CloudSec: A security monitoring appliance for Virtual Machines in the IaaS cloud model

2011 ◽  
Author(s):  
Amani S. Ibrahim ◽  
James Hamlyn-Harris ◽  
John Grundy ◽  
Mohamed Almorsy
Keyword(s):  
Virtual Machines ◽  
Cloud Model ◽  
Security Monitoring

scholarly journals Job Scheduling with Efficient Resource Monitoring in Cloud Datacenter

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Shyamala Loganathan ◽  
Saswati Mukherjee
Keyword(s):  
Cloud Computing ◽  
Job Scheduling ◽  
Virtual Machines ◽  
Cloud Model ◽  
Scheduling Algorithm ◽  
Private Cloud ◽  
Resource Monitoring ◽  
Efficient Resource ◽  
Efficient Data ◽  
Guaranteed Service

Cloud computing is an on-demand computing model, which uses virtualization technology to provide cloud resources to users in the form of virtual machines through internet. Being an adaptable technology, cloud computing is an excellent alternative for organizations for forming their own private cloud. Since the resources are limited in these private clouds maximizing the utilization of resources and giving the guaranteed service for the user are the ultimate goal. For that, efficient scheduling is needed. This research reports on an efficient data structure for resource management and resource scheduling technique in a private cloud environment and discusses a cloud model. The proposed scheduling algorithm considers the types of jobs and the resource availability in its scheduling decision. Finally, we conducted simulations using CloudSim and compared our algorithm with other existing methods, like V-MCT and priority scheduling algorithms.

scholarly journals A Virtual Machine Migration Strategy Based on Time Series Workload Prediction Using Cloud Model

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Yanbing Liu ◽  
Bo Gong ◽  
Congcong Xing ◽  
Yi Jian
Keyword(s):  
Time Series ◽  
Virtual Machine ◽  
Virtual Machines ◽  
Cloud Model ◽  
Prediction Algorithm ◽  
Virtual Machine Migration ◽  
Migration Strategy ◽  
Workload Prediction ◽  
Vm Migration ◽  
Host Machine

Aimed at resolving the issues of the imbalance of resources and workloads at data centers and the overhead together with the high cost of virtual machine (VM) migrations, this paper proposes a new VM migration strategy which is based on the cloud model time series workload prediction algorithm. By setting the upper and lower workload bounds for host machines, forecasting the tendency of their subsequent workloads by creating a workload time series using the cloud model, and stipulating a general VM migration criterion workload-aware migration (WAM), the proposed strategy selects a source host machine, a destination host machine, and a VM on the source host machine carrying out the task of the VM migration. Experimental results and analyses show, through comparison with other peer research works, that the proposed method can effectively avoid VM migrations caused by momentary peak workload values, significantly lower the number of VM migrations, and dynamically reach and maintain a resource and workload balance for virtual machines promoting an improved utilization of resources in the entire data center.

scholarly journals Cloud-based Network Virtualization in IoT with OpenStack

2022 ◽  
Vol 22 (1) ◽  
pp. 1-26
Author(s):  
Zakaria Benomar ◽  
Francesco Longo ◽  
Giovanni Merlino ◽  
Antonio Puliafito
Keyword(s):  
Cloud Computing ◽  
Overlay Networks ◽  
Virtual Machines ◽  
Cloud Model ◽  
Network Virtualization ◽  
Bare Metal ◽  
Infrastructure As A Service ◽  
Model Network ◽  
The Core ◽  
Geographically Distributed

In Cloud computing deployments, specifically in the Infrastructure-as-a-Service (IaaS) model, networking is one of the core enabling facilities provided for the users. The IaaS approach ensures significant flexibility and manageability, since the networking resources and topologies are entirely under users’ control. In this context, considerable efforts have been devoted to promoting the Cloud paradigm as a suitable solution for managing IoT environments. Deep and genuine integration between the two ecosystems, Cloud and IoT, may only be attainable at the IaaS level. In light of extending the IoT domain capabilities’ with Cloud-based mechanisms akin to the IaaS Cloud model, network virtualization is a fundamental enabler of infrastructure-oriented IoT deployments. Indeed, an IoT deployment without networking resilience and adaptability makes it unsuitable to meet user-level demands and services’ requirements. Such a limitation makes the IoT-based services adopted in very specific and statically defined scenarios, thus leading to limited plurality and diversity of use cases. This article presents a Cloud-based approach for network virtualization in an IoT context using the de-facto standard IaaS middleware, OpenStack, and its networking subsystem, Neutron. OpenStack is being extended to enable the instantiation of virtual/overlay networks between Cloud-based instances (e.g., virtual machines, containers, and bare metal servers) and/or geographically distributed IoT nodes deployed at the network edge.

C-FDLA: Crow Search with Integrated Fractional Dragonfly Algorithm for Load Balancing in Cloud Computing Environments

2019 ◽  
Vol 28 (07) ◽  
pp. 1950115 ◽  
Author(s):  
C. Ashok Kumar ◽  
R. Vimala
Keyword(s):  
Load Balancing ◽  
Virtual Machines ◽  
Search Algorithm ◽  
Cloud Model ◽  
Cloud Environment ◽  
Dragonfly Algorithm ◽  
Multi Objective ◽  
Significant Performance ◽  
Objective Model ◽  
Selection Probabilities

Cloud environment provides a shared pool of resources to various users all around the world. The cloud model has the physical machines and the virtual machines for processing the tasks from the users in a parallel manner. In certain situations, the user’s demand may be high, which leads to the overloading of the processing units, and this situation affects the performance of the cloud setup. Several works have introduced the load balancing strategy to balance the load of the cloud environment, but they lack in the ability to reduce the number of task migrations. This paper introduces the load balancing strategy by defining the optimization algorithm and the multi-objective model. This research introduces the Crow search with the integrated Fractional Dragonfly Algorithm (C-FDLA), for load balancing through the hybridization of the Crow Search Algorithm (CSA), Dragonfly Algorithm (DA) and the fractional calculus. Also, the paper uses the multi-objective model based on selection probabilities, the frequency scaling based capacity of the machine and the data length of the task. The performance of the proposed C-FDLA is analyzed under different cloud scenarios, and from the results, it is evident that the proposed work has achieved significant performance with the minimum load of 0.0913 and number of tasks reallocated as 11.

Task Scheduling Algorithm based on Resources Segregation in Cloud Environment

2018 ◽  
pp. 96-101
Author(s):  
Shailendra Raghuvanshi ◽  
Priyanka Dubey
Keyword(s):  
Load Balancing ◽  
Task Scheduling ◽  
Waiting Time ◽  
Virtual Machines ◽  
Scheduling Algorithm ◽  
Cloud Environment ◽  
Task Scheduling Algorithm ◽  
Machine Utilization ◽  
Independent Tasks ◽  
Optimal Machine

Load balancing of non-preemptive independent tasks on virtual machines (VMs) is an important aspect of task scheduling in clouds. Whenever certain VMs are overloaded and remaining VMs are under loaded with tasks for processing, the load has to be balanced to achieve optimal machine utilization. In this paper, we propose an algorithm named honey bee behavior inspired load balancing, which aims to achieve well balanced load across virtual machines for maximizing the throughput. The proposed algorithm also balances the priorities of tasks on the machines in such a way that the amount of waiting time of the tasks in the queue is minimal. We have compared the proposed algorithm with existing load balancing and scheduling algorithms. The experimental results show that the algorithm is effective when compared with existing algorithms. Our approach illustrates that there is a significant improvement in average execution time and reduction in waiting time of tasks on queue using workflowsim simulator in JAVA.

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