The ever increasing demand for Internet traffic, storage and processing requires an ever increasing amount of hardware resources. In addition to this, infrastructure providers over-provision system architectures to serve users at peak times without performance delays. Over-provisioning leads to underutilization and thus to unnecessary power consumption. Therefore, there is a need for workload management strategies to map and schedule different services simultaneously in an energy-efficient manner without compromising performance, specially for heterogeneous micro-server architectures. This requires statistical models of how services interfere with each other, thereby affecting both performance and energy consumption. Indeed, the performance-energy behavior when mixing workloads is not well understood. This paper presents an interference analysis for heterogeneous workloads (i.e., CPU- and memory-intensive) on a big.LITTLE MPSoC architecture. We employ state-of-the-art tools to generate multiple single-application mappings and characterize the interference among two different services. We observed a performance degradation factor between 1.1 and 2.5. For some configurations, executing on different clusters resulted in reduced energy consumption with no performance penalty. This kind of detailed analysis give us first insights towards more general models for future workload management systems.
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:85456 |
| Date | 11 May 2023 |
| Creators | Hähnel, Markus, Arega, Frehiwot Melak, Dargie, Waltenegus, Khasanov, Robert, Castrillo, Jeronimo |
| Publisher | IEEE |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/acceptedVersion, doc-type:conferenceObject, info:eu-repo/semantics/conferenceObject, doc-type:Text |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | 978-1-5386-2784-6, 10.1109/INFCOMW.2017.8116415, info:eu-repo/grantAgreement/Deutsche Forschungsgemeinschaft/Sonderforschungsbereich/164481002//Highly Adaptive Energy-Efficient Computing/HAEC |
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