Achieving Energy Efficiency by Optimal Resource Utilisation in Cloud Environment

Tiwari

Computational Intelligence and Neuroscience

et al. 2022

One of the important and challenging tasks in cloud computing is to obtain the usefulness of cloud by implementing several specifications for our needs, to meet the present growing demands, and to minimize energy consumption as much as possible and ensure proper utilization of computing resources. An excellent mapping scheme has been derived which maps virtual machines (VMs) to physical machines (PMs), which is also known as virtual machine (VM) placement, and this needs to be implemented. The tremendous diversity of computing resources, tasks, and virtualization processes in the cloud causes the consolidation method to be more complex, tedious, and problematic. An algorithm for reducing energy use and resource allocation is proposed for implementation in this article. This algorithm was developed with the help of a Cloud System Model, which enables mapping between VMs and PMs and among tasks of VMs. The methodology used in this algorithm also supports lowering the number of PMs that are in an active state and optimizes the total time taken to process a set of tasks (also known as makespan time). Using the CloudSim Simulator tool, we evaluated and assessed the energy consumption and makespan time. The results are compiled and then compared graphically with respect to other existing energy-efficient VM placement algorithms.

Section: Literature Surveysmentioning

confidence: 99%

An Energy-Efficient Strategy and Secure VM Placement Algorithm in Cloud Computing

Tiwari

Computational Intelligence and Neuroscience

et al. 2022

2018 17th International Symposium on Parallel and Distributed Computing (ISPDC)

“…Demand-based VM placement algorithms may aim to minimize energy consumption (e.g. [13], [16]- [19]) while the reallocation may be power-aware as in [20]. A dynamic VM placement controller may consider different management objectives, such as energy efficiency, load balancing, fair allocation, or service differentiation (e.g.…”

Section: Related Workmentioning

confidence: 99%

Dynamic Tuning for Parameter-Based Virtual Machine Placement

Mosa

Sakellariou

2018

Virtual machine (VM) placement is the process that allocates virtual machines onto physical machines (PMs) in cloud data centers. Reservation-based VM placement allocates VMs to PMs according to a (statically) reserved VM size regardless of the actual workload. If, at some point in time, a VM is making use of only a fraction of its reservation this leads to PM underutilization, which wastes energy and, at a grand scale, it may result in financial and environmental costs. In contrast, demand-based VM placement consolidates VMs based on the actual workload's demand. This may lead to better utilization, but it may incur a higher number of Service Level Agreement Violations (SLAVs) resulting from overloaded PMs and/or VM migrations from one PM to another as a result of workload fluctuations. To control the tradeoff between utilization and the number of SLAVs, parameter-based VM placement can allow a provider, through a single parameter, to explore the whole space of VM placement options that range from demand-based to reservation-based. The idea investigated by this paper is to adjust this parameter continuously at run-time in a way that a provider can maintain the number of SLAVs below a certain (predetermined) threshold while using the smallest possible number of PMs for VM placement. Two dynamic algorithms to select a value of this parameter on-the-fly are proposed. Experiments conducted using CloudSim evaluate the performance of the two algorithms using one synthetic and one real workload.

“…[12][13][14]), in which decisions once made are not reviewed during the lifetime of the VM, or dynamic (e.g. [2,[15][16][17][18]), in which the VM to PM assignment may change during the execution of the VM. As dynamic approaches often make use of information on the actual load that is not available to static approaches, dynamic approaches often use a static approach for initial placement.…”

Section: Virtual Machine Placementmentioning

confidence: 99%

“…Dynamic VM placement proposals have employed heuristics (e.g. [2,12,16,17]), integer linear programming (e.g. [12]) and bespoke algorithms (e.g.…”

Section: Virtual Machine Placementmentioning

confidence: 99%

See 1 more Smart Citation

Optimizing virtual machine placement for energy and SLA in clouds using utility functions

Mosa

Paton

2016

J Cloud Comp

Cloud computing provides on-demand access to a shared pool of computing resources, which enables organizations to outsource their IT infrastructure. Cloud providers are building data centers to handle the continuous increase in cloud users' demands. Consequently, these cloud data centers consume, and have the potential to waste, substantial amounts of energy. This energy consumption increases the operational cost and the CO 2 emissions. The goal of this paper is to develop an optimized energy and SLA-aware virtual machine (VM) placement strategy that dynamically assigns VMs to Physical Machines (PMs) in cloud data centers. This placement strategy co-optimizes energy consumption and service level agreement (SLA) violations. The proposed solution adopts utility functions to formulate the VM placement problem. A genetic algorithm searches the possible VMs-to-PMs assignments with a view to finding an assignment that maximizes utility. Simulation results using CloudSim show that the proposed utility-based approach reduced the average energy consumption by approximately 6 % and the overall SLA violations by more than 38 %, using fewer VM migrations and PM shutdowns, compared to a well-known heuristics-based approach.