“Big, Medium, Little”: Reaching Energy Proportionality with Heterogeneous Computing Scheduler

Villebonnet, Violaine; Costa, Georges Da; Lefèvre, Laurent; Pierson, Jean‐Marc; Stolf, Patricia

doi:10.1142/s0129626415410066

Cited by 13 publications

(18 citation statements)

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“…The strong aspect is that it does not rely on a specific processor design. This idea has been introduced previously [9], and its feasibility has been studied [3]. The present work implements scheduling policies that take into account both benefits and drawbacks of using independent machines.…”

Section: Related Work On Energy Proportionalitymentioning

confidence: 99%

“…Each machine type is profiled by running the target application, and the best resources combinations for all application performance rates are computed. Our previous work introduced the concept of heterogeneous energy proportional infrastructure, named "Big,Medium,Little" (BML) [3]. The present work extends it by providing a scheduler that handles dynamic reconfiguration decisions, which consist in dynamic resources management with switch on and off actions, whose time and energy overheads are taken into account, to achieve energy proportionality while respecting QoS requirements.…”

Section: Introductionmentioning

confidence: 99%

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Dynamically Building Energy Proportional Data Centers with Heterogeneous Computing Resources

Villebonnet¹,

Costa²,

Lefèvre³

et al. 2016

2016 IEEE International Conference on Cluster Computing (CLUSTER)

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OATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible. This is an author-deposited version published in : http://oatao.univ-toulouse.fr/ Eprints ID : 18804The contribution was presented at CLUSTER 2016 :http://www.ieeecluster2016.org/ Abstract-As the number of data centers increases, it is urgent to reduce their energy consumption. Although servers are becoming more energy-efficient, their idle consumption remains high, which is an issue as data centers are often over-provisioned. This work proposes a novel approach for building data centers with heterogeneous machines carefully chosen for their performance and energy efficiency ratios. We focus on web applications whose load varies over time, and design a scheduler that dynamically reconfigures the infrastructure, by migrating applications and switching machines on or off, so that the energy consumed by the data center is proportional to the load. Experiments evaluate the approach and show the energy savings achieved by our heterogeneous data center design and management while satisfying Quality of Service (QoS) constraints.

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Section: Related Work On Energy Proportionalitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dynamically Building Energy Proportional Data Centers with Heterogeneous Computing Resources

Villebonnet¹,

Costa²,

Lefèvre³

et al. 2016

2016 IEEE International Conference on Cluster Computing (CLUSTER)

Self Cite

View full text Add to dashboard Cite

show abstract

“…The strong aspect is that it does not rely on a specific processor design. This idea has been introduced previously [10], and its feasibility has been studied [3]. The present work implements a BML infrastructure with scheduling policies that take into account both benefits and drawbacks of using independent machines.…”

Section: Related Work On Energy Proportionalitymentioning

confidence: 99%

“…Each server type is profiled by running the target application, and the best server combinations for all application performance rates are then computed. Our previous work introduced the concept of a heterogeneous energy proportional infrastructure, named "Big,Medium,Little" (BML) [3]. The present work extends it by providing a scheduler that handles reconfiguration decisions, such as dynamic application migrations and management of computing resources with switch on and off actions.…”

Section: Introductionmentioning

confidence: 99%

Energy Proportionality in Heterogeneous Data Center Supporting Applications with Variable Load

Villebonnet¹,

Costa²,

Lefèvre³

et al. 2016

2016 IEEE 22nd International Conference on Parallel and Distributed Systems (ICPADS)

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OATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible. Abstract-The increasing number of data centers raises serious concerns regarding their energy consumption. Although servers have become more energy-efficient over time, their idle consumption remains high, which is an issue as resources in data centers are often over-provisioned. This work proposes a novel approach for building data centers so that their energy consumption is proportional to load. A data center hence comprises heterogeneous machines carefully chosen for their performance and energy efficiency ratios. We focus on web applications whose load varies over time and design a scheduler that dynamically reconfigures the infrastructure to minimize its energy consumption according to current load and application requirements. Based on load forecasts, it takes reconfiguration decisions and performs actions such as migrating applications and switching machines on or off. The approach is evaluated considering a data center with heterogeneous resources, and the experiments show how to adjust the parameters of scheduling policies to save the most energy while satisfying Quality of Service (QoS) constraints.

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“…is la er e ect is commonly referred to as the breakdown of Dennard's scaling, which forces designs to operate only a fraction of the whole system, leaving the remaining design in a dark silicon state [8,10]. To address this, heterogeneous multiprocessors (HMPs) have emerged over other designs, especially in mobile devices [17,33], where keeping within a strict energy envelope while still being able to deliver performance on demand is crucial.To be able to deliver high performance through Memory Level Parallelism (MLP) and instruction level parallelism (ILP), an Out-of-Order (OoO) core is commonly used that has large caches, does aggressive speculation (branch predictors, prefetchers) and masks memory latency at the cost of signi cantly increased design complexity, area and power requirements. On the other hand, In-Order (InO) cores aim at conserving energy through a simpler and smaller design, at the expense of performance and lower operating frequency.…”

mentioning

confidence: 99%

Nucleus

Vougioukas

Sandberg

Diestelhorst

et al. 2017

ACM Trans. Embed. Comput. Syst.

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Heterogeneous multi-processors are designed to bridge the gap between performance and energy e ciency in modern embedded systems. is is achieved by pairing Out-of-Order (OoO) cores, yielding performance through aggressive speculation and latency masking, with In-Order (InO) cores, that preserve energy through simpler design. By leveraging migrations between them, workloads can therefore select the best se ing for any given energy/delay envelope. However, migrations introduce execution overheads that can hurt performance if they happen too frequently. Finding the optimal migration frequency is critical to maximize energy savings while maintaining acceptable performance. We develop a simulation methodology that can 1) isolate the hardware e ects of migrations from the so ware, 2) directly compare the performance of di erent core types, 3) quantify the performance degradation and 4) calculate the cost of migrations for each case. To showcase our methodology we run mibench, a microbenchmark suite, and show that migrations can happen as fast as every 100k instructions with li le performance loss. We also show that, contrary to numerous recent studies, hypothetical designs do not need to share all of their internal components to be able to migrate at that frequency. Instead, we propose a feasible system that shares level 2 caches and a translation lookaside bu er that matches performance and e ciency. Our results show that there are phases comprising up to 10% that a migration to the OoO core leads to performance bene ts without any additional energy cost when running on the InO core, and up to 6% of phases where a migration to the InO core can save energy without a ecting performance. When considering a policy that focuses on improving the energy-delay product, results show that on average 66% of the phases can be migrated to deliver equal or be er system operation without having to aggressively share the entire memory system or to revert to migration periods ner than 100k instructions. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for pro t or commercial advantage and that copies bear this notice and the full citation on the rst page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permi ed. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior speci c permission and/or a fee. Request permissions from permissions@acm.org. Instructions per cycle Fig. 1. An excerpt of execution of dijkstra running on two di erent core types. Sampling at a higher resolution reveals phases that can potentially be exploited to increase e iciency. INTRODUCTIONIn today's embedded devices, performance is always tied to power constraints and energy e ciency. is happens because, while process technology scaling is enabling larger transistor densities, power per area has shown to increase beyond a certain thr...

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“Big, Medium, Little”: Reaching Energy Proportionality with Heterogeneous Computing Scheduler

Cited by 13 publications

References 14 publications

Dynamically Building Energy Proportional Data Centers with Heterogeneous Computing Resources

Dynamically Building Energy Proportional Data Centers with Heterogeneous Computing Resources

Energy Proportionality in Heterogeneous Data Center Supporting Applications with Variable Load

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