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

Abstract

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.

Sustainable Development and Implementation of Quality Management Excellence Models in Public Organizations: A Systematic Literature Review

“…We suggest that future studies consider academic databases, books and English language and non-English language journals for a systematic literature review on a larger scale to study quality models in more detail. In addition, more research ought to be conducted with the help of scientometrics, document analysis, text analysis, text classification and bibliometrics, as well as with big datasets using algorithms related to pipeline-based linear scheduling of big data streams in the cloud [193][194][195][196][197][198][199][200][201].…”

A Novel Method for General Hierarchical System Modeling via Colored Petri Nets Based on Transition Extractions from Real Datasets

“…In our previous work, we implemented a CPN model for resource allocation policies in the cloud, where big data applications are executed [8]. This CPN model combined elements from a series of strategies on cloud systems and resource allocation, which we developed [9][10][11][12][13]. Other fields of PN and CPN applications are mentioned below: parallel processing [14], grid computing applications [15,16], traffic control [17], analysis of safety-critical interactive Systems [18], manufacturing [19], everyday applications [20], supply chain management [21], medicine [22], industry [23], project management [24], fuzzy systems [25], and communication protocols [26,27].…”

VMMISD: An Efficient Load Balancing Model for Virtual Machine Migrations via Fused Metaheuristics With Iterative Security Measures and Deep Learning Optimizations

“…However, the complexity of managing VM migrations in a way that balances load, minimizes energy consumption, and fortifies security poses a significant challenge. Traditional methods often lack the sophistication needed to effectively handle the intricacies of these issues, leading to suboptimal system performance, high energy consumption, and potential security vulnerabilities for different scenarios [1,2,3]. This can be overcome via use of Online VM Prediction-Based Multiple Objective Load Balancing (OPMLB) operations.…”