Cloud computing is expanding gradually as the number of educational applications is rapidly increasing. To get Educational cloud services, internet connectivity is predominantly important and Cloud Environment uses one of the basic technology to manage the Physical servers effectively ie; Virtualization Technology. In Cloud Computing, the data centers host numerous Virtual Machines (VMs) on top of the Servers. Due to the rapid growth of Educational platforms, the workload of the VM is computationally getting increased. In the Cloud Educational platforms, to execute the jobs IT resources are provisioned over the network. Since the data generated from the client-side is dynamic in nature, it is difficult to allocate the computational resources efficiently. So to enhance the energy efficiency and to provide the resources in an optimized way, a VM Scheduling mechanism with Hybrid Genetic Algorithm-Modified Particle Swarm Optimization (GA-MPSO) is proposed in this work to achieve QoS parameters like reduced Energy consumption, SLA violation, and cost reduction over the heterogeneous environments. The Hybrid G-MPSO develops the optimal range and improves the best range of scheduling the Virtual resources to VMs from Physical Machines (PMs). The proposed approach, when compared to other VM scheduling algorithms, it intensifies the energy consumption to 105KWH, SLA violation rate of 0.08%, reduces the migrations count to 2122, and consumes the overall cost of 2567.68$. The different scheduling methods for VMs are evaluated against the results, which show that the Hybrid GA-MPSO method is far better than the existing algorithms.

Hybrid Genetic Algorithm and Modified-Particle Swarm Optimization Algorithm (GA-MPSO) for Predicting Scheduling Virtual Machines in Educational Cloud Platforms

VM Scheduling for Efficient Dynamically Migrated Virtual Machines (VMS-EDMVM) in Cloud Computing Environment

In the last decade, users can access their applications, data, and services via the cloud from any location with an internet connection. The scale of heterogeneous cloud environments is continuously growing due to the development of computing-intensive smart devices. A data center is the central processing unit of any cloud environment, and it is made up of hardware-oriented machines known as Physical Machines (PMs) or server and software-oriented machines known as Virtual Machines (VMs). The deployment of a huge number of physical servers as a result of the exponential development in demand for cloud services has resulted in high energy consumption and ineffective resource usage. Efficient resource utilization and minimizing power consumption by any data center have become crucial challenges. Virtual machine consolidation(VMC) is a method of optimizing computing resources by consolidating multiple VMs onto a reduced number of PMs. By consolidating VMs and running fewer physical servers, VM consolidation can lead to reducing power consumption and efficient resource utilization. This review paper presents a comprehensive analysis of cloud computing virtual machine consolidation, exploring various strategies, benefits, challenges, and future trends in this domain. By examining a wide range of literature from the year 2015 to 2023, this review attempts to provide insight into the current state of VM consolidation and its possible effects on the performance and sustainability of cloud computing. The main flaw in the articles is that the various authors focused on different assessment metrics while the emphasis should have been on improving energy efficiency and quality of service for cloud systems. Future research can be aimed at developing a multi-objective system that emphasizes minimizing cloud energy usage without sacrificing service quality, preventing service level agreements with cloud users from being compromised. 

A Comprehensive Review of Cloud Computing Virtual Machine Consolidation

In recent years, companies and researchers have hosted and rented computer resources over ‎the ‎‎internet due to cloud computing, which led to an increase in the energy consumed by ‎data centers. This ‎‎consumption is considered one of the world's highest, ‎which pushed many ‎researchers to propose ‎several techniques such as server ‎consolidation (SC) to solve the‎‏ ‏trade‏-‏off‏ ‏‏‎between energy saving and ‎quality of service ‎‎(QoS). SC requires maintaining service level ‎agreements (SLA) violations and ‎minimizing ‎the number of active physical machines (PMs). ‎Furthermore, to achieve this balance and ‎‎avoid ‎increasing hardware costs, the SC challenge targets ‎placing new virtual machines ‎‎(VMs) in ‎suitable PMs. This work explored the existing SC algorithms ‎that include ‎CloudSim as a simulator ‎environment and PlanetLab as a dataset. The authors compared ‎the well-known optimization methods ‎and extracted the weaknesses of the main three deployed ‎‎approaches involved in the consolidation ‎process: bin-packing model, metaheuristics, ‎and machine ‎learning-based solutions.‎

Server Consolidation Algorithms for Cloud Computing

SR-PSO: server residual efficiency-aware particle swarm optimization for dynamic virtual machine scheduling

With the continuous expansion of the scale of cloud datacenters, high energy consumption has become a problem. Virtual machine (VM) consolidation is an effective method of energy management, but overly aggressive consolidation has caused issues such as high service level agreement violation (SLA) rates and excessive migrations. To reduce energy consumption while ensuring the quality of service of a datacenter, a VM dynamic consolidation strategy based on a gray model and an improved discrete particle swarm algorithm (GM-DPSO) is proposed. This strategy uses a gray prediction algorithm for load detection and adjusts the size of the threshold according to the load condition. Then, we select VMs for overloaded and underloaded hosts and complete the placement of VMs through a VM placement strategy based on an improved discrete particle swarm algorithm. This method aims to improve the load balance, establish a mapping between VMs and hosts, use particles to search for the best VM placement in the global scope, and reduce the probability of SLAV. Our algorithm considers the impact of VM migration on system energy consumption and service quality and takes sufficient measures to reduce the number of migrations. To verify the effectiveness and practicality of this strategy, experiments were conducted on actual workloads, and the results were compared with those of other strategies. The experimental results show that the GM-DPSO greatly improves service quality while reducing energy consumption and SLAV by 34.53% and 97.53%, respectively. The methods and theories in this paper provide strong theoretical and practical engineering guidance for large-scale cloud deployment.

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A Dynamic Virtual Machine Resource Consolidation Strategy Based on a Gray Model and Improved Discrete Particle Swarm Optimization

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A Dynamic Virtual Machine Resource Consolidation Strategy Based on a Gray Model and Improved Discrete Particle Swarm Optimization