Energy and Service Level Agreement Aware Resource Allocation Heuristics for Cloud Data Centers
Abstract:Cloud computing offers a wide range of on-demand resources over the internet. Utility-based resource allocation in cloud data centers significantly increases the number of cloud users. Heavy usage of cloud data center encounters many problems such as sacrificing system performance, increasing operational cost and high-energy consumption. Therefore, the result of the system damages the environment extremely due to heavy carbon (CO 2) emission. However, dynamic allocation of energy-efficient resources in cloud d… Show more
“…These algorithms exhibited better results in terms of resource consumption and resource utilisation. SLA-aware Modified Best Fit Decreasing (MBFD) algorithm, an energy and SLA Aware resource allocation heuristic algorithm is proposed in [7]. MBFD reduces the power consumption of VMs using minPower and maxUtilization VM migration policies.…”
Cloud Service Providers (CSPs) offers Information Technology services like infrastructure and software to users on a pay as you go basis. Energy consumption is one of the significant challenges faced by Cloud Service Providers (CSP). Virtual Machine (VM) placement is an energy-efficient practice performed in the cloud datacenters. Best-Fit Decreasing (BFD) is a VM placement and is known to give a near-optimal solution in a reasonable time by sorting the VMs in decreasing order. We propose a Hybrid Best-Fit (HBF) Heuristic for VM placements. Experimental results show that HBF is consuming 2.516% and 3.392% less energy compared to Best-Fit and BFD heuristics.
“…These algorithms exhibited better results in terms of resource consumption and resource utilisation. SLA-aware Modified Best Fit Decreasing (MBFD) algorithm, an energy and SLA Aware resource allocation heuristic algorithm is proposed in [7]. MBFD reduces the power consumption of VMs using minPower and maxUtilization VM migration policies.…”
Cloud Service Providers (CSPs) offers Information Technology services like infrastructure and software to users on a pay as you go basis. Energy consumption is one of the significant challenges faced by Cloud Service Providers (CSP). Virtual Machine (VM) placement is an energy-efficient practice performed in the cloud datacenters. Best-Fit Decreasing (BFD) is a VM placement and is known to give a near-optimal solution in a reasonable time by sorting the VMs in decreasing order. We propose a Hybrid Best-Fit (HBF) Heuristic for VM placements. Experimental results show that HBF is consuming 2.516% and 3.392% less energy compared to Best-Fit and BFD heuristics.
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