Communication Cost Aware Resource Efficient Load Balancing (CARELB) Framework for Cloud Datacenter

Background: Load balancing of communication-intensive applications, allowing efficient resource utilization and minimization of power consumption, is a challenging multi-objective virtual machine (VM) placement problem. The communication among inter-dependent VMs, raises network traffic, hampers cloud client’s experience and degrades overall performance by saturating the network. Introduction: Cloud computing has become an indispensable part of Information Technology (IT), which supports digitization throughout the world. It provides a shared pool of IT resources, which are: alwaysactive , accessible from anywhere, at any time and delivered on demand as a service. The scalability and pay-per-use benefits of cloud computing have driven the entire world towards on-demand IT services that facilitate increased usage of virtualized resources. The rapid growth in the demands of cloud resources has amplified the network traffic in and out of the datacenter. Cisco Global Cloud Index predicts that by the year 2021, the network traffic among the devices within the data center will grow at Compound Annual Growth Rate (CAGR) of 23.4%. Methods: To address these issues, a Communication cost Aware and Resource Efficient Load Balancing (CARE-LB) framework is presented that minimizes the communication cost, power consumption and maximizes resource utilization. To reduce the communication cost, VMs with high affinity and inter-dependency are intentionally placed closer to each other. The VM placement is carried out by applying the proposed integration of Particle Swarm Optimization and nondominated sorting based Genetic Algorithm i.e. PSOGA algorithm encoding VM allocation as particles as well as chromosomes. Results: The performance of the proposed framework is evaluated by the execution of numerous experiments in the simulated data center environment and it is compared with state-of-the-art methods like Genetic Algorithm, First-Fit, Random-Fit and Best-Fit heuristic algorithms. The experimental outcome reveals that the CARE-LB framework improves resource utilization by 11%, minimizes power consumption by 4.4% and communication cost by 20.3% with a reduction of execution time up to 49.7% over Genetic Algorithm based Load Balancing framework. Conclusion: The proposed CARE-LB framework provides a promising solution for faster execution of data-intensive applications with improved resource utilization and reduced power consumption. Discussion: In the observed simulation, we analyzed all the three objectives after the execution of the proposed multi-objective VM allocations and results are shown in Table 4. To choose the number of users for analysis of communication cost, the experiments were conducted with different numbers of users. For instance, for 100 VMs, we chose 10, 20 ,..., 80 users, and their request for VMs (number of VMs and type of VMs) were generated randomly, such that the total number of requested VMs did not exceed the number of available VMs.