Tight Load Balancing Via Randomized Local Search

Enhanced shuffled frog leaping algorithm with improved local exploration and energy-biased load reduction phase for load balancing of gateways in WSNs

Open Computer Science ◽

10.1515/comp-2020-0218 ◽

2021 ◽

Vol 11 (1) ◽

pp. 437-460

Author(s):

Amol Adamuthe ◽

Abdulhameed Pathan

Keyword(s):

Load Balancing ◽

Convergence Rate ◽

Local Search ◽

Heuristic Algorithm ◽

Information Exchange ◽

Load Reduction ◽

Deterministic Approach ◽

Shuffled Frog Leaping Algorithm ◽

Shuffled Frog Leaping ◽

Search Capability

Abstract Wireless sensor networks (WSNs) have grown widely due to their application in various domains, such as surveillance, healthcare, telecommunication, etc. In WSNs, there is a necessity to design energy-efficient algorithms for different purposes. Load balancing of gateways in cluster-based WSNs is necessary to maximize the lifetime of a network. Shuffled frog leaping algorithm (SFLA) is a popular heuristic algorithm that incorporates a deterministic approach. Performance of any heuristic algorithm depends on its exploration and exploitation capability. The main contribution of this article is an enhanced SFLA with improved local search capability. Three strategies are tested to enhance the local search capability of SFLA to improve the load balancing of gateways in WSNs. The first proposed approach is deterministic in which the participation of the global best solution in information exchange is increased. The next two variations reduces the deterministic approach in the local search component of SFLA by introducing probability-based selection of frogs for information exchange. All three strategies improved the success of local search. Second contribution of article is increased lifetime of gateways in WSNs with a novel energy-biased load reduction phase introduced after the information exchange step. The proposed algorithm is tested with 15 datasets of varying areas of deployment, number of sensors and number of gateways. Proposed ESFLA-RW variation shows significant improvement over other variations in terms of successful local explorations, best fitness values, average fitness values and convergence rate for all datasets. Obtained results of proposed ESFLA-RW are significantly better in terms of network energy consumption, load balancing, first gateway die and network life. The proposed variations are tested to check the effect of various algorithm-specific parameters namely frog population size, probability of information exchange and probability of energy-biased load reduction phase. Higher population size and probabilities give better solutions and convergence rate.

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Local search with an exponential neighborhood for the servers load balancing problem

Journal of Applied and Industrial Mathematics ◽

10.1134/s1990478915010044 ◽

2015 ◽

Vol 9 (1) ◽

pp. 27-35

Author(s):

I. A. Davydov ◽

P. A. Kononova ◽

Yu. A. Kochetov

Keyword(s):

Load Balancing ◽

Local Search ◽

Exponential Neighborhood

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Enhancing the dynamic load balancing technique for cloud computing using HBATAABC algorithm

International Journal of Modeling Simulation and Scientific Computing ◽

10.1142/s1793962320500415 ◽

2020 ◽

Vol 11 (05) ◽

pp. 2050041

Author(s):

Arif Ullah ◽

Nazri Mohd Nawi

Keyword(s):

Cloud Computing ◽

Load Balancing ◽

Local Search ◽

Data Center ◽

Bat Algorithm ◽

Network Stability ◽

Cloud Data Center ◽

Abc Algorithm ◽

Cloud Data ◽

Novel Technique

Cloud computing brings incipient transmutations in different fields of life and consists of different characteristics and virtualization is one of them. Virtual machine (VM) is one of the main elements of virtualization. VM is a process in which physical server changes into the virtual machine and works as a physical server. When a user sends data or request for data in cloud data center, a situation can occur that may cause the virtual machines to underload data or overload data. The aforementioned situation can lead to failure of the system or delay the user task. Therefore, appropriate load balancing techniques are required to surmount the above two mentioned problems. Load balancing is a technique utilized in cloud computing for management of the resource by a condition such that a maximum throughput is achieved with slightest reaction time and additionally dividing the traffic between different servers or VM so that it can get data without any delay. For the amelioration of load balancing technique in this study, a novel technique is used which is coalescence of BAT and ABC algorithms both of which are nature-inspired algorithms. When the ABC algorithm local search section changes with BAT algorithm local search section, a second modification takes place in the fitness function of BAT algorithm. The proposed technique is known as HBATAABC algorithm. The novel technique implemented by utilizing transfer strategy policy in VM improves the performance of data allocation system of VM in the cloud data center. To check the performance of the proposed algorithm, three main parameters are used which are network average time, network stability and throughput. The performance of the proposed novel technique is verified and tested with the help of cloudsim simulator. The result shows that the suggested modified algorithm increases performance by 1.30% of network average time, network stability and throughput as compared with BAT algorithm, ABC algorithm and RRA algorithm. Nevertheless, the proposed algorithm is more precise and expeditious as compared with the three models.

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