Resource-conscious scheduling for energy efficiency on multicore processors

Merkel, Andreas; Stoess, Jan; Bellosa, Frank

doi:10.1145/1755913.1755930

Cited by 148 publications

(129 citation statements)

References 28 publications

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“…In the area of OS and runtime scheduling, most studies focus on job co-scheduling to avoid co-locating cache contentious applications together to improve performance and fairness [25,46,14,6,31,21,20]. Approaches to alleviating resource contention and guaranteeing QoS by controlling the execution rate through hardware features or a runtime are proposed [18,29].…”

Section: Related Workmentioning

confidence: 99%

The impact of memory subsystem resource sharing on datacenter applications

Tang

Mars

Vachharajani

et al. 2011

Proceedings of the 38th Annual International Symposium on Computer Architecture

123

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In this paper we study the impact of sharing memory resources on five Google datacenter applications: a web search engine, bigtable, content analyzer, image stitching, and protocol buffer. While prior work has found neither positive nor negative effects from cache sharing across the PARSEC benchmark suite, we find that across these datacenter applications, there is both a sizable benefit and a potential degradation from improperly sharing resources. In this paper, we first present a study of the importance of thread-tocore mappings for applications in the datacenter as threads can be mapped to share or to not share caches and bus bandwidth. Second, we investigate the impact of co-locating threads from multiple applications with diverse memory behavior and discover that the best mapping for a given application changes depending on its co-runner. Third, we investigate the application characteristics that impact performance in the various thread-to-core mapping scenarios. Finally, we present both a heuristics-based and an adaptive approach to arrive at good thread-to-core decisions in the datacenter. We observe performance swings of up to 25% for web search and 40% for other key applications, simply based on how application threads are mapped to cores. By employing our adaptive thread-to-core mapper, the performance of the datacenter applications presented in this work improved by up to 22% over status quo thread-to-core mapping and performs within 3% of optimal.

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Section: Related Workmentioning

confidence: 99%

The impact of memory subsystem resource sharing on datacenter applications

Tang

Mars

Vachharajani

et al. 2011

Proceedings of the 38th Annual International Symposium on Computer Architecture

123

View full text Add to dashboard Cite

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“…However, consolidation of different set of applications comes with interferences amongst them. Existing work suggest that consolidation of applications in a single server increases the contention on the shared resources such as on-chip caches, buses, main memory, CPUs and network [80,84]. This contention results in performance degradation of applications.…”

Section: Heterogeneous Applicationsmentioning

confidence: 99%

Sustainable Cloud Computing

Domdouzis

2015

Green Information Technology

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Energy consumption of the data centers worldwide is rapidly growing fueled by everincreasing demand for Cloud computing applications ranging from social networking to e-commerce. Understandably, ensuring energy-efficiency and sustainability of Cloud data centers without compromising performance is important for both economic and environmental reasons. In order to achieve these objectives, this dissertation develops a cyber-physical multi-tier server and workload management architecture which operates at the local and the global (geo-distributed) data center level.This dissertation devises optimization frameworks for each tier to optimize energy consumption, energy cost and carbon footprint of the data centers. The proposed solutions are aware of various energy management tradeoffs that manifest due to the cyber-physical interactions in data centers, while providing provable guarantee on the solutions' computation efficiency and energy/cost efficiency. The local data center level energy management takes into account the impact of server consolidation on the cooling energy, avoids cooling-computing power tradeoff, and optimizes the total energy (computing and cooling energy) considering the data centers' technology trends (servers' power proportionality and the cooling power efficiency). The global data center level cost management explores the diversity of the data centers to minimize the utility cost while satisfying the carbon cap requirement of the Cloud and while dealing with the adversity of the prediction error on the data center parameters. Finally, the synergy of the local and the global data center energy and cost optimization is shown to help towards achieving carbon neutrality (net-zero) in a cost efficient manner.

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“…These vectors concentrate on the most important resources, CPU and memory bandwidth utilization. This approach of characterization is inspired by the notion of activity vectors, initially introduced for energy-efficient scheduling on multicores [32].…”

Section: Resource Activity Vectorsmentioning

confidence: 99%

“…Additionally, there are several methods for characterizing and modeling thread performance on multicores [32,41], and their possible interference when sharing resources [6,11]. Some of these models are used to aid comprehension and optimization of code performance, while others are used to minimize overall resource contention and performance degradation.…”

Section: General Schedulingmentioning

confidence: 99%

Deployment of query plans on multicores

et al. 2014

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Efficient resource scheduling of multithreaded software on multicore hardware is difficult given the many parameters involved and the hardware heterogeneity of existing systems. In this paper we explore the efficient deployment of query plans over a multicore machine. We focus on shared query systems, and implement the proposed ideas using SharedDB.The goal of the paper is to explore how to deliver maximum performance and predictability, while minimizing resource utilization when deploying query plans on multicore machines. We propose to use resource activity vectors to characterize the behavior of individual database operators. We then present a novel deployment algorithm which uses these vectors together with dataflow information from the query plan to optimally assign relational operators to physical cores. Experiments demonstrate that this approach significantly reduces resource requirements while preserving performance and is robust across different server architectures.

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Resource-conscious scheduling for energy efficiency on multicore processors

Cited by 148 publications

References 28 publications

The impact of memory subsystem resource sharing on datacenter applications

The impact of memory subsystem resource sharing on datacenter applications

Sustainable Cloud Computing

Deployment of query plans on multicores

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