Energy-Aware Mappings of Series-Parallel Workflows onto Chip Multiprocessors

Benoît, Anne; Renaud-Goud, Paul; Robert, Yves; Melhem, Rami

doi:10.1109/icpp.2011.61

Cited by 8 publications

(9 citation statements)

References 52 publications

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“…al. [34] considers a subset of stream programs that can be modeled as serial-parallel workflows, and studies the problem of mapping such workflows to CMPs to minimize energy. Eprof [35] designs an energy-efficient scheduling algorithm for stream applications, with a non-DVFS based solution.…”

Section: Related Workmentioning

confidence: 99%

Green Streams for data-intensive software

Bartenstein

Liu

2013

2013 35th International Conference on Software Engineering (ICSE)

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This paper introduces GREEN STREAMS, a novel solution to address a critical but often overlooked property of data-intensive software: energy efficiency. GREEN STREAMS is built around two key insights into data-intensive software. First, energy consumption of data-intensive software is strongly correlated to data volume and data processing, both of which are naturally abstracted in the stream programming paradigm; Second, energy efficiency can be improved if the data processing components of a stream program coordinate in a "balanced" way, much like an assembly line that runs most efficiently when participating workers coordinate their pace. GREEN STREAMS adopts a standard stream programming model, and applies Dynamic Voltage and Frequency Scaling (DVFS) to coordinate the pace of data processing among components, ultimately achieving energy efficiency without degrading performance in a parallel processing environment. At the core of GREEN STREAMS is a novel constraint-based inference to abstract the intrinsic relationships of data flow rates inside a stream program, that uses linear programming to minimize the frequencies -hence the energy consumption -for processing components while still maintaining the maximum output data flow rate. The core algorithm of GREEN STREAMS is formalized, and its optimality is established. The effectiveness of GREEN STREAMS is evaluated on top of the StreamIt framework, and preliminary results show the approach can save CPU energy by an average of 28% with a 7% performance improvement.

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Section: Related Workmentioning

confidence: 99%

Green Streams for data-intensive software

Bartenstein

Liu

2013

2013 35th International Conference on Software Engineering (ICSE)

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“…Efforts have been made to help lower worldwide computer energy consumption by promoting adoption of highefficiency power supplies and encouraging use of powersaving features already available on users' equipment. Technologies including virtualization [22], voltage and frequency scaling [23] and dynamic power management [24], energy-aware task mapping and scheduling [25,26], energy-aware server provisioning [3] and thermal-aware server provisioning [4] have contributed to better energy efficiency on multiprocessors, servers and data centers. While most existing works are aimed at reducing energy consumption of IDC servers, minimizing energy cost for IDCs has attracted much attention with the emergent multi-electricity markets.…”

Section: Related Workmentioning

confidence: 99%

Temporal Load Balancing with Service Delay Guarantees for Data Center Energy Cost Optimization

Luo

Rao

2014

IEEE Trans. Parallel Distrib. Syst.

104

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Cloud computing services are becoming integral part of people's daily life. These services are supported by infrastructure known as Internet data center (IDC). As demand for cloud computing services soars, energy consumed by IDCs is skyrocketing. Both academia and industry have paid great attention to energy management of IDCs. This paper studies an important energy management problem-how to minimize energy cost for IDCs in deregulated electricity markets. We propose a novel two-stage design and the eco-IDC (Energy Cost Optimization-IDC) algorithm to exploit the temporal diversity of electricity price and dynamically schedule workload to execute on IDC servers through an input queue. Extensive evaluation experiments are performed using real-life electricity price and workload traces at an enterprise production data center. The evaluation results demonstrate that the proposed approach significantly reduces energy cost for IDCs, guarantees a service delay bound, and alleviates workload drop if the service delay bound is sufficiently large.Index Terms-data center, energy, cost management, delay bound, load balance, algorithm. ! • J. Luo is with

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“…This tendency has also reached the distributed computing domain [7,8,9]. In the case of flow applications where the global throughput is directed by the lower throughput of the graph, it is not always necessary that all machines run at maximum speed [10]. Several papers define an energy model based on power consumption modes where the processing ca-pabilities depend on the supplied voltage [11,12].…”

Section: Related Workmentioning

confidence: 99%

Optimal energy consumption and throughput for workflow applications on distributed architectures

Othman

Nicod

Philippe

et al. 2014

Sustainable Computing: Informatics and Systems

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In this article we study both the throughput and the energy optimization problem for a distributed system subject to failures that executes a workflow at different speed levels. The application is modeled as a directed acyclic graph composed of typed tasks linked by dependency constraints. A continuous flow, or a great number of application instances, has to be processed. Optimizing the collaborative system performance implies to increase the throughput-the number of application instances processed by time unit-or to decrease the period-the time needed to output one instance of the system. The system is designed as a collaborative platform of distributed machines. Each machine collaborates with others by performing all the instances of at least one task of the DAG. The problem we tackle is to optimize the configuration of the platform. In this paper we propose two polynomial algorithms that optimize the two objectives of period (i.e., throughput) minimization and energy minimization. We prove that the proposed algorithms give optimal results. Our optimization approach is hierarchic in that we either minimize the energy consumption for the optimal period or minimize the period for the optimal energy consumption. Moreover a minor modification of our algorithms allows to compute the Pareto front between the two optimal solutions.

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Energy-Aware Mappings of Series-Parallel Workflows onto Chip Multiprocessors

Cited by 8 publications

References 52 publications

Green Streams for data-intensive software

Green Streams for data-intensive software

Temporal Load Balancing with Service Delay Guarantees for Data Center Energy Cost Optimization

Optimal energy consumption and throughput for workflow applications on distributed architectures

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