A Game-based Thermal-Aware Resource Allocation Strategy for Data Centers

Akbar, Saeed; Malik, Saif Ur Rehman; Choo, Kim‐Kwang Raymond; Khan, Samee U.; Ahmad, Naveed; Anjum, Adeel

doi:10.1109/tcc.2019.2899310

Cited by 25 publications

(14 citation statements)

References 28 publications

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“…Our model is more suitable for static workload cases and can be extended to dynamically update the scheduling policy for stochastic workloads. Akbar et al 13 propose a game-theoretic thermal-aware allocation strategy (GTARA). Their work presents a methodology to efficiently manage the computational diversity within a Cloud data center by using the concept of cooperative game theory with Nash-bargaining to assign resources based on a thermal profile.…”

Section: Related Workmentioning

confidence: 99%

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iThermoFog: IoT‐Fog based automatic thermal profile creation for cloud data centers using artificial intelligence techniques

Tuli

Gill

Casale

et al. 2020

Internet Technology Letters

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Preventing failures in Cloud Data Centers (CDCs) due to high temperatures is a key challenge. Such centers have so many servers that it is very difficult to efficiently keep their temperature under control. To help address this issue, we propose an artificial intelligence (AI) based automatic scheduling method that creates a thermal profile of CDC nodes using an integrated Internet of Things (IoT) and Fog computing environment called iThermoFog. We use a Gaussian Mixture Model to approximate the thermal characteristics of the servers which are used to predict and schedule tasks to minimize the average CDC temperature. Through empirical evaluation on an iFogSim and ThermoSim based testbed and IoT based smart home application, we show that iThermoFog outperforms the current state‐of‐the‐art thermal‐aware scheduling method. Specifically, iThermoFog reduces mean square temperatures by 13.5%, while simultaneously improving energy consumption, execution time, scheduling time and bandwidth usage.

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Section: Related Workmentioning

confidence: 99%

“…temperature compared to GTARA. 13 As GTARA only works in the cloud layer, we also adapt it to work in Fog environments by considering all fog nodes same as cloud nodes, but with latency and computational characteristics as those of fog devices. Figure 3A shows the variation of execution time with different numbers of operations.…”

Section: Datasetmentioning

confidence: 99%

iThermoFog: IoT‐Fog based automatic thermal profile creation for cloud data centers using artificial intelligence techniques

Tuli

Gill

Casale

et al. 2020

Internet Technology Letters

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“…Saeed et al [25] propose game based thermal aware resource allocation in data center (cloud) to reduce the emission of thermal energy due to high computation. Authors claim the proposed technique avoid creating hotspots as compared to counterpart strategies.…”

Section: Problems Identified Proposed Solutionmentioning

confidence: 99%

“…heterogeneous services and too many requests increase the Processing Time (PT) [22], • the physical long distance between end-user and physical cloud infrastructure increase the Response Time (RT) [23,24], • high computation heats the physical resources, which are cooled by high powered air-conditioning systems, which increase service cost [25], and • economical and environmental friendly huge power generation is challenging [26], especially for increasing demand of computing devices and cooling system data center is challenging [27].…”

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confidence: 99%

Cost Efficient Real Time Electricity Management Services for Green Community Using Fog

et al. 2020

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The computing devices in data centers of cloud and fog remain in continues running cycle to provide services. The long execution state of large number of computing devices consumes a significant amount of power, which emits an equivalent amount of heat in the environment. The performance of the devices is compromised in heating environment. The high powered cooling systems are installed to cool the data centers. Accordingly, data centers demand high electricity for computing devices and cooling systems. Moreover, in Smart Grid (SG) managing energy consumption to reduce the electricity cost for consumers and minimum rely on fossil fuel based power supply (utility) is an interesting domain for researchers. The SG applications are time-sensitive. In this paper, fog based model is proposed for a community to ensure real-time energy management service provision. Three scenarios are implemented to analyze cost efficient energy management for power-users. In first scenario, community’s and fog’s power demand is fulfilled from the utility. In second scenario, community’s Renewable Energy Resources (RES) based Microgrid (MG) is integrated with the utility to meet the demand. In third scenario, the demand is fulfilled by integrating fog’s MG, community’s MG and the utility. In the scenarios, the energy demand of fog is evaluated with proposed mechanism. The required amount of energy to run computing devices against number of requests and amount of power require cooling down the devices are calculated to find energy demand by fog’s data center. The simulations of case studies show that the energy cost to meet the demand of the community and fog’s data center in third scenario is 15.09% and 1.2% more efficient as compared to first and second scenarios, respectively. In this paper, an energy contract is also proposed that ensures the participation of all power generating stakeholders. The results advocate the cost efficiency of proposed contract as compared to third scenario. The integration of RES reduce the energy cost and reduce emission of CO 2 . The simulations for energy management and plots of results are performed in Matlab. The simulation for fog’s resource management, measuring processing, and response time are performed in CloudAnalyst.

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“…Two temperature-aware algorithms were proposed to prevent hot spots and to minimize the rise of operating temperature [7]. A game-based thermal-aware resource allocation was proposed in [8]. It uses a cooperative Nash-bargaining solution to reduce the thermal imbalance in data centers.…”

Section: Introductionmentioning

confidence: 99%

Optimized Energy Cost and Carbon Emission-Aware Virtual Machine Allocation in Sustainable Data Centers

et al. 2020

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Cloud data center’s total operating cost is conquered by electricity cost and carbon tax incurred due to energy consumption from the grid and its associated carbon emission. In this work, we consider geo-distributed sustainable datacenter’s with varying on-site green energy generation, electricity prices, carbon intensity and carbon tax. The objective function is devised to reduce the operating cost including electricity cost and carbon cost incurred on the power consumption of servers and cooling devices. We propose renewable-aware algorithms to schedule the workload to the data centers with an aim to maximize the green energy usage. Due to the uncertainty and time variant nature of renewable energy availability, an investigation is performed to identify the impact of carbon footprint, carbon tax and electricity cost in data center selection on total operating cost reduction. In addition, on-demand dynamic optimal frequency-based load distribution within the cluster nodes is performed to eliminate hot spots due to high processor utilization. The work suggests optimal virtual machine placement decision to maximize green energy usage with reduced operating cost and carbon emission.

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A Game-based Thermal-Aware Resource Allocation Strategy for Data Centers

Cited by 25 publications

References 28 publications

iThermoFog: IoT‐Fog based automatic thermal profile creation for cloud data centers using artificial intelligence techniques

iThermoFog: IoT‐Fog based automatic thermal profile creation for cloud data centers using artificial intelligence techniques

Cost Efficient Real Time Electricity Management Services for Green Community Using Fog

Optimized Energy Cost and Carbon Emission-Aware Virtual Machine Allocation in Sustainable Data Centers

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