Consolidating VMs in Green Cloud Computing Using Harmony Search Algorithm

Fathi, Mohammad H.; Khanli, Leyli Mohammad

doi:10.1145/3230348.3230369

Cited by 13 publications

(5 citation statements)

References 19 publications

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“…Best Fit Decreasing (BFD) and First Fit Decreasing (FFD) are similar to BF and FF, but they are preceded by VMs sorting in a decreasing order. On the other hand, there are also some meta-heuristic methods that have been proposed to find the near-optimal places for VMs such as Ant Colony Optimization (ACO) [42], [43], Genetic Algorithm (GA) [44], [45], Simulated Annealing (SA) [46], Improved Lévy based on Whale Optimization Algorithm (ILWOA) [47], Glowworm Swarm Optimisation [48], Harmony Search (HS) [49], Krill Herd (KH) algorithm [50], and hybridized algorithm [51]. A recent detailed work has been introduced to review the state-of-the-art multiobjective techniques based on meta-heuristic algorithms [52].…”

Section: Vm Placementmentioning

confidence: 99%

PAPSO: A Power-Aware VM Placement Technique Based on Particle Swarm Optimization

et al. 2020

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With the widespread usage of cloud computing to benefit from its services, cloud service providers have invested in constructing large scale data centers. Consequently, a tremendous increase in energy consumption has arisen in conjunction with its results, including a remarkable rise in costs of operating and cooling servers. Besides, increasing energy consumption has a significant impact on the environment due to emissions of carbon dioxide. Dynamic consolidation of Virtual Machines (VMs) into the minimal number of Physical Machines (PMs) is considered as one of the magic solutions to manage power consumption. The virtual machine placement problem is a critical issue for good VM consolidation. This paper proposes a Power-Aware technique depending on Particle Swarm Optimization (PAPSO) to determine the near-optimal placement for the migrated VMs. A discrete version of Particle Swarm Optimization (PSO) is adopted based on a decimal encoding to map the migrated VMs to the best appropriate PMs. Furthermore, an effective minimization fitness function is employed to reduce power consumption without violating the Service Level Agreement (SLA). Specifically, PAPSO consolidates the migrated VMs into the minimum number of PMs with a major constraint to decrease the number of overloaded hosts as much as possible. Therefore, the number of VM migrations can be reduced drastically by taking into consideration the main sources for VM migrations; overloaded hosts and underloaded ones. PAPSO is implemented in CloudSim and the experimental results on random workloads with different sizes of VMs and PMs show that PAPSO does not violate SLA and outperforms the Power-Aware Best Fit Decreasing algorithm (PABFD). It can reduce about 8.01%, 39.65%, 66.33%, and 11.87% on average in terms of consumed energy, number of VM migrations, number of host shutdowns and the combined metric Energy SLA Violation (ESV), respectively.

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Section: Vm Placementmentioning

confidence: 99%

PAPSO: A Power-Aware VM Placement Technique Based on Particle Swarm Optimization

et al. 2020

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“…Fathi and Khanli [56] have suggested a new method based on the HS algorithm for the active allotment of VMs, which has been proven to be effective in power management systems. To solve the problem of virtual machine allocation, this algorithm has been used to detect an optimal solution.…”

Section: Harmony Search Algorithmmentioning

confidence: 99%

Nature-Inspired Optimization for Virtual Machine Allocation in Cloud Computing: Current Methods and Future Directions

YANG

2023

IJACSA

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An expanding range of services is offered by cloud data centers. The execution of application tasks is facilitated by assigning (VMs) Virtual Machines to (PMs) Physical Machines. Speaking of VM allocation in the cloud service center, two key factors are taken into consideration: quality of service (QoS) and energy consumption. The cloud service center aims to optimize these aspects while allocating VMs. On the other hand, cloud users have their priorities and focus on their specific requirements, particularly throughput and reliability. User requirements are considered by the cloud service center, resulting in VM allocation that meets QoS targets and optimizes energy consumption. Cloud service centers must, therefore, find a balance between QoS and energy efficiency while considering the user's requirements. To achieve this, various optimization algorithms and techniques must be employed. The objective is to find the best allocation of VMs to PMs. Due to the NP-hardness of the VM allocation problem, nature-inspired meta-heuristic algorithms have become commonly used to solve it. However, there are no comprehensive and in-depth review papers on this specific area. This paper aims to bridge a knowledge gap by providing an understanding of the significance of metaheuristic methods to address the VM allocation issue effectively. It not only highlights the role played by these algorithms but also examines the existing methods, provides comprehensive comparisons of strategies based on key parameters, and concludes with valuable recommendations for future research.

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“…The nature of the harmony search algorithm adopts itself to suit both discrete and continuous variable problems [18]. Virtual machine consolidation is performed using harmony memory search by reducing the number of active machines along with the quality of service requirement [19]. Ant-colony-based consolidation of VMs is utilized to minimize energy consumption with acceptable performance levels [20].…”

Section: Related Workmentioning

confidence: 99%

“…The migration time involves the communication overhead drawn in by the memory reserved by the VM. The communication over head is calculated as Mbw = bw/2, MT = (Mem-size/Mbw) (19) where migration bandwidth (Mbw) and migration time (MT) depends on the memory size (Mem-size) of the VM to be migrated.…”

Section: Maximum Migration With Least Resource Requestmentioning

confidence: 99%

Energy-Efficient Resource Provisioning Using Adaptive Harmony Search Algorithm for Compute-Intensive Workloads with Load Balancing in Datacenters

et al. 2020

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Drastic variations in high-performance computing workloads lead to the commencement of large number of datacenters. To revolutionize themselves as green datacenters, these data centers are assured to reduce their energy consumption without compromising the performance. The energy consumption of the processor is considered as an important metric for power reduction in servers as it accounts to 60% of the total power consumption. In this research work, a power-aware algorithm (PA) and an adaptive harmony search algorithm (AHSA) are proposed for the placement of reserved virtual machines in the datacenters to reduce the power consumption of servers. Modification of the standard harmony search algorithm is inevitable to suit this specific problem with varying global search space in each allocation interval. A task distribution algorithm is also proposed to distribute and balance the workload among the servers to evade over-utilization of servers which is unique of its kind against traditional virtual machine consolidation approaches that intend to restrain the number of powered on servers to the minimum as possible. Different policies for overload host selection and virtual machine selection are discussed for load balancing. The observations endorse that the AHSA outperforms, and yields better results towards the objective than, the PA algorithm and the existing counterparts.

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Consolidating VMs in Green Cloud Computing Using Harmony Search Algorithm

Cited by 13 publications

References 19 publications

PAPSO: A Power-Aware VM Placement Technique Based on Particle Swarm Optimization

PAPSO: A Power-Aware VM Placement Technique Based on Particle Swarm Optimization

Nature-Inspired Optimization for Virtual Machine Allocation in Cloud Computing: Current Methods and Future Directions

Energy-Efficient Resource Provisioning Using Adaptive Harmony Search Algorithm for Compute-Intensive Workloads with Load Balancing in Datacenters

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