Energy-efficient Virtual Machine Allocation Technique Using Flower Pollination Algorithm in Cloud Datacenter: A Panacea to Green Computing

Cloud computing has attracted significant interest due to the increasing service demands from organizations offloading computationally intensive tasks to datacenters. Meanwhile, datacenter infrastructure comprises hardware resources that consume high amount of energy and give out carbon emissions at hazardous levels. In cloud datacenter, Virtual Machines (VMs) need to be allocated on various Physical Machines (PMs) in order to minimize resource wastage and increase energy efficiency. Resource allocation problem is NP-hard. Hence finding an exact solution is complicated especially for large-scale datacenters. In this context, this paper proposes an Energy-oriented Flower Pollination Algorithm (E-FPA) for VM allocation in cloud datacenter environments. A system framework for the scheme was developed to enable energy-oriented allocation of various VMs on a PM. The allocation uses a strategy called Dynamic Switching Probability (DSP). The framework finds a near optimal solution quickly and balances the exploration of the global search and exploitation of the local search. It considers a processor, storage, and memory constraints of a PM while prioritizing energy-oriented allocation for a set of VMs. Simulations performed on MultiRecCloudSim utilizing planet workload show that the E-FPA outperforms the Genetic Algorithm for Power-Aware (GAPA) by 21.8%, Order of Exchange Migration (OEM) ant colony system by 21.5%, and First Fit Decreasing (FFD) by 24.9%. Therefore, E-FPA significantly improves datacenter performance and thus, enhances environmental sustainability

RED-BL: Evaluating dynamic workload relocation for data center networks

In this paper, we present RED-BL (Relocate Energy Demand to Better Locations), a framework to minimize the electricity cost for operating data center networks over consecutive intervals of fixed duration. Within each interval, RED-BL provides a mapping of workload to a set of geographically distributed data centers. To this end, RED-BL uses the geographical and temporal variations in electricity prices as exhibited by electrical energy markets. In addition, we incorporate the transition costs associated with a change in workload mapping from one interval to the next, over a planning window comprising multiple such intervals. This results in a sequence of workload mappings that is optimal over the entire planning window, even though the workload mapping in a given interval may not be locally optimal. Our evaluation of RED-BL uses electricity prices from the US markets and workload traces from live Internet applications with millions of users. We find that RED-BL can reduce the electric bill by as much as 45% compared to the case when the workload is uniformly distributed. When compared to existing workload relocation solutions, for a wide range of data center deployment sizes, RED-BL achieves electricity cost savings that are 8.28% higher, on average. This seemingly modest reduction can save millions of dollars for the operators. The cost of this saving is an inexpensive computation at the start of each planning window. © 2014 Elsevier B.V. All rights reserved

Energy-efficient Nature-Inspired techniques in Cloud computing datacenters

Cloud computing is a systematic delivery of computing resources as services to the consumers via the Internet. Infrastructure as a Service (IaaS) is the capability provided to the consumer by enabling smarter access to the processing, storage, networks, and other fundamental computing resources, where the consumer can deploy and run arbitrary software including operating systems and applications. The resources are sometimes available in the form of Virtual Machines (VMs). Cloud services are provided to the consumers based on the demand, and are billed accordingly. Usually, the VMs run on various datacenters, which comprise of several computing resources consuming lots of energy resulting in hazardous level of carbon emissions into the atmosphere. Several researchers have proposed various energy-efficient methods for reducing the energy consumption in datacenters. One such solutions are the Nature-Inspired algorithms. Towards this end, this paper presents a comprehensive review of the state-of-the-art Nature-Inspired algorithms suggested for solving the energy issues in the Cloud datacenters. A taxonomy is followed focusing on three key dimension in the literature including virtualization, consolidation, and energy-awareness. A qualitative review of each techniques is carried out considering key goal, method, advantages, and limitations. The Nature-Inspired algorithms are compared based on their features to indicate their utilization of resources and their level of energy-efficiency. Finally, potential research directions are identified in energy optimization in data centers. This review enable the researchers and professionals in Cloud computing datacenters in understanding literature evolution towards to exploring better energy-efficient methods for Cloud computing datacenters