Octopus-Man: QoS-driven task management for heterogeneous multicores in warehouse-scale computers

Petrucci, Vinícius; Laurenzano, Michael A.; Doherty, John; Zhang, Yunqi; Mossé, Daniel; Mars, Jason; Tang, Lingjia

doi:10.1109/hpca.2015.7056037

Cited by 81 publications

(75 citation statements)

References 47 publications

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“…Figure 2 raises two main concerns with current state-of-the-art heuristic algorithm [56]. First, Figure 2a demonstrates that in periods of low load (less than 60% of max capacity for Memcached), exclusive use of low performance cores at lower DVFS ensures QoS is met while reducing static-power, thus making it an excellent option to use for periods of low load.…”

Section: Mixing DI Erent Core Types With Dvfsmentioning

confidence: 99%

“…Previous studies [6,19,56,57] have shown that data centers like Google and Facebook operate at peak load only for a small fraction of the day, where the power consumption and load are proportional. The number of queries per second during a web search, a typical load at Google data center, varies between about 5% and 80% of maximum capacity [31,40,47].…”

Section: Motivationmentioning

confidence: 99%

“…Similarly, Facebook consistently sees diurnal load variations between 10% and 95% of maximum capacity, across multiple server clusters [4,8]. The periods of low server utilization provide opportunity to reduce data center energy consumption [17,18,41,56].…”

Section: Motivationmentioning

confidence: 99%

“…Heterogeneous servers can minimize power consumption at low load by deploying small cores, and can provide maximum performance using big cores to meet the QoS target for latency-critical workloads [11,35,56]. Figure 2 shows the energy e ciency in RPS/Watt (Requests Per Second per Watt) and QPS/Watt (Queries Per Second per Watt) when using a state-of-the-art baseline policy [56]. We explore a heterogeneous architecture mixing di erent cores types and DVFS (HetCMP) running Memcached ( Figure 2a) and Web-Search (Figure 2b) at di erent load levels.…”

Section: Motivationmentioning

confidence: 99%

“…The workload is given by queries per second (QPS). [56] and heterogeneous platforms with DVFS (HetCMP) at di erent load levels along with their respective state machines (2c)…”

Section: Motivationmentioning

confidence: 99%

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The Hipster Approach for Improving Cloud System Efficiency

Nishtala

Carpenter

Petrucci

et al. 2017

ACM Trans. Comput. Syst.

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In 2013, U.S. data centers accounted for 2.2% of the country's total electricity consumption, a gure that is projected to increase rapidly over the next decade. Many important data center workloads in cloud computing are interactive, and they demand strict levels of quality-of-service (QoS) to meet user expectations, making it challenging to optimize power consumption along with increasing performance demands. This paper introduces Hipster, a technique that combines heuristics and reinforcement learning to improve resource e ciency in cloud systems. Hipster explores heterogeneous multi-cores and dynamic voltage and frequency scaling (DVFS) for reducing energy consumption while managing the QoS of the latency-critical workloads. To improve data center utilization and make best usage of the available resources, Hipster can dynamically assign remaining cores to batch workloads without violating the QoS constraints for the latency-critical workloads. We perform experiments using a 64-bit ARM big.LITTLE platform, and show that, compared to prior work, Hipster improves the QoS guarantee for Web-Search from 80% to 96%, and for Memcached from 92% to 99%, while reducing the energy consumption by up to 18%. Hipster is also e ective in learning and adapting automatically to speci c requirements of new incoming workloads just enough to meet the QoS and optimize resource consumption. In this work, we extend our previous work in several ways. First, we present an analysis of the size of the reward lookup table and an optimization for the table to improve the scalability of our reinforcement learning mechanism. Second, we demonstrate Hipster's capability to adapt to changes in the latency-critical application at runtime and still satisfy QoS guarantees of the new incoming applications. Lastly, we present a deployment methodology for setting up new applications managed by Hipster's runtime system.

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Section: Mixing DI Erent Core Types With Dvfsmentioning

confidence: 99%

Section: Motivationmentioning

confidence: 99%

Section: Motivationmentioning

confidence: 99%

Section: Motivationmentioning

confidence: 99%

“…The workload is given by queries per second (QPS). [56] and heterogeneous platforms with DVFS (HetCMP) at di erent load levels along with their respective state machines (2c)…”

Section: Motivationmentioning

confidence: 99%

See 3 more Smart Citations

The Hipster Approach for Improving Cloud System Efficiency

Nishtala

Carpenter

Petrucci

et al. 2017

ACM Trans. Comput. Syst.

Self Cite

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A survey of techniques for improving efficiency of mobile web browsing

Mittal

Mattela

2019

Concurrency and Computation

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Summary Mobile web traffic has now surpassed the desktop web traffic and has become the primary means for service providers to reach out to the billions of end users. Due to this trend, optimization of mobile web browsing (MWB) has gained significant attention. In this paper, we present a survey of techniques for improving the efficiency of web browsing on mobile systems, proposed in the last 6‐7 years. We review the techniques from both the networking domain (eg, proxy and browser enhancements) and the processor architecture domain (eg, hardware customization and thread‐to‐core scheduling). We organize the research works based on key parameters to highlight their similarities and differences. Beyond summarizing the recent works, this survey aims to emphasize the need of architecting for MWB as the first principle, instead of retrofitting for it.

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