The fog computing models are getting popular as the demand and capacity of data processing is rising for the various applications every year. The fog computing models incorporate the various task scheduling algorithms for the resource selection among the given list of virtual machines (VMs). The task scheduling models are designed around the various task metrics, which include the task length (time), energy, processing cost etc. for the various purposes. The cost oriented scheduling models are primarily built for the customer's perspectives, and saves them a handful amount of money by efficiently assigning the resources for the tasks. In this paper, we have worked upon the multiple task scheduling models based upon the Local Regression (LR), Inter Quartile Range (IQR), Local Regression Robust (LRR), Non-Power Aware (NPA), Median Absolute Deviation (MAD), Dynamic Voltage and Frequency Scheduling (DVFS) and The Static Threshold (THR) methods using the ifogsim simulation designed with the 50 nodes and 50 virtual machines, i.e. 1 virtual machine per node. All of the models have been implemented using the standard input simulation parameters for the purpose of performance assessment in the various domains, specifically in the time domain and effective consumption of energy. The results obtained from the experiments have shown the overall time of 86,400 seconds during the simulation, where the DVFS has been recorded with the 52.98 kWh consumption of energy, which shows the efficient processing in comparison to the 150.68 kWh of energy consumption in the NPA model. Also, there are no SLA violations recorded during both of the simulation, because no VM migration model has been utilized among both of the implemented models, which clearly shows that the VM migrations are the major cause of SLA violation cases. The LRR (2520 VMs) has been observed as best contender on the basis of mean of number of VM migrations in comparison with LR (2555 VMs), THR (4769 VMs), MAD (5138 VMs) and IQR (5352 VMs).

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Dynamic Task Scheduling using Balanced VM Allocation Policy for Fog Computing Platforms

This book chapter proposes use of Ant Colony Optimization (ACO), a novel computational intelligence technique for balancing loads of virtual machine in cloud computing. Computational intelligence(CI), includes study of designing bio-inspired artificial agents for finding out probable optimal solution. So the central goal of CI can be said as, basic understanding of the principal, which helps to mimic intelligent behavior from the nature for artifact systems. Basic strands of ACO is to design an intelligent multi-agent systems imputed by the collective behavior of ants. From the perspective of operation research, it’s a meta-heuristic. Cloud computing is a one of the emerging technology. It’s enables applications to run on virtualized resources over the distributed environment. Despite these still some problems need to be take care, which includes load balancing. The proposed algorithm tries to balance loads and optimize the response time by distributing dynamic workload in to the entire system evenly.

An Ant-Colony-Based Meta-Heuristic Approach for Load Balancing in Cloud Computing

Resource management is a hotspot issue in distributed systems like cloud computing (CC). It means how to prepare the computational resources, i.e., servers and virtual machines (VMS), to execute the tasks. This paper offers a new approach based on Group Technology (GT)—known as a powerful philosophy for the resource management in cellular manufacturing systems—to deal with the resource management problem in CC. We develop a mathematical model to optimally consolidate the VMs, servers and tasks simultaneously to control several important factors such as task migrations and server load variation, as well as the number of VMs. To test the validity of our proposed model, several small problems are generated randomly and solved by LINGO 9 software. Furthermore, to cope with larger problems, which cannot be solved optimally, a genetic algorithm is proposed. We, finally, compare our methods with the most well-known algorithms in this context, round robin (RR) and first-come, first-served (FCFS) algorithms.

New approach based on group technology for the consolidation problem in cloud computing-mathematical model and genetic algorithm

In premise of ensuring users' QoS, the paper proposes a novel load balancing mechanism in cloud computing environment. The idea takes into account not only the energy efficiency of cloud providers, also the needs of users' QoS. The main work includes: construct QoS model, resource model of cloud infrastructure and the mapping between low-level resource metrics and QoS attributes; model load status of the virtual machine instance, and estimate the resource utilization ratio of the virtual machine cluster quantitatively; design the task scheduling algorithm and elastic scaling algorithm to achieve the tasks distribution and the elastic scaling of the virtual machines cluster respectively. By simulation using CloudSim platform, the result shows that this novel method has better load balance degree and complement time compared with other common algorithms such as round robin and green load, achieving the purpose of optimal load balancing in premise of ensuring QoS.

A Novel Cloud Load Balancing Mechanism in Premise of Ensuring QoS

Cloud computing can provide Virtual Machine (VM) computing resources to meet the growing computational demands. Efficient and flexible resource management is a critical issue in cloud computing context. We present a scheduling study on OpenStack VMs scheduler using CloudSim. We focus it on from a more practical viewpoint, the simulation experiments indicate that each of the different scheduling policies suited to different applications.

Research of Scheduling Strategy on OpenStack

The algorithm for scheduling resources under clouding computing environment is different from that under traditional distributed computing environment because of the high scalability and heterogeneity of computing resources in cloud computing environment. In this paper, a resource-scheduling algorithm based on dynamic load balance is presented. The different data-processing power of nodes in cloud is considered in this algorithm, as well as different data-transferring power and transfer delay between nodes in cloud. The algorithm selects the "best" node to fulfill the task in order to improve the efficiency of cloud computing and minimize the average response time of tasks. And the simulation results show that the algorithm distinctly reduces the average response time of tasks.

Haihua Chang

Papers

A load-balance based resource-scheduling algorithm under cloud computing environment