Communications - July 2011

Sequential-read workloads are similar, but the constants depend strongly on the per byte processing required. Traditional cluster architectures retain a place for CPU-bound workloads, but we do note that architectures such as IBM’s BlueGene successfully apply large numbers of low-power, efficient processors to many supercomputing applications—but they augment their wimpy processors with custom floating point units to do so.

Our definition of “total cost of ownership” ignores several notable costs: In comparison to traditional architectures, FAWN should reduce power and cooling infrastructure but may increase network-related hardware and power costs due to the need for more switches. Our current hardware prototype improves work done per volume, thus reducing costs associated with datacenter rack or floor space. Finally, our analysis assumes that cluster software developers can engineer away the human costs of management—an optimistic assumption for all architectures. We similarly ignore issues such as ease of programming, though we selected an x86-based wimpy platform for ease of development.

6. RELATED WoRK

Several projects are using low-power processors for datacenter workloads to reduce energy consumption. 5, 8, 14, 19 These systems leverage low-cost, low-power commodity components for datacenter systems, similarly arguing that this approach can achieve the highest work per dollar and per Joule. More recently, ultra-low power server systems have become commercially available, with companies such as SeaMicro, Marvell, Calxeda, and ZT Systems producing low-power datacenter computing systems based on Intel Atom and ARM platforms.

FAWN builds upon these observations by demonstrating the importance of re-architecting the software layers in obtaining the potential energy efficiency such hardware can provide.

7. ConCLuSIon

The FAWN approach uses nodes that target the “sweet spot” of per node energy efficiency, typically operating at about half the frequency of the fastest available CPUs. Our experience in designing systems using this approach, often coupled with fast flash memory, has shown that it has substantial potential to improve energy efficiency, but that these improvements may come at the cost of re-architecting software or algorithms to operate with less memory, slower CPUs, or the quirks of flash memory: The FAWN-KV key-value system presented here is one such example. By successfully adapting the software to this efficient hardware, our then four-year-old FAWN nodes delivered over an order of magnitude more queries per Joule than conventional disk-based systems.

Our ongoing experience with newer FAWN-style systems shows that its energy efficiency benefits remain achievable, but that further systems challenges—such as high kernel I/O overhead—begin to come into play. In this light, we view our experience with FAWN as a potential harbinger of the systems challenges that are likely to arise for future many-core energy-efficient systems.

Acknowledgments

This work was supported in part by gifts from Network Appliance, Google, and Intel Corporation, and by grant CCF-0964474 from the National Science Foundation, as well as graduate fellowships from NSF, IBM, and APC. We extend our thanks to our OSDI and SOSP reviewers, Vyas Sekar, Mehul Shah, and to Lorenzo Alvisi for shepherding the work for SOSP. Iulian Moraru provided feedback and performance-tuning assistance.

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