Bio-inspired Load Balancing Algorithm in Cloud Computing

Gamal, Marwa; Rizk, Rawya; Mahdi, Hani; Elhady, Basem

doi:10.1007/978-3-319-64861-3_54

Cited by 24 publications

(16 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [26], Hybrid artificial Bee and Ant Colony optimization (H_BAC) algorithm is proposed to perform load balancing in VMs placement among hosts. It shows the strong points of both ACO and ABC.…”

Section: Related Workmentioning

confidence: 99%

Osmotic Bio-Inspired Load Balancing Algorithm in Cloud Computing

et al. 2019

Self Cite

View full text Add to dashboard Cite

Cloud computing is increasing rapidly as a successful paradigm presenting on-demand infrastructure, platform, and software services to clients. Load balancing is one of the important issues in cloud computing to distribute the dynamic workload equally among all the nodes to avoid the status that some nodes are overloaded while others are underloaded. Many algorithms have been suggested to perform this task. Recently, worldview is turning into a new paradigm for optimization search by applying the osmosis theory from chemistry science to form osmotic computing. Osmotic computing is aimed to achieve balance in highly distributed environments. The main goal of this paper is to propose a hybrid metaheuristics technique which combines the osmotic behavior with bio-inspired load balancing algorithms. The osmotic behavior enables the automatic deployment of virtual machines (VMs) that are migrated through cloud infrastructures. Since the hybrid artificial bee colony and ant colony optimization proved its efficiency in the dynamic environment in cloud computing, the paper then exploits the advantages of these bio-inspired algorithms to form an osmotic hybrid artificial bee and ant colony (OH_BAC) optimization load balancing algorithm. It overcomes the drawbacks of the existing bio-inspired algorithms in achieving load balancing between physical machines. The simulation results show that OH_BAC decreases energy consumption, the number of VMs migrations and the number of shutdown hosts compared to existing algorithms. In addition, it enhances the quality of services (QoSs) which is measured by service level agreement violation (SLAV) and performance degradation due to migrations (PDMs). INDEX TERMS Ant colony optimization, artificial bee colony, bio-inspired systems, cloud computing, load balancing, metaheuristics, osmotic computing.

show abstract

Section: Related Workmentioning

confidence: 99%

Osmotic Bio-Inspired Load Balancing Algorithm in Cloud Computing

et al. 2019

Self Cite

View full text Add to dashboard Cite

show abstract

“…A Hybrid Artificial Bee and Ant Colony optimization (H_BAC) load balancing algorithm is proposed in (Gamal et al, 2017). It inherits the main behaviors of both ACO and ABC algorithms and takes into consideration monitoring the load of Virtual machines (VMs) and the decision of load balancing before scheduling tasks in VMs.…”

Section: Related Workmentioning

confidence: 99%

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

“…In the context of the Internet of Things (IoT), the cloud provides a scalable way to manage all aspects of an IoT deployment. But particularly for time-sensitive applications, there are some downsides to the cloud that are better addressed at the edge [1][2][3][4]. Fog computing is a promising solution to deal with the demands of the ever-increasing number of Internet-connected devices.…”

Section: Introductionmentioning

confidence: 99%

IoT Application Placement Algorithm Based on Multi-Dimensional QoE Prioritization Model in Fog Computing Environment

2020

Self Cite

View full text Add to dashboard Cite

with the increasing popularity of Internet of Things (IoT) applications in many different areas, the round-trip delay of data processing in the cloud affect the user's quality perception. But fortunately, fog computing aims to service users at the network edge similar to cloud services, which helps in supporting IoT in processing the data near to the end-user, especially for time-sensitive applications. It makes the resource allocation of application placement requests in a fog environment more necessary to satisfy the Quality of Experience (QoE) Influence Factors (IFs). In this paper, an IoT application placement algorithm based on the Multi-Dimensional QoE (MD-QoE) model is proposed in a fog computing environment. The algorithm is composed of two main phases. The first phase is to prioritize different IoT application placement requests depending on three main domains of IFs which are: Environment runtime context, application usage, and user expectations considering the Quality of Service (QoS) violation as a feedback. The second phase is to map and place the request to the appropriate fog node instance, depending on its proximity, computing capabilities, and expected response time. The proposed algorithm is evaluated by simulating a fog environment using iFogSim. Experimental results indicate that the proposed algorithm significantly improves the QoE in respect of application placement time, application delay, network usage, and power consumption. Therefore, the proposed algorithm can improve the overall system performance with a slight increasing in power consumption in fog control nodes.

show abstract

Bio-inspired Load Balancing Algorithm in Cloud Computing

Cited by 24 publications

References 8 publications

Osmotic Bio-Inspired Load Balancing Algorithm in Cloud Computing

Osmotic Bio-Inspired Load Balancing Algorithm in Cloud Computing

Application of Viral System Algorithm in Load Balancing of Cloud Environment

IoT Application Placement Algorithm Based on Multi-Dimensional QoE Prioritization Model in Fog Computing Environment

Contact Info

Product

Resources

About