A Load Balancing and Multi-Tenancy Oriented Data Center Virtualization Framework

Duan, Jun; Yang, Yuanyuan

doi:10.1109/tpds.2017.2657633

Cited by 41 publications

(10 citation statements)

References 31 publications

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“…In Tao et al, 49 a load feedback‐based resource scheduling and VM migration model was developed, although its probability to undergo iterative hotspot generation could not be avoided. In continuation of Zhang et al, 42 Zhao and Huang, 45 and Wang et al, 46 Duan and Yang 50 used link information and VM–host mapping information to perform load balancing or allied VM migration control. Similar to Zhao and Huang, 45 Ningning et al 51 used delay and resource utilization‐sensitive migration scheduling for load balancing.…”

Section: Related Workmentioning

confidence: 99%

Multilevel controller‐assisted intrinsically modified ant colony optimization heuristic‐based load‐balancing model for mega cloud infrastructures

Govardhan

Srinivasan

2022

Int J Communication

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Summary The exponential rise in decentralized computing demands has made cloud computing an inevitable technical need; however, under exceedingly high load dynamism, ensuring service level agreement (SLA) has remained a challenge. Despite of the fact that heuristic‐driven VM migration or load‐balancing methods are effective towards quality of service (QoS) and reliable cloud services, the major existing solutions ignore the issues of local minima and convergence and hence undergo SLA violation (SLAV). Moreover, most of the existing methods are designed as a reactive scheduling concept that often results into delayed scheduling and hence SLAV or QoS compromise. To alleviate such issues, in this paper, a robust intrinsically modified evolutionary computing‐driven dynamic load‐balancing model (intrinsically modified ant colony system [IMACS]) is proposed for mega cloud infrastructures. To address aforesaid issues, IMACS employs stochastic prediction‐based overload detection, followed by SLA‐sensitive VM selection and IMACS for load balancing. In IMACS, the use of stochastic prediction‐based overload detection method enabled timely hotspot detection and hence improved proactive decision ability. Moreover, it applies minimum migration time‐sensitive VM selection concept that too helped in guaranteeing load balancing without undergoing downtime or SLAV. To improve VM migration, unlike classical heuristic methods, we designed IMACS with multitype population, adaptive pheromone update, adaptive pheromone diffusion, and coevolution concept, which not only helped in reducing the search space and allied computation but also enabled time‐efficient scheduling decision. The overall proposed model was realized in sync with two controllers named local controller and global controller. Here, the first controller collects the real‐time VM and VM manager (VMM) information which is used to detect overloaded host and the VM to be migrated. While the global controller is designed with IMACS to exploit the information from local controller to execute QoS‐sensitive VM migration. The proposed IMACS‐driven load‐balancing model was realized with CloudSim platform were simulating it over 800 number of independent VMs, each encompassing autonomous task, the performance was analyzed in terms of SLAV, number of migrations, SLA per active host, and energy consumption. Simulation results revealed that the proposed model exhibits 20% higher migration, while retaining almost 19% better SLA in comparison to the other state‐of‐art methods like genetic algorithm or classical ant colony system‐based load‐balancing methods. IMACS exhibited 30% higher host shutdown signifying better energy efficiency (15% lower power consumption).

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Section: Related Workmentioning

confidence: 99%

Multilevel controller‐assisted intrinsically modified ant colony optimization heuristic‐based load‐balancing model for mega cloud infrastructures

Govardhan

Srinivasan

2022

Int J Communication

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“…The heterogeneous VM configuration adds more difficulty to assign user requests and balancing the load among available VM's. Today load balancing has become a very complex task and considered as a key challenge in cloud computing [5][6][7][8][9].…”

Section: Motivationmentioning

confidence: 99%

Load Balancing Algorithm for Efficient VM Allocation in Heterogeneous Cloud

Mulla¹,

Krishna

Tickoo³

2020

SSRN Journal

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Cloud computing is a growing service computing trend that offers users a range of on-demand variety of services from applications, processing capability, and storage based on the concept of the "Pay-As-Per-Use" model. Organizations from every sector are now realizing the benefits offered by cloud computing technology and moving towards the cloud. Cloud computing offers numerous advantages over conventional computing. However, it still faces a few challenges such as resource utilization in a cloud data centre and quality of service to the end-users due to improper workload balance among available resources. Heterogeneous cloud resources also impact cloud systems overall performance. We proposed an enhanced load balancing algorithm in this research paper for efficient VM allocation in a heterogeneous cloud. Our proposed algorithm allocates independent user tasks or requests to available virtual machines in cloud datacentre efficiently to manage proper load balancing. This algorithm is aimed at minimizing user request response time and the time required for data centre processing. The results obtained showed a significant reduction in user request response time and data centre processing time as compared to "Throttled" and "Round Robin (RR)" algorithms.

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“…Multi-tenancy mainly works for sharing of resources in a cloud whether it may be public or private. The models used in this paper [16] are Fat tree networking and Hose traffic model. As we know Fat tree is three-tier architecture but for the convenience it is abstracted into two-tier.…”

Section: Related Workmentioning

confidence: 99%

A Novel Energy Efficient Scheduling for VM Consolidation and Migration in Cloud Data Centers

Yakobu¹,

Chirra²,

Sistla³

2019

ISI

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In data centers, the energy-efficient scheduling of virtual machines (VMs) is critical to the full utilization of physical machines (PMs). Considering the sheer amount of data in cloud environment, this paper puts forward a novel energy-efficient scheduling method for VM consolidation and migration in cloud data centers. The proposed method optimizes the energy consumption at cloud data centers through three algorithms: the first algorithm describes the general migration of VMs among PMs; the second algorithm defines the migration of VMs among PMs; the third algorithm explains how the migration takes place. The effectiveness of our method was demonstrated on CloudSim with 5 PMs and 30 VMs, under the constraints of arrival time and deadline. The results show that our method can balance the load of input jobs and schedule the VMs properly, thus reducing the carbon emissions at the cloud data center.

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A Load Balancing and Multi-Tenancy Oriented Data Center Virtualization Framework

Cited by 41 publications

References 31 publications

Multilevel controller‐assisted intrinsically modified ant colony optimization heuristic‐based load‐balancing model for mega cloud infrastructures

Multilevel controller‐assisted intrinsically modified ant colony optimization heuristic‐based load‐balancing model for mega cloud infrastructures

Load Balancing Algorithm for Efficient VM Allocation in Heterogeneous Cloud

A Novel Energy Efficient Scheduling for VM Consolidation and Migration in Cloud Data Centers

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