a world of persistent RAM. We show that Twizzler is simpler, more extensible, and more secure than existing I/O models and implementations by building software for Twizzler and evaluating it on NVM DIMMs. Most persistent pointer operations in Twizzler impose less than 0.5 ns added latency. Twizzler operations are up to faster than Unix , and SQLite queries are up to faster than on PMDK. YCSB workloads ran 1.1– faster on Twizzler than on native and NVM-optimized SQLite backends.

Non-Volatile Random Access Memory (NVRAM) technologies are closing the performance gap between traditional storage and memory. However, the integrity of persistent data structures after an unclean shutdown remains a major concern. Logging is commonly used to ensure consistency of NVRAM systems, but it imposes significant performance overhead and causes additional wear out by writing extra data into NVRAM. Our goal is to eliminate the extra writes that are needed to achieve consistency. SSP (i) exploits a novel cache-line-level remapping mechanism to eliminate redundant data copies in NVRAM, (ii) minimizes the storage overheads using page consolidation and (iii) removes failure-atomicity overheads from the critical path, significantly improving the performance of NVRAM systems. Our evaluation results demonstrate that SSP reduces overall write traffic by up to 1.8×, reduces extra NVRAM writes in the critical path by up to 10× and improves transaction throughput by up to 1.6×, compared to a state-of-the-art logging design.

Byte-addressable non-volatile memory (NVM) placed alongside DRAM promises a fundamental shift in software abstractions, yet many approaches to using NVM promise merely incremental improvement by relying on old interfaces and archaic abstractions. We assert that redesigning the core programming model presented by the operating system is vital to best exploiting this technology. We are developing Twizzler, an OS that presents an effective programming model for NVM sufficient to construct persistent data structures that can be easily and globally shared without serialization costs. We consider and evolve a key-value store that runs on Twizzler, and demonstrate how our programming model improves programmability with early experiments indicating performance need not be lost and may be improved.

Increasingly, archival systems are relying on authentication-based techniques that leverage secret-splitting rather than encryption to secure data for long-term storage. Secret-splitting data across multiple independent repositories reduces complexities in key management, eliminates the need for updates due to encryption algorithm deprecation over time, and reduces the risk of insider compromise. While reconstruction of stored data objects is straightforward if a user-maintained index is available, the system must also support disaster recovery incase the index is unavailable. Designing a mechanism for efficient index-free reconstruction, that does not increase the risk of attacker compromise, is a challenge. Reconstruction requires the association of chunks that make up an object, which is the kind of information attackers can use to identify chunks they must steal to illicitly obtain data. We propose two new techniques, the set-subset reconstruction and secret-split secure hash (S3H) reconstruction, which allow chunks of data to be correlated and quickly reconstructed without providing useful information to an attacker. Both techniques operate on the entire collections of secret-split chunks in the archive. While they can efficiently rebuild an entire archive, they are inefficient and impractical for rebuilding single objects, making them useless for attackers that do not have access to all of the data. These techniques can each be tuned to trade-off between reconstruction performance and security, reducing overall runtime from O(NK) (for N objects requiring K recombined chunks each to return the original object) to between O(N) and O(N2). These runtimes are practical for archives containing as many as 107objects for the secret-split secure hash method and 109objects for the set-subset method. Larger archives can run these techniques with manageable runtimes by grouping data into separate smaller collections and running the algorithms on each collection in parallel.

Removing hard drives from a data center may expose sensitive data, such as encryption keys or passwords. To prevent exposure, data centers have security policies in place to physically secure drives in the system, and securely delete data from drives that are removed. Despite advances in security technology and best practices, implementation of these security measures is often done incorrectly. We anticipate that physical security will fail, and fixing the issue after the failure is costly and ineffective.