A Shapley value-based thermal-efficient workload distribution in heterogeneous data centers

The transferrable characteristics of workloads both spatial and temporal have received much attention recently. A data centre prosumer (DCP) is created by combining multiple data centre buildings, which contain workloads, photovoltaic generations, and battery energy storage systems (BESSs). Few studies have considered electricity trading and payoff sharing among various DCPs in a data centre park. This paper presents a cooperative framework for interconnected DCPs in a data centre park and proposes a novel day‐ahead operation strategy considering uncertainties in photovoltaic (PV) generations and workloads using the equivalent linear robust reformation method. The operation strategy is formulated as an asymmetric Nash bargaining cooperative game, which aims at maximizing the overall payoff of the cooperative alliance while considering the contributions of shared electricity to share the payoff. To solve the formulated cooperative game problem while preserving user privacy, an enhanced self‐adaptive prediction‐correction‐based alternating direction multiplier method (PCB‐ADMM) algorithm with adaptive penalty coefficients to accelerate convergence is employed in a distributed manner. The case studies indicate a 6.63% reduction in overall cost and verify the feasibility and fairness of the proposed cooperative strategy. The enhanced self‐adaptive PCB‐ADMM algorithm also shows superiority over other ADMM algorithms in effectiveness and rationality.

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Green Data Analytics of Supercomputing from Massive Sensor Networks: Does Workload Distribution Matter?

Guo

Ramesh

2023

Information Systems Research

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Energy costs represent a significant share of the total cost of ownership in high-performance computing systems. Using a unique data set collected by massive sensor networks in a petascale national supercomputing center, we first present an explanatory model to identify key factors affecting energy consumption in supercomputing. Our analytic results show that workload distribution among the nodes has significant effects and could effectively be leveraged to improve energy efficiency. We then establish the high model performance using in-sample and out-of-sample analyses and develop prescriptive models for energy-optimal runtime workload management. We present four dynamic resource management methodologies (packing, load balancing, threshold-based switching, and energy optimization), model their application at two levels (within-rack and cross-rack resource allocation), and explore runtime resource redistribution policies for jobs under the computational steering and comparatively evaluate strategies that use computational steering with those that do not. Our experimental results lead to a threshold strategy that yields near-optimal energy efficiency under all workload conditions. We further calibrate the energy-optimal resource allocations over the full range of workloads and present a bi-criteria evaluation to consider energy consumption and job performance tradeoffs. We conclude with implementation guidelines and policy insights into energy-efficient computing resource management in large supercomputing centers.

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A Shapley value-based thermal-efficient workload distribution in heterogeneous data centers

Cited by 3 publications

References 39 publications

Spatial and thermal aware methods for efficient workload management in distributed data centers

Spatial and thermal aware methods for efficient workload management in distributed data centers

Day‐ahead operation strategy for multiple data centre prosumers: A cooperative game approach

Green Data Analytics of Supercomputing from Massive Sensor Networks: Does Workload Distribution Matter?

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