Analytic workloads on terabyte data-sets are often run in the cloud, where application and storage servers are separate and connected via network. In order to saturate the storage bandwidth and to hide the long storage latency, such a solution requires an expensive server cluster with sufficient aggregate DRAM capacity and hardware threads. An alternative solution is to push the query computation into storage servers. In this paper we present an in-storage Analytics QUery Offloading MAchiNe (AQUOMAN) to “offload” most SQL operators, including multi-way joins, to SSDs. AQUOMAN executes Table Tasks, which apply a static dataflow graph of SQL operators to relational tables to produce an output table. Table Tasks use a streaming computation model, which allows AQUOMAN to process queries with a reasonable amount of DRAM for intermediate results. AQUOMAN is a general analytic query processor, which can be integrated in the database software stack transparently. We have built a prototype of AQUOMAN in FPGAs, and using TPC-H benchmarks on 1TB data sets, shown that a single instance of 1TB AQUOMAN disk, on average, can free up 70% CPU cycles and reduce DRAM usage by 60%. One way to visualize this saving is to think that if we run queriesmore »
FoggyCache: Cross-Device Approximate Computation Reuse
Mobile and IoT scenarios increasingly involve interactive
and computation intensive contextual recognition. Existing
optimizations typically resort to computation offloading or
simplified on-device processing.
Instead, we observe that the same application is often
invoked on multiple devices in close proximity. Moreover,
the application instances often process similar contextual
data that map to the same outcome.
In this paper, we propose cross-device approximate computation
reuse, which minimizes redundant computation by
harnessing the “equivalence” between different input values
and reusing previously computed outputs with high confidence. We devise adaptive locality sensitive hashing (A-LSH)
and homogenized k nearest neighbors (H-kNN). The former
achieves scalable and constant lookup, while the latter
provides high-quality reuse and tunable accuracy guarantee.
We further incorporate approximate reuse as a service,
called FoggyCache, in the computation offloading runtime.
Extensive evaluation shows that, when given 95% accuracy
target, FoggyCache consistently harnesses over 90% of reuse
opportunities, which translates to reduced computation latency
and energy consumption by a factor of 3 to 10.
- Award ID(s):
- 1815115
- Publication Date:
- NSF-PAR ID:
- 10122201
- Journal Name:
- Mobicom'18
- Volume:
- 2018
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Publisher's Version of Record
- https://doi.org/10.1145/3241539.3241557
