Enhanced Active Monitoring Load Balancing algorithm for Virtual Machines in cloud computing

Garg, Shikha; Gupta, Devansh; Dwivedi, R.

doi:10.1109/sysmart.2016.7894546

Cited by 18 publications

(8 citation statements)

References 7 publications

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“…Yakhchi et al 55 considered MMT information to select a VM for migration or allied load balancing. A similar effort was made in Garg et al 56 where authors developed enhanced active monitoring load balancing (EAMLB). In this work, authors primarily focused on reducing the response time rather overall QoS performance which may impact SLAV outcomes.…”

Section: Related Workmentioning

confidence: 89%

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: 89%

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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“…Other VMLB strategies are based on active monitoring to locate the least loaded virtual machine among all the virtual machines. The dynamic resource reconfiguration is performed according to the real time requirements [24], [25]. The aforementioned strategies, just like the majority of LB strategies, are reactive [2].…”

Section: Related Workmentioning

confidence: 99%

A Fair, Dynamic Load Balanced Task Distribution Strategy for Heterogeneous Cloud Platforms Based on Markov Process Modeling

et al. 2022

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Load balancing techniques in cloud computing can be applied at three different levels: Virtual machine load balancing, task load balancing, and resource load balancing. At all levels, load balancing should also be implemented in an efficient manner, to increase system performance. In this paper, we propose a fair, in terms of added workload per VM, task load balancing strategy, that aims to improve the average response time and the makespan of the system in the cloud environment. The problem is formulated as an irreducible finite state Markov process, which is known to have a balance equation for each state. From the balance state probabilities we derive the expected utilizations for the virtual machines (VM), which play a vital role in our task allocation approach. In our model, the Load Balancer (LBer) acts as a central server, which uses our proposed fair task allocation scheme to distribute the incoming tasks in a fair, balanced manner among the virtual machines, taking into account their current state as well as their processing capabilities. Our scheme has been compared to recent algorithms that use the particle swarm optimization and the Honey bee foraging scheme to achieve load balancing. Our experimental results show that our proposed scheme outperforms other state of the art schemes in terms of makespan, average response time, and resource utilization and provides lower degree of imbalance.

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“…However, the unstable concurrency results a performance pressure on load balancing challenges. Dynamic load balancing algorithm is outlined to lower the response time for virtual machines in cloud systems [19]. Not to mention Erlang distributed traffic roots lengthy problems such as load imbalance and linearizability.…”

Section: Literatures Reviewmentioning

confidence: 99%

Cloud computing based load balancing algorithm for erlang concurrent traffic

Jittawiriyanukoon

2020

IJEECS

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<p>The distribution of scheduler from user inquiries in the clouds is complex. In keeping up with the cloud computing environment and the inquirers, the clouds meet with some problematic load balancing complications as an improving load balancing tool induces the rigorous efficiency of the cloud based website’s user access. Overloaded or underloaded conditions originate processing catastrophe regarding the prolonged execution time, bandwidth hog, malfunction, and etc. Besides, to manipulate Erlang concurrent tasks is another skyward situation. Hence, the load balancing is obliged to exhaust all mentioned conditions. The proposed load balancing algorithm for Erlang concurrent tasks (those are and could also be autonomous and unstable.) on VMware workstations is introduced. There are several load patterns within the clouds corresponding to CPU’s load (utilization), memory load (queue size), link capacity load (bandwidth), and so on. The proposed load balancing is to spot underloaded and overloaded conditions then stabilizes the weight amidst computing nodes. There are countless load balancing approaches in the cloud environment to examine performance parameters. A short outline of corresponding performance metrics in the review and their findings are presented. To investigate the fit efficiency of the proposed algorithm, the simulation is applied then results based on the proposed method are compared to the existing ones. The outcomes settle the weight balancing, outperform others when executing Erlang traffic, and are catered in the context.</p>

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Enhanced Active Monitoring Load Balancing algorithm for Virtual Machines in cloud computing

Cited by 18 publications

References 7 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

A Fair, Dynamic Load Balanced Task Distribution Strategy for Heterogeneous Cloud Platforms Based on Markov Process Modeling

Cloud computing based load balancing algorithm for erlang concurrent traffic

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