CLB: A novel load balancing architecture and algorithm for cloud services

Chen, Shang Liang; Chen, Yun Yao; Kuo, Suang Hong

doi:10.1016/j.compeleceng.2016.01.029

Cited by 119 publications

(51 citation statements)

References 7 publications

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“…[26][27][28] Load balancing is distributing the amount of work between two or more computing resources. 29,30 Tasks can be transferred from an overloaded node to an under-loaded one using dynamic algorithms. In static algorithms, it is essential to have prior FIGURE 1 An overview of SDN architecture with its main planes: Data, control, and application plane 19 information of the system, such as communication time, job resource requirements, system nodes processing power, storage media, and memory capacity, etc.…”

Section: Sdn Load Balancingmentioning

confidence: 99%

Nature‐inspired meta‐heuristic algorithms for solving the load balancing problem in the software‐defined network

Neghabi

Navimipour

Hosseinzadeh

et al. 2019

Int J Communication

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The growth of the networks has difficult network management. Recently, a concept called software-defined network (SDN) has been proposed to address this issue, which makes network management more adaptable. Control and forwarding planes are separated in SDN. The control plane is a centralized logical controller that controls the network. The forwarding plane that consists of transfer devices is responsible for transmitting packets. Because the network resources are limited, optimizing the use of resources in the networks is an important issue. Load balancing improves the balanced distribution of loads across multiple resources in order to maximize the reliability and network resources efficiency. SDN controllers can create an optimal load balancing compared to traditional networks because they have a network global view. The load-balancing problem can be solved using many different natureinspired meta-heuristic techniques because it has the NP-complete nature.Hence, for solving load balancing problem in SDN, nature-inspired metaheuristic techniques are important methods. However, to the best of our knowledge, there is not a survey or systematic review on studying these matters. Accordingly, in the area of the load balancing in the SDN, this paper reviews systematically the nature-inspired meta-heuristic techniques. Also, this study demonstrates advantages and disadvantages regarded of the chosen nature-inspired meta-heuristic techniques and considers their algorithms metrics. Moreover, to apply better load balancing techniques in the future, the important challenges of these techniques have been investigated.

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Section: Sdn Load Balancingmentioning

confidence: 99%

Nature‐inspired meta‐heuristic algorithms for solving the load balancing problem in the software‐defined network

Neghabi

Navimipour

Hosseinzadeh

et al. 2019

Int J Communication

View full text Add to dashboard Cite

show abstract

“…Dynamic load balancing usually deals with unpredictable processing loads. Many dynamic load balancing algorithms were proposed for various applications, eg, cloud server optimization and heterogeneous multi‐GPU systems . Yean‐Fu Wen et al analyzes 3 task assignment schemes, FCFS, Min‐Min, and Max‐Min along with dynamic clustering.…”

Section: Related Workmentioning

confidence: 99%

A self‐adaptive load‐dispatching control framework for device data accessing in IoT‐based systems

Wang

Yin

et al. 2017

Int J Communication

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Summary The Internet of things (IoT) information system plays important roles in disposing of huge volumes of real‐time service requests from heterogeneous devices, targeting for different complex application requirements. Load‐dispatching control (LDC) is a key problem to be solved for devices accessing concurrently in cluster systems. Self‐adaptive LDC optimizes the resource allocation to ensure no overloading node, thus, improving the performance of IoT systems. This paper focuses on adaptive dispatching control problem in IoT information system. First, a device data access platform is proposed for reducing the load imbalance and improving the efficiency of data processing. Then, we propose a processing capability prediction model to evaluate the system performance. On the basis of the model, we present a practical self‐adaptive LDC framework with a self‐adaptive control strategy and a load dispatching method. Finally, a case study is given to verify the framework and the control strategy. Experimental results show that the proposed strategy can meet the requirements of dynamic load balancing with the ability to avoid the load imbalance problem, and the LDC‐based device access platform can process data accessing effectively and ubiquitously.

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“…It handles thousands and thousands of machines linked together in a very complex manner. It is good technology to mete hardware, applications, important files, information, and software to other computers on demand . It is an excellent model which works under pay per use methodology.…”

Section: Introductionmentioning

confidence: 99%

“…It is good technology to mete hardware, applications, important files, information, and software to other computers on demand. [1][2][3][4] It is an excellent model which works under pay per use methodology. It provides platform, software, and infrastructure services.…”

mentioning

confidence: 99%

Magnum opus of an efficient hospitality technique for load balancing in cloud environment

Sakthivelmurugan

Vimala

Britto

2018

Concurrency and Computation

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Summary The load balancing approach in the cloud environment is an open issue in the recent days. It helps obtain better resource utilization in cloud environment. It also improves the performance of the system. A good load balancing algorithm in cloud ensures no under loading or over loading in a single host. This paper proposes an algorithm named Hospitality Load Balancing (HLB) algorithm, which aims to balance the load among virtual machines (VM) for increasing the performance and throughput. The proposed algorithm handles preemptive or nonpreemptive tasks, which may be independent. It helps to reduce make span and obtain low task migration. The HLB algorithm has been analyzed and compared with plenty of load balancing technique. An experimental result shows that it is an excellent and effective algorithm when compared with the existing load balancing algorithm. It also illustrates the performance of proposed load balancing scheme with analysis and proves that it attains low makespan and low task migration.

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CLB: A novel load balancing architecture and algorithm for cloud services

Cited by 119 publications

References 7 publications

Nature‐inspired meta‐heuristic algorithms for solving the load balancing problem in the software‐defined network

Nature‐inspired meta‐heuristic algorithms for solving the load balancing problem in the software‐defined network

A self‐adaptive load‐dispatching control framework for device data accessing in IoT‐based systems

Magnum opus of an efficient hospitality technique for load balancing in cloud environment

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