arXiv subjects

Vinay Banakar

Publications and source records attributed to Vinay Banakar.

5 recordsLinked to original sources

arXiv2026

OBASE: Object-Based Address-Space Engineering to Improve Memory Tiering

Hardware and OS mechanisms for memory tiering are widely deployed, yet datacenters still overprovision DRAM. The root cause is hotness fragmentation: allocators place objects by size rather than access pattern, so hot and cold objects become interleaved within the same pages. A single hot object marks its page as active, trapping surrounding cold data in expensive DRAM. Our analysis of Google production workloads shows that up to 97% of the bytes in active pages are cold and unreclaimable. We propose address-space engineering: dynamically reorganizing virtual memory so that hot objects cluster into uniformly hot pages and cold objects into uniformly cold pages. We present OBASE, a compiler-runtime system for unmanaged languages that serves as an object-aware frontend for page-aware OS backends. OBASE tracks accesses via lightweight pointer instrumentation and migrates objects at runtime using a lock-free protocol that is safe under concurrency. By reorganizing the address space, OBASE enables unmodified backends (kswapd, TMO, TPP, Memtis) to tier memory effectively. Across ten concurrent data structures, six backends, and production traces from Meta and Twitter, OBASE improves page utilization by 2-4x and reduces memory footprint by up to 70%, with only 2-5% overhead.

cs.OS↗

arXiv2025

Tidying Up the Address Space

Memory tiering in datacenters does not achieve its full potential due to hotness fragmentation -- the intermingling of hot and cold objects within memory pages. This fragmentation prevents page-based reclamation systems from distinguishing truly hot pages from pages containing mostly cold objects, fundamentally limiting memory efficiency despite highly skewed accesses. We introduce address-space engineering: dynamically reorganizing application virtual address spaces to create uniformly hot and cold regions that any page-level tiering backend can manage effectively. HADES demonstrates this frontend/backend approach through a compiler-runtime system that tracks and migrates objects based on access patterns, requiring minimal developer intervention. Evaluations across ten data structures achieve up to 70% memory reduction with 3% performance overhead, showing that address space engineering enables existing reclamation systems to reclaim memory aggressively without performance degradation.

cs.OS↗

arXiv2023

WiscSort: External Sorting For Byte-Addressable Storage

We present WiscSort, a new approach to high-performance concurrent sorting for existing and future byte-addressable storage (BAS) devices. WiscSort carefully reduces writes, exploits random reads by splitting keys and values during sorting, and performs interference-aware scheduling with thread pool sizing to avoid I/O bandwidth degradation. We introduce the BRAID model which encompasses the unique characteristics of BAS devices. Many state-of-the-art sorting systems do not comply with the BRAID model and deliver sub-optimal performance, whereas WiscSort demonstrates the effectiveness of complying with BRAID. We show that WiscSort is 2-7x faster than competing approaches on a standard sort benchmark. We evaluate the effectiveness of key-value separation on different key-value sizes and compare our concurrency optimizations with various other concurrency models. Finally, we emulate generic BAS devices and show how our techniques perform well with various combinations of hardware properties.

cs.DB↗

arXiv2020

Understanding and Benchmarking the Impact of GDPR on Database Systems

The General Data Protection Regulation (GDPR) provides new rights and protections to European people concerning their personal data. We analyze GDPR from a systems perspective, translating its legal articles into a set of capabilities and characteristics that compliant systems must support. Our analysis reveals the phenomenon of metadata explosion, wherein large quantities of metadata needs to be stored along with the personal data to satisfy the GDPR requirements. Our analysis also helps us identify new workloads that must be supported under GDPR. We design and implement an open-source benchmark called GDPRbench that consists of workloads and metrics needed to understand and assess personal-data processing database systems. To gauge the readiness of modern database systems for GDPR, we follow best practices and developer recommendations to modify Redis, PostgreSQL, and a commercial database system to be GDPR compliant. Our experiments demonstrate that the resulting GDPR compliant systems achieve poor performance on GPDR workloads, and that performance scales poorly as the volume of personal data increases. We discuss the real-world implications of these findings, and identify research challenges towards making GDPR compliance efficient in production environments. We release all of our software artifacts and datasets at http://www.gdprbench.org

cs.DB↗

arXiv2019

Analyzing the Impact of GDPR on Storage Systems

The recently introduced General Data Protection Regulation (GDPR) is forcing several companies to make significant changes to their systems to achieve compliance. Motivated by the finding that more than 30% of GDPR articles are related to storage, we investigate the impact of GDPR compliance on storage systems. We illustrate the challenges of retrofitting existing systems into compliance by modifying Redis to be GDPR-compliant. We show that despite needing to introduce a small set of new features, a strict real-time compliance (eg logging every user request synchronously) lowers Redis' throughput by 20x. Our work reveals how GDPR allows compliance to be a spectrum, and what its implications are for system designers. We discuss the technical challenges that need to be solved before strict compliance can be efficiently achieved.

cs.DC↗