Oversubscription is an essential cost management strategy for cloud database providers, and its importance is magnified by the emerging paradigm of serverless databases. In contrast to general purpose techniques used for oversubscription in hypervisors, operating systems and cluster managers, we develop techniques that leverage our understanding of how DBMSs use resources and how resource allocations impact database performance. Our techniques are designed to flexibly redistribute resources across database tenants at the node and cluster levels with low overhead. We have implemented our techniques in a commercial cloud database service: Azure SQL Database. Experiments using microbenchmarks, industry-standard benchmarks and real-world resource usage traces show that using our approach, it is possible to tightly control the impact on database performance even with a relatively high degree of oversubscription.

Efficient Top- k query evaluation relies on practices that utilize auxiliary data structures to enable early termination. Such techniques were designed to trade-off complex work in the buffer pool against costly access to disk-resident data. Parallel in-memory Top- k selection with support for early termination presents a novel challenge because computation shifts higher up in the memory hierarchy. In this environment, data scan methods using SIMD instructions and multithreading perform well despite requiring evaluation of the complete dataset. Early termination schemes that favor simplicity require random access to resolve score ambiguity while those optimized for sequential access incur too many object evaluations. In this work, we introduce the concept of rank uncertainty , a measure of work efficiency that enables classifying existing solutions according to their potential for efficient parallel in-memory Top-fc selection. We identify data reordering and layering strategies as those having the highest potential and provide practical guidelines on how to adapt them for parallel in-memory execution (creating the VTA and SLA approaches). In addition, we show that the number of object evaluations can be further decreased by combining data reordering with angle space partitioning (introducing PTA). Our extensive experimental evaluation on varying query parameters using both synthetic and real data, showcase that PTA exhibits between 2 and 4 orders of magnitude better query latency, and throughput when compared to prior work and our optimized algorithmic variants (i.e. VTA, SLA).

Inexpensive DRAMs have created new opportunities for in-memory data analytics. However, the major bottleneck in such systems is high memory access latency. Traditionally, this problem is solved with large cache hierarchies that only benefit regular applications. Alternatively, many data-intensive applications exhibit irregular behavior. Hardware multithreading can better cope with high latency seen in such applications. This article implements a multithreaded prototype (MTP) on FPGAs for the relational selection operator that exhibits control flow irregularity. On a standard TPC-H query evaluation, MTP achieves a bandwidth utilization of 83%, while the CPU and the GPU implementations achieve 61% and 64%, respectively. Besides being bandwidth efficient, MTP is also 14.2× and 4.2× more power efficient than CPU and GPU, respectively.

Processing-In-Memory (PIM) is an increasingly popular architecture aimed at addressing the 'memory wall' crisis by prioritizing the integration of processors within DRAM. It promotes low data access latency, high bandwidth, massive parallelism, and low power consumption. The skyline operator is a known primitive used to identify those multi-dimensional points offering optimal trade-offs within a given dataset. For large multidimensional dataset, calculating the skyline is extensively compute and data intensive. Although, PIM systems present opportunities to mitigate this cost, their execution model relies on all processors operating in isolation with minimal data exchange. This prohibits direct application of known skyline optimizations which are inherently sequential, creating dependencies and large intermediate results that limit the maximum parallelism, throughput, and require an expensive merging phase.