AppFlow: Autonomic Performance-Per-Watt Management of Large-Scale Data Centers

Khargharia, Bithika; Luo, Haoting; Al-Nashif, Youssif; Hariri, Salim

doi:10.1109/greencom-cpscom.2010.103

Cited by 8 publications

(4 citation statements)

References 16 publications

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“…For instance, Berral et al demonstrated a way of modeling expected processor and disk usage based on the M5P algorithm [32]. Khargharia et al applied a decision tree to map a power consumption model with current system behaviour [33]. Dhiman et al piloted a GMM-based approach for power prediction following an unsupervised learning method with an error below 10% [34].…”

Section: Contributionsmentioning

confidence: 99%

Designing Energy Efficiency for Traditional Data Center

Bhattacharya

Mao

Bhattacharya

et al. 2022

Preprint

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Power Management Strategies and the impact of carbon dioxide emissions from data centers across the globe have drawn significant attention worldwide. The rapid growth of energy consumed in data centers has led to 1) huge costs 2) depletion of non-renewable resources such as coal and petroleum, and 3) emission of greenhouse gases like CO2 in the atmosphere. These greenhouse gas significantly contributes to the climate change of the earth. To tackle this challenge, our research deals with modeling the energy resources of data centers, thereby offering insights to reduce the global carbon footprint and energy cost. In our model, we prioritize green energy consumption by eliminating brown energy resources. In this process, we devise an algorithm that can determine the amount of CO2 emission in the atmosphere per hour by different energy resources. We create an energy model for data centers by incorporating the support vector regression algorithm. Our model is adroit at projecting energy consumed in data centers powered by green energy. Our experimental results confirm that our model consistently delivers high prediction accuracy in terms of energy usage in data centers. The model is expected to facilitate data analytic venues to optimize energy efficiency and sustaininability for the development of future data centers.

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Section: Contributionsmentioning

confidence: 99%

Designing Energy Efficiency for Traditional Data Center

Bhattacharya

Mao

Bhattacharya

et al. 2022

Preprint

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“…In [15], a power-aware cache partitioning mechanism is presented, which reduces energy consumption by 20% while maintaining the performance. Similarly, [16] showed that using memory management in addition to multicore processor management, it is possible to reduce power consumption up to 63.75% compared to other methods by activating/deactivating memory banks during runtime. It should be noted that the proposed methods assume that the amount of the memory is fixed at runtime and then they reduce power consumption by switching the memory states.…”

Section: A Dynamic Voltage and Frequency Scalingmentioning

confidence: 99%

“…These features enable the management system to predict trends in the dynamic cloud resource requirements of the workload and to proactively configure the cloud resources to meet these requirements ahead of time. For further detail about AppFlow and how it can be used to characterize and predict workloads, please refer to [16,32,6].…”

Section: A Appflow: a Data Structure For Autonomic Managementmentioning

confidence: 99%

“…Instead of static and pessimistic resource allocation style, it is possible to reduce the power consumption using dynamic power and performance management schemes. More recently several methods focused on dynamic management of only a single resource such as frequency scaling [7][8][9][10][11][12][13], disk [20][21], memory [14][15][16], network devices [17][18], etc. for power management and also applied for replacing the static and pessimistic resource allocation style in order to reduce power consumption without causing performance loss.…”

Section: Introductionmentioning

confidence: 99%

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Autonomic Workload and Resources Management of Cloud Computing Services

Fargo

Tunc

Al-Nashif

et al. 2014

2014 International Conference on Cloud and Autonomic Computing

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The power consumption of data centers and cloud systems have increased almost three times between 2007 and 2012. Over-provisioning techniques are typically used for meeting the peak workloads. In this paper we present an autonomic power and performance management method for cloud systems in order to dynamically match the application requirements with "just-enough" system resources at runtime that lead to significant power reduction while meeting the quality of service requirements of the cloud applications. Our solution offers the following capabilities: 1) real-time monitoring of the cloud resources and workload behavior running on virtual machines (VMs); 2) determine the current operating point of both workloads and the VMs running these workloads; 3) characterize workload behavior and predict the next operating point for the VMs; 4) dynamically manage the VM resources (scaling up and down the number of cores, CPU frequency, and memory amount) at run time; and 5) assign available cloud resources that can guarantee optimal power consumption without sacrificing the QoS requirements of cloud workloads. We validate the performance of our approach using the RUBiS benchmark, an auction model emulating eBay transactions that generates a wide range of workloads (such as browsing and bidding with different number of clients). Our experimental results show that our approach can lead to reduction in power consumption up to 87% when compared to the static resource allocation strategy, 72% compared to adaptive frequency scaling strategy and 66% compared to a similar multi-resource management strategy.

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