Honey bee behavior inspired load balancing of tasks in cloud computing environments

Babu, L. D. Dhinesh; Krishna, P. Venkata

doi:10.1016/j.asoc.2013.01.025

Cited by 501 publications

(41 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Since effective load balancing can reduce the makespan considerably therefore in this section, performance of VSBLB is compared with FCFS, WRR and LAGA in terms of makespan i.e., the overall task completion time (Dhinesh Babu and Venkata Krishna, 2013). The initial values of various parameters of algorithm used in VSBLB during simulation are shown in Table 1.…”

Section: Resultsmentioning

confidence: 99%

“…Dhinesh Babu and Venkata Krishna (2013) proposed an algorithm for load balancing of tasks, which is completely inspired by natural foraging behavior of honey bees, which adopt to find and reap food. In bee hives, scout bees forage for food sources and upon finding one, they come back to the beehive and advertise it by a waggle/tremble/vibration dance that gives the idea about the quality and/or quantity of food and its distance from the beehive.…”

Section: Related Workmentioning

confidence: 99%

“…Under this technology, clients can use computational power, software services and platform offered by cloud service providers while paying only for duration those resources have been accessed. This forces the conventional software licensing policies to change and avoids spending of money for the facilities the client does not use in a software package (Dhinesh Babu and Venkata Krishna, 2013).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Application of Viral System Algorithm in Load Balancing of Cloud Environment

Tiwari¹,

Singh²,

Sharma³

2018

Journal of Computer Science

View full text Add to dashboard Cite

Abstract:As the cloud computing technology is gaining popularity with time, more and more users and applications are shifting towards it. This is why clouds are experiencing high load, which demands for load balancing of user tasks submitted to cloud for execution. This makes load balancing of non-preemptive tasks a key issue in cloud computing. Superior task scheduling leads to balanced loads among cloud nodes, which results in faster execution of tasks. Task scheduling in cloud environment is an instance of NP-hard optimization problem. When few nodes in a cloud are overloaded whereas other nodes are under loaded then in such situation the performance of overloaded VMs is diminished. It demands a task scheduling so that the incoming tasks can be distributed uniformly across virtual machines (VMs) for proper utilization of available resources. In this study, we propose a novel load balancing algorithm named Viral System Based Load Balancing (VSB-LB) algorithm, which is based on bio-inspired viral system algorithm that distributes the tasks uniformly among VMs. The proposed algorithm is compared with basic load balancing algorithms such as First Come First Serve (FCFS), Weighted Round Robin (WRR) as well as newer bio-inspired Load balance Aware Genetic Algorithm (LAGA) to show its effectiveness. Simulation results proved that VSBLB outperforms FCFS and WRR and LAGA for performing load balancing.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Application of Viral System Algorithm in Load Balancing of Cloud Environment

Tiwari¹,

Singh²,

Sharma³

2018

Journal of Computer Science

View full text Add to dashboard Cite

show abstract

“…The advantage of using bio-inspired techniques is that they require modelling data structures with low processing overhead while by defining a proper fitness function, the optimal workload placement can be found in relatively short convergence times without processing the entire search space as opposed to classical exhaustive search strategies. For example, the energy efficient relocation of workload across multiple interconnected DCs can be approached exploiting the bees foraging behaviour [11,12]. The GEYSER Optimizer may implement scout mobile agents (similar to scout bees) which randomly migrate from a source DC (the hive) to the interconnected DCs (food sources) aiming at gathering information regarding their operation context such as load levels, SLAs, cross-DCs network capabilities, energy consumption, power source type, and so on.…”

Section: Geyser System Design 31 General Architecturementioning

confidence: 99%

GEYSER: Enabling Green Data Centres in Smart Cities

Anghel

Bertoncini

Cioara

et al. 2015

Energy Efficient Data Centers

View full text Add to dashboard Cite

Abstract. Information Technology is a dominant player of our modern societies; Data Centres, lying at the heart of the IT landscape, have attracted attention, with their increasing energy consumption being a constant topic of concern, especially when it comes to the negative impact on the quality of their surrounding environment. Nevertheless, recent technological and societal advances are paving the way for DCs to change their role from passive energy consumers into prosumers, thus, transforming themselves into leading players within their smart district surroundings. This paper describes the innovative GEYSER approach to enabling green networked DCs to monitor, control, reuse, and optimize both their energy consumption and production, and in particular from renewable resources, towards becoming active participants within Smart Grids and Smart Cities.

show abstract

“…Some researchers focus on mathematical approaches to improve the performance of load balancing algorithms. For example, heuristic optimization and artificial intelligence techniques [8]. 4.…”

Section: Related Researchesmentioning

confidence: 99%

Adaptive Load Balancing Dashboard in Dynamic Distributed Systems

Mirtaheri

Fatemi

Grandinetti

2017

JSFI

View full text Add to dashboard Cite

Considering the dynamic nature of new generation scientific problems, load balancing is a necessity to manage the load in an efficient manner. Load balancing systems are used to optimize the resource consumption, maximize the throughput, minimize response time, and to prevent overload in resources. In current research, we consider operational distributed systems with dynamic variables caused by different nature of the applications and heterogeneity of the various levels in the system. The conducted studies indicate that many different factors should be considered to select the load balancing algorithm, including the processing power, load transfer and communication delay of nodes. In this work, We aim to design a dashboard that is capable of merging the load balancing algorithms in different environments. We design an adaptive system infrastructure with the ability to adjust various factors in the run time of a load balancing algorithm. We propose a task and a resource allocation mechanism and further introduce a mathematical model of load balancing process in the system. We calculate a normalized hardware score that determines the maturity of the system according to the environmental conditions of the load balancing process. The evaluation results confirm that the proposed method performs well and reduces the probability of system failure.

show abstract

Honey bee behavior inspired load balancing of tasks in cloud computing environments

Cited by 501 publications

References 14 publications

Application of Viral System Algorithm in Load Balancing of Cloud Environment

Application of Viral System Algorithm in Load Balancing of Cloud Environment

GEYSER: Enabling Green Data Centres in Smart Cities

Adaptive Load Balancing Dashboard in Dynamic Distributed Systems

Contact Info

Product

Resources

About