Abstract
The tremendous growth of big data analysis and IoT (Internet of Things) has made cloud computing an integral part of society. The prominent problem associated with data centers is the growing energy consumption, which results in environmental pollution. Data centers can reduce their carbon emissions through efficient management of server power consumption for a given workload. Dynamic voltage frequency scaling (DVFS) can be applied to control the operating frequencies of the servers based on the workloads assigned to them, as this approach has a cubic increment relationship with power consumption. This research work proposes two DVFS-enabled host selection algorithms for virtual machine (VM) placement with a cluster selection strategy, namely the carbon and power-efficient optimal frequency (C-PEF) algorithm and the carbon-aware first-fit optimal frequency (C-FFF) algorithm.The main aims of the proposed algorithms are to balance the load among the servers and dynamically tune the cooling load based on the current workload. The cluster selection strategy is based on static and dynamic power usage effectiveness (PUE) values and the carbon footprint rate (CFR). The cluster selection is also extended to non-DVFS host selection policies, namely the carbon- and power-efficient (C-PE) algorithm, carbon-aware first-fit (C-FF) algorithm, and carbon-aware first-fit least-empty (C-FFLE) algorithm. The results show that C-FFF achieves 2% more power reduction than C-PEF and C-PE, and demonstrates itself as a power-efficient algorithm for CO2 reduction, retaining the same quality of service (QoS) as its counterparts with lower computational overheads.
Highlights
Datacenters are critical infrastructures that amalgamate vast computing and storage resources, offering online computing as and when needed
Selection of data centers and clusters is performed based on the PUE and CO2 emission rate, aiming to reduce the overall carbon footprints of the data centers; Load balancing is done by identifying a feasible server with a minimal operating frequency for the current workload with the required quality of service, aiming to reduce hot spots in central processing unit (CPU) heat dissipation, which have a direct impact on hardware lifetime and performance; The impacts of static and dynamic power usage effectiveness (PUE) on placement decisions are analyzed, along with cooling load power impacts
carbon-aware first-fit least-empty (C-FFLE) algorithm is used toin show the impact on power consumption when only resource usage is considered as a parameter in the heuristic approach
Summary
Datacenters are critical infrastructures that amalgamate vast computing and storage resources, offering online computing as and when needed. Virtualization techniques embedded in grid computing platforms aid data centers in providing computing resources as a service to customers [1]. Synchronized power and resource management are essential to assist data centers in conserving energy while providing the required quality of service (QoS) for hosted applications [3]. The power consumption of an idle server is two-thirds of its energy consumption with 100% utilization at full load [7,8,9]. The energy reduction achieved by shrinking the number of existing resources through VM consolidation may result in lower resource availability, jeopardizing the credibility of the provider. Resource utilization should be optimized based on the computing capacities of the servers in order to reduce idle and active server power consumption [10]. Considering the above, minimum power consumption is achieved through optimum central processing unit (CPU) utilization of the servers with our proposed algorithms
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