Energy-performance trade-offs in data transfer tuning at the end-systems

Alan, Ismail; Arslan, Engin; Kosar, Tevfik

doi:10.1016/j.suscom.2014.08.004

Cited by 12 publications

(11 citation statements)

References 7 publications

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“…On the other hand, when we extend the CPU-based model to the AMD server by proportioning its TDP value to Intel server's TDP value, the error rate increases by 2-3% compared to fine-grained model. Even though the accuracy of the CPU-based model is higher, it still remains above 95% for ftp, bbcp and gridftp and above 92% for the rest [5].…”

Section: Modeling Data Transfer Energy Consumptionmentioning

confidence: 89%

“…However setting the optimal numbers for these parameters is not an easy task since incorrect tuning of parameters can either cause underutilization of the network or increase the power consumption unnecessarily [5].…”

Section: Application-layer Parameter Tuningmentioning

confidence: 99%

“…In our previous work [5], we proposed two power models to estimate the server power consumption during data transfers for two different cases of access privileges: (i) the fine-grained power model requires access to utilization information of four system components: CPU, memory, disk and NIC; (ii) the CPU-based power model only needs utilization information of the CPU in the target system. Our approach resembles Mantis [16] in predicting power consumption of data transfers based on operating system metrics.…”

Section: Modeling Data Transfer Energy Consumptionmentioning

confidence: 99%

“…Thus, we let the user to be able to decide the maximum acceptable number of concurrent channels for a data transfer. We have chosen concurrency level to tune the throughput, since in our previous work [5,10] we have observed that concurrency is the most influential transfer parameter for all file sizes in most test environments. Even though parallelism also creates multiple channels similar to concurrency, allotting channels to multiple file transfer instead of a single one yields higher disk IO throughput which qualifies concurrency to be the most effective parameter on transfer throughput.…”

Section: High Throughput Energy-efficient Transfer Algorithmmentioning

confidence: 99%

“…In our prior work [5], we analyzed various factors that affect the power consumption in end-to-end data transfers with a focus on the end-system energy consumption. Different protocol parameters such as TCP pipelining, parallelism and concurrency levels play a significant role in the achievable network throughput.…”

Section: Introductionmentioning

confidence: 99%

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Energy-aware data transfer algorithms

Alan

Arslan

Kosar

2015

Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis

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The amount of data moved over the Internet per year has already exceeded the Exabyte scale and soon will hit the Zettabyte range. To support this massive amount of data movement across the globe, the networking infrastructure as well as the source and destination nodes consume immense amount of electric power, with an estimated cost measured in billions of dollars. Although considerable amount of research has been done on power management techniques for the networking infrastructure, there has not been much prior work focusing on energy-aware data transfer algorithms for minimizing the power consumed at the end-systems. We introduce novel data transfer algorithms which aim to achieve high data transfer throughput while keeping the energy consumption during the transfers at the minimal levels. Our experimental results show that our energy-aware data transfer algorithms can achieve up to 50% energy savings with the same or higher level of data transfer throughput.

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Section: Modeling Data Transfer Energy Consumptionmentioning

confidence: 89%

Section: Application-layer Parameter Tuningmentioning

confidence: 99%

Section: Modeling Data Transfer Energy Consumptionmentioning

confidence: 99%

Section: High Throughput Energy-efficient Transfer Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Energy-aware data transfer algorithms

Alan

Arslan

Kosar

2015

Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis

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Dynamic Protocol Tuning Algorithms for High Performance Data Transfers

Arslan

Ross

Kosar

2013

Euro-Par 2013 Parallel Processing

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Obtaining optimal data transfer performance is of utmost importance to today's data-intensive distributed applications and wide-area data replication services.Doing so necessitates effectively utilizing available network bandwidth and resources, yet in practice transfers seldom reach the levels of utilization they potentially could. Tuning protocol parameters such as pipelining, parallelism, and concurrency can significantly increase utilization and performance, however determining the best settings for these parameters is a difficult problem, as network conditions can vary greatly between sites and over time. Nevertheless, it is an important problem, since poor tuning can cause either under-or over-utilization of network resources and thus degrade transfer performance. In this paper, we present three algorithms for application-level tuning of different protocol parameters for maximizing transfer throughput in wide-area networks.Our algorithms dynamically tune the number of parallel data streams per file (for large file optimization), the level of control channel pipelining (for small file optimization), and the number of concurrent file transfers to increase I/O throughput (a technique useful for all types of files). The proposed heuristic algorithms improve the transfer throughput up to 10x compared to the baseline and 7x compared to the state of the art solutions.

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GreenDataFlow: Minimizing the Energy Footprint of Global Data Movement

Nine

Tacchio

Imran

et al. 2018

2018 IEEE International Conference on Big Data (Big Data)

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The global data movement over Internet has an estimated energy footprint of 100 terawatt hours per year, costing the world economy billions of dollars. The networking infrastructure together with source and destination nodes involved in the data transfer contribute to overall energy consumption. Although considerable amount of research has rendered power management techniques for the networking infrastructure, there has not been much prior work focusing on energy-aware data transfer solutions for minimizing the power consumed at the end-systems. In this paper, we introduce a novel applicationlayer solution based on historical analysis and real-time tuning called GreenDataFlow, which aims to achieve high data transfer throughput while keeping the energy consumption at the minimal levels. GreenDataFlow supports service level agreements (SLAs) which give the service providers and the consumers the ability to fine tune their goals and priorities in this optimization process. Our experimental results show that GreenDataFlow outperforms the closest competing state-of-the art solution in this area 50% for energy saving and 2.5× for the achieved end-to-end performance.

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Energy-performance trade-offs in data transfer tuning at the end-systems

Cited by 12 publications

References 7 publications

Energy-aware data transfer algorithms

Energy-aware data transfer algorithms

Dynamic Protocol Tuning Algorithms for High Performance Data Transfers

GreenDataFlow: Minimizing the Energy Footprint of Global Data Movement

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