A Carbon-Aware Incentive Mechanism for Greening Colocation Data Centers

Islam, Mohammad A.; Mahmud, Hasan; Ren, Shaolei; Wang, Xiaorui

doi:10.1109/tcc.2017.2767043

Cited by 9 publications

(4 citation statements)

References 28 publications

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“…We prove Theorem 2 in Appendix B of [33]. Substituting A(X(τ )) and C(R(τ )) into inequality (15), we finally get the final optimization objective P2 min x(τ ),r lt (t),rrt(τ ) 6), ( 7), ( 8).…”

Section: A Problem Transformationmentioning

confidence: 99%

“…Edge servers can use green energy, e.g., solar energy [12], and thus the mixture of energy supplied to edge servers varies from site to site caused by the volatility of green energy [13]. As a result, the carbon intensity of such mixed energy fluctuates over time and across locations [14], [15]. At the cloud server, an uncompressed DNN model is deployed to achieve lower accuracy loss at the cost of higher carbon emissions.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic Task Scheduling in Cloud-Assisted Mobile Edge Computing

Zhou

Zhang

et al. 2023

IEEE Trans. on Mobile Comput.

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Provisioning dynamic machine learning (ML) inference as a service for artificial intelligence (AI) applications of edge devices faces many challenges, including the trade-off among accuracy loss, carbon emission, and unknown future costs. Besides, many governments are launching carbon emission rights (CER) for operators to reduce carbon emissions further to reverse climate change. Facing these challenges, to achieve carbon-aware ML task offloading under limited carbon emission rights thus to achieve green edge AI, we establish a joint ML task offloading and CER purchasing problem, intending to minimize the accuracy loss under the long-term time-averaged cost budget of purchasing the required CER. However, considering the uncertainty of the resource prices, the CER purchasing prices, the carbon intensity of sites, and ML tasks' arrivals, it is hard to decide the optimal policy online over a long-running period time. To overcome this difficulty, we leverage the two-timescale Lyapunov optimization technique, of which the T -slot drift-pluspenalty methodology inspires us to propose an online algorithm that purchases CER in multiple timescales (on-preserved in carbon future market and on-demanded in the carbon spot market) and makes decisions about where to offload ML tasks. Considering the NP-hardness of the T -slot problems, we further propose the resource-restricted randomized dependent rounding algorithm to help to gain the near-optimal solution with no help of any future information. Our theoretical analysis and extensive simulation results driven by the real carbon intensity trace show the superior performance of the proposed algorithms.

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Section: A Problem Transformationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dynamic Task Scheduling in Cloud-Assisted Mobile Edge Computing

Zhou

Zhang

et al. 2023

IEEE Trans. on Mobile Comput.

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show abstract

“…Then, in the area of eco-friendly power usage, [14] proposed a bid mechanism for the colocation of data centers where equipment, space, and bandwidth were available for rental, and operators could realize carbon-aware task scheduling by using economic incentives to reshape the tenants' demand. [15] investigated the balance of power demands and supplies with the aid of task scheduling among geographically green DCs powered by fuel cells.…”

Section: A Related Workmentioning

confidence: 99%

Eco-Friendly Powering and Delay-Aware Task Scheduling in Geo-Distributed Edge-Cloud System: A Two-Timescale Framework

Sun

Wen

et al. 2020

IEEE Access

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Power consumption and task latency are two crucial issues in edge-cloud computing. This paper mainly aims to promote the use of clean power in geo-distributed data centers (DCs) in a deregulated electricity market where customers are allowed to buy power from multiple suppliers, combined with the guarantee of task latency. To alleviate the conflict between frequent switches of servers and the uncertainty of task arrivals in DCs, this paper proposes a two-timescale framework consisting of the long-term capacity planning of geo-distributed DCs and the real-time task dispatching from edge gateways (EGs) to DCs. First, DCs make long-term plans on the number of active servers aiming at the eco-friendly and delay-aware power cost minimization, which is formulated as problem P. Specifically, we introduce a convex pollution indicator function (PIF) to measure the pollution cost of the various types of powers sold by different suppliers, which can encourage the use of cleaner power and improve power savings. Second, in each sub-slot, each EG separately optimizes its individual mixed strategies of task dispatching to DCs with the knowledge of the planned capacities and the real-time queue backlogs of DCs, where a Lyapunov optimization framework is applied. Finally, we give the corresponding distributed algorithm design. Simulation results reveal that our method can realize the trade-off between the power cost and the delay cost of requests, and improve the clean power usage by up to 50%-60% of the total power usage in DCs. Additionally, comparisons with other schemes show that our approach can provide more stable guarantees of task latency in different situations of workload density, which benefits from the diverse-timescale optimizations of capacities of DCs and task routing from EGs to DCs. INDEX TERMS distributed task scheduling; edge-cloud system; two-timescale framework; pollution indicator function; eco-friendly and delay-aware; Lyapunov optimization

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“…Several works have researched the power cost minimization for geo-distributed data centers by jointly considering renewable energy, multi-source power and workload scheduling. [25] proposed a bid mechanism to reduce the carbon footprint. [26] and [27] proposed some strategies of geographical workload balancing, where more workloads were dispatched to the areas with lower carbon power or more renewable power.…”

Section: A Related Workmentioning

confidence: 99%

Eco-friendly Power Cost Minimization for Geo-distributed Data Centers Considering Workload Scheduling

Sun,

Wen,

et al. 2018

Preprint

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The rapid development of renewable energy in the energy Internet is expected to alleviate the increasingly severe power problem in data centers, such as the huge power costs and pollution. This paper focuses on the eco-friendly power cost minimization for geo-distributed data centers supplied by multisource power, where the geographical scheduling of workload and temporal scheduling of batteries' charging and discharging are both considered. Especially, we innovatively propose the Pollution Index Function to model the pollution of different kinds of power, which can encourage the use of cleaner power and improve power savings. We first formulate the eco-friendly power cost minimization problem as a multi-objective and mixedinteger programming problem, and then simplify it as a singleobjective problem with integer constraints. Secondly, we propose a Sequential Convex Programming (SCP) algorithm to find the globally optimal non-integer solution of the simplified problem, which is non-convex, and then propose a low-complexity searching method to seek for the quasi-optimal mixed-integer solution of it. Finally, simulation results reveal that our method can improve the clean energy usage up to 50%-60% and achieve power cost savings up to 10%-30%, as well as reduce the delay of requests.

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A Carbon-Aware Incentive Mechanism for Greening Colocation Data Centers

Cited by 9 publications

References 28 publications

Dynamic Task Scheduling in Cloud-Assisted Mobile Edge Computing

Dynamic Task Scheduling in Cloud-Assisted Mobile Edge Computing

Eco-Friendly Powering and Delay-Aware Task Scheduling in Geo-Distributed Edge-Cloud System: A Two-Timescale Framework

Eco-friendly Power Cost Minimization for Geo-distributed Data Centers Considering Workload Scheduling

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