Resource‐efficient load‐balancing framework for cloud data center networks

Kumar, Jitendra; Singh, Anurag; Mohan, Anand

doi:10.4218/etrij.2019-0294

Cited by 20 publications

(12 citation statements)

References 53 publications

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“…Chen et al [31] consider the problem of LB in a multi-objective framework, where initially the problem of resource allocation for emergent demands is resolved. In [32] the authors present a loadbalancing framework with the objective of minimizing the operational cost of data centers using a genetic algorithm for resource allocation. Weight factors have also been employed for resources like physical memory, bandwidth, number of processors, and processor speed [33].…”

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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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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“…Li et al [31] concluded that certain bin-packing strategies were considered for renting 3 Wireless Communications and Mobile Computing servers in the cloud. Some approaches have been studied for connecting the bin-packing problem to the centralized scheduling problem [29,30,[32][33][34][35][36][37].…”

Section: Related Workmentioning

confidence: 99%

Fuzzy‐Based Mobile Edge Orchestrators in Heterogeneous IoT Environments: An Online Workload Balancing Approach

Khanh

Nguyen

Huh

2021

Wireless Communications and Mobile Computing

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Online workload balancing guarantees that the incoming workloads are processed to the appropriate servers in real time without any knowledge of future resource requests. Currently, by matching the characteristics of incoming Internet of Things (IoT) applications to the current state of computing and networking resources, a mobile edge orchestrator (MEO) provides high-quality service while temporally and spatially changing the incoming workload. Moreover, a fuzzy-based MEO is used to handle the multicriteria decision-making process by considering multiple parameters within the same framework in order to make an offloading decision for an incoming task of an IoT application. In a fuzzy-based MEO, the fuzzy-based offloading strategy leads to unbalanced loads among edge servers. Therefore, the fuzzy-based MEO needs to scale its capacity when it comes to a large number of devices in order to avoid task failures and to reduce service times. In this paper, we investigate and propose an online workload balancing algorithm, which we call the fuzzy-based (FuB) algorithm, for a fuzzy-based MEO. By considering user configuration requirements, server geographic locations, and available resource capacities for achieving an online algorithm, our proposal allocates the proximate server for each incoming task in real time at the MEO. A simulation was conducted in augmented reality, healthcare, compute-intensive, and infotainment applications. Compared to two benchmark schemes that use the fuzzy logic approach for an MEO in IoT environments, the simulation results (using EdgeCloudSim) show that our proposal outperforms the existing algorithms in terms of service time, the number of failed tasks, and processing times when the system is overloaded.

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“…20-36 repeat till accomplishment of termination criteria (by utilizing fourth stage of Section 4).A), where step 21 evaluates cost values of four objectives using Eqs. ( 5), ( 6), (9), and (11) associated to each solution of g th iteration (i.e., ψ g ). Step…”

Section: B Operational Design and Complexity Computationmentioning

confidence: 99%

“…To meet the ever-growing and dynamic demand of users, more and more VMs are deployed on a large number of servers and cooling devices are installed at data centre that account for high power consumption [6], [7], [8]. The VM placement (VMP) has a crucial impact on resource utilization, power consumption, and the overall operational cost of the data centre [9], [10], [11]. The inefficient VMP leads to under/over-utilized servers with increased power consumption and resource wastage.…”

Section: Introductionmentioning

confidence: 99%

A Secure and Multi-objective Virtual Machine Placement Framework for Cloud Data Centre

Saxena,

Gupta,

Kumar

et al. 2021

Preprint

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To facilitate cost-effective and elastic computing benefits to the cloud users, the energy-efficient and secure allocation of virtual machines (VMs) plays a significant role at the data centre. The inefficient VM Placement (VMP) and sharing of common physical machines among multiple users leads to resource wastage, excessive power consumption, increased inter-communication cost and security breaches. To address the aforementioned challenges, a novel secure and multi-objective virtual machine placement (SM-VMP) framework is proposed with an efficient VM migration. The proposed framework ensures an energy-efficient distribution of physical resources among VMs that emphasizes secure and timely execution of user application by reducing inter-communication delay. The VMP is carried out by applying the proposed Whale Optimization Genetic Algorithm (WOGA), inspired by whale evolutionary optimization and nondominated sorting based genetic algorithms. The performance evaluation for static and dynamic VMP and comparison with recent state-of-the-arts observed a notable reduction in shared servers, inter-communication cost, power consumption and execution time up to 28.81%, 25.7%, 35.9% and 82.21%, respectively and increased resource utilization up to 30.21%.

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Resource‐efficient load‐balancing framework for cloud data center networks

Cited by 20 publications

References 53 publications

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

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

Fuzzy‐Based Mobile Edge Orchestrators in Heterogeneous IoT Environments: An Online Workload Balancing Approach

A Secure and Multi-objective Virtual Machine Placement Framework for Cloud Data Centre

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