Load balancing algorithm based on estimating finish time of services in cloud computing

Fog-based VANETs (Vehicular Ad hoc NETworks) is a new model with vehicular cloud and fog computing benefits. Fog-based VANETs consist of a series of mobile nodes that are fully dynamic without any central management. These networks have some limitations due to the use of the mobile power source. There are many challenges in extending mobilebased networks' life span. Many methods have been suggested to minimize the nodes' energy consumption and extend the network life. Also, some works have been developed for load-balanced routing. Still, energy-efficient routing and load balancing in VANETs are challenges. The purpose of this study is to present a suitable method based on energy awareness for load balancing in fog-based VANETs using a hybrid optimization algorithm (employing ant colony optimization and artificial bee colony (ACO-ABC)). The simulation results in the Network Simulator 2 (NS2) environment showed that as the number of nodes increased, the consumed amount of energy in the VANET is increased. Also, with the increasing number of tasks, load balancing by the proposed method has been improved. Finally, the simulation experiments showed that the proposed hybrid algorithm (ACO-ABC) outperforms all other algorithms.1 Recently, extending cloud computing to the network's edge is very hot topic, for example, deploying fog nodes to the network edge [4][5][6]. In other words, computational capabilities are pushed to devices located on the edge of the network based on the internet "decentralization" feature. It lets some services be decentralized from the cloud to the fog devices [7,8], reducing data transmission delay, bandwidth consumption, computa-This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…The degree of imbalance measures among nodes is computed using Equation () [51].

D I = \frac{T_{m a x} - T_{m i n}}{T_{a v g}}

where T max is the maximum, and T i is the minimum amongst all nodes, and T avg is calculated by Equation ().…”

Section: Resultsmentioning

confidence: 99%

A new energy‐aware method for load balance managing in the fog‐based vehicular ad hoc networks (VANET) using a hybrid optimization algorithm

Ren

Arefzadeh

2021

IET Communications

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“…VM's space shared (SS) scheduling: It is a VM's allocation policy that allocates one or more processing elements (Pes) to a VM and does not allow sharing of Pes. For implementing the scheduling policy, VM and cloudlet scheduler operate together in the following combination (Chien et al, 2016;Calheiros et al, 2011):…”

Section: Cloudsim Scheduling Policymentioning

confidence: 99%

“…If the scheduling policy is TS-TS, then according to (Chien et al, 2016), capacity is calculated as:…”

Section: Response Timementioning

confidence: 99%

“…Load balancing is measured in term of load and load performance. Load is assessed by the CPU queue index and CPU utilization while performance is assessed in term of the average response time of (Chien et al, 2016). In this study, performance of virtual machine is measured by the consumption of VM's CPU by each cloudlets at that current time (Ahmad & Khan, 2019;Cloudsim.org, 2013).…”

Section: Vm's Cpu Utilizationmentioning

confidence: 99%

“…On analysis, this scheme outperform state-of-the-art scheduling in term of waiting time and turnaround time besides other parameters. Chien et al in (2016) evaluate the average RT and processing time in TS and SS scheduling cases. The RT is based on the capacity, which is defined as the average processing power of the core elements of the VM, and the execution finish time.…”

Section: Introductionmentioning

confidence: 99%

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Efficient Cloudlet Allocation to Virtual Machine to Impact Cloud System Performance

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Yadav

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Performance is an essential characteristic of any cloud computing system. It can be enhance through parallel computing, scheduling and load balancing. This work evaluates the connection between the response time (RT) and virtual machine’s (VM) CPU utilization when cloudlets are allocated from the datacenter broker to VM. To accentuate the RT and VM’s CPU utilization, a set of 100 and 500 heterogeneous cloudlets are analyzed under hybridized provisioning, scheduling and allocation algorithm using CloudSim simulator. These includes space shared (SS) and time shared (TS) provisioning policy, shortest job first (SJF), first come first search (FCFS), round robin (RR) and a novel length-wise allocation (LwA) algorithm. The experimental analysis shows that the RT is the least when SJF is combined with RR allocation at 40.665 secs and VM’s CPU utilization is the least when SJF is combined with LwA policy at 12.48 in all combinations of SS and TS provisioning policy.

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Multi‐objective approach of energy efficient workflow scheduling in cloud environments

Rehman

Hussain

Rehman

et al. 2018

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Scheduling the tasks of a workflow to the cloud resources is a well-known N-P hard problem. The stakeholders involved in a cloud environment have different interests in scheduling problem. In addition to the traditional objectives like makespan, budget, and deadline, optimized in workflow scheduling, considering the green aspect of cloud, (ie, energy consumption) increase the problem complexity. Moreover, the interests of a cloud's stakeholders are conflicting, and satisfying all these interests simultaneously is a big problem. In this paper, we proposed a new Multi-Objective Genetic Algorithm(MOGA) for workflow scheduling in a cloud environment. MOGA considered the conflicting interest of the cloud stakeholders for optimization and provided a solution, which not only minimizes the makespan under the budget and deadline constraints but also provided an energy efficient solution using the dynamic voltage frequency scaling. We provided a gap search algorithm in this paper, which is used to optimize the resource utilization of the cloud's resources.We compared our results with genetic algorithms considering the budget, deadline, and energy efficiency individually. We also compared the performance of MOGA with Multi-objective Particle Swarm Optimization (MOPSO) with the same objectives as those of MOGA. To the best of our knowledge, there is no solution presented in the literature that considers the diverse objectives considered in this work. The results show that our proposed algorithm MOGA has significantly improved not only in terms of budget, deadline, and energy but also improved the utilization of cloud's resources as compared to the competitive algorithms of this work.

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Load balancing algorithm based on estimating finish time of services in cloud computing

Cited by 16 publications

References 2 publications

A new energy‐aware method for load balance managing in the fog‐based vehicular ad hoc networks (VANET) using a hybrid optimization algorithm

A new energy‐aware method for load balance managing in the fog‐based vehicular ad hoc networks (VANET) using a hybrid optimization algorithm

Efficient Cloudlet Allocation to Virtual Machine to Impact Cloud System Performance

Multi‐objective approach of energy efficient workflow scheduling in cloud environments

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