Cost-aware virtual machines placement problem under constraints over a distributed cloud infrastructure

2017 ◽  
Author(s):  
Eya Dhib ◽  
Khaled Boussetta ◽  
Nawel Zangar ◽  
Nabil Tabbane
Keyword(s):  
Virtual Machines ◽  
Cloud Infrastructure ◽  
Placement Problem ◽  
Distributed Cloud

Optimal Dynamic Placement of Virtual Machines in Geographically Distributed Cloud Data Centers

2017 ◽  
Vol 26 (03) ◽  
pp. 1750001 ◽  
Author(s):  
Hana Teyeb ◽  
Nejib Ben Hadj-Alouane ◽  
Samir Tata ◽  
Ali Balma
Keyword(s):  
Virtual Machines ◽  
Time Frame ◽  
Cloud Infrastructure ◽  
Vm Placement ◽  
Vm Migration ◽  
Cloud Data ◽  
Geographically Distributed ◽  
Optimal Dynamic ◽  
And Migration ◽  
Distributed Cloud

In geo-distributed cloud systems, a key challenge faced by cloud providers is to optimally tune and configure the underlying cloud infrastructure. An important problem in this context, deals with finding an optimal virtual machine (VM) placement, minimizing costs, while at the same time, ensuring good system performance. Moreover, due to the fluctuations of demand and traffic patterns, it is crucial to dynamically adjust the VM placement scheme over time. It should be noted that most of the existing studies, however, dealt with this problem either by ignoring its dynamic aspect or by proposing solutions that are not suitable for a geographically distributed cloud infrastructure. In this paper, exact as well as heuristic solutions based on Integer Linear programming (ILP) formulations are proposed. Our work focuses also on the problem of scheduling the VM migration by finding the best migration sequence of intercommunicating VMs that minimizes the resulting traffic on the backbone network. The proposed algorithms execute within a reasonable time frame to readjust VM placement scheme according to the perceived demand. Our aim is to use VM migration as a tool for dynamically adjusting the VM placement scheme while minimizing the network traffic generated by VM communication and migration. Finally, we demonstrate the effectiveness of our proposed algorithms by performing extensive experiments and simulation.

Scheduling Strategies for Business Process Applications in Cloud Environments

2013 ◽  
Vol 5 (4) ◽  
pp. 65-78 ◽  
Author(s):  
Kahina Bessai ◽  
Samir Youcef ◽  
Ammar Oulamara ◽  
Claude Godart ◽  
Selmin Nurcan
Keyword(s):  
Business Processes ◽  
Virtual Machines ◽  
Cloud Infrastructure ◽  
On Demand ◽  
Computing Paradigm ◽  
Elastic Computing ◽  
Cloud Environments ◽  
Scheduling Strategies ◽  
The Many ◽  
Distributed Cloud

The Cloud computing paradigm is adopted for its several advantages like reduction of cost incurred when using a set of resources. However, despite the many proven benefits of using a Cloud infrastructure to run business processes, it is still faced with a major problem that can compromise its success: the lack of guidance for choosing between multiple offerings. Moreover, when running business processes it is difficult to automate all tasks and several objectives often conflicting must be taken into account. For this, the authors propose a set of scheduling strategies for business processes in Cloud contexts. More precisely, the authors propose three bi-criteria complementary approaches for scheduling business processes on distributed Cloud resources while taking into account its elastic computing characteristic that allows users to allocate and release compute resources (virtual machines) on-demand and its business model based on pay as you go. Therefore, it is reasonable to assume that the number of virtual machine is infinite while the number of human resources is finite. Experiment results demonstrate that the proposed approaches present good performances.

Opportunistic Two Virtual Machines Placements in Distributed Cloud Environment

2020 ◽  
Vol 12 (4) ◽  
pp. 13-34
Author(s):  
Kamal Kumar ◽  
Jyoti Thaman
Keyword(s):  
Experimental Validation ◽  
Virtual Machines ◽  
Vm Placement ◽  
Placement Problem ◽  
Basic Aspect ◽  
Active Research ◽  
Placement Algorithm ◽  
Distributed Cloud ◽  
And Storage ◽  
Best Fit

Cloud computing is a potentially tremendous platform and its presence is experienced in day to day life. Most infrastructure and technology enterprises have migrated to a cloud-based infrastructure and storage. With so much dependence on the cloud as a distributed and reliable platform, but a few issues remain as a challenge and provide food for the ever-active research entity. Considering a very basic aspect of VM migration followed by VM placement, one VM at a time is a prominent approach. This article presents a novel idea of placing two VMs at a time. This proposal is a draft of solution for the Two VM Placement problem. The experimental validation was done against a well-known placement algorithm, the power aware best fit decreasing (PABFD). PABFD and TVMP were applied on a given context and results were obtained for three important parameters, which include the number of VM migrations, reallocation means, and energy efficiency. Improvements on these parameters may prove beneficial.

scholarly journals Swarm-Like Distributed Algorithm for Scheduling a Microservice-Based Application to the Cloud Servers

Electronics ◽  
2021 ◽  
Vol 10 (13) ◽  
pp. 1553
Author(s):  
Marian Rusek ◽  
Grzegorz Dwornicki
Keyword(s):  
Distributed Applications ◽  
Practical Implementation ◽  
Live Migration ◽  
Cloud Infrastructure ◽  
Application Development ◽  
Cloud Management ◽  
Cloud Application ◽  
Cloud Servers ◽  
Distributed Cloud ◽  
Better Than

Introduction of virtualization containers and container orchestrators fundamentally changed the landscape of cloud application development. Containers provide an ideal way for practical implementation of microservice-based architecture, which allows for repeatable, generic patterns that make the development of reliable, distributed applications more approachable and efficient. Orchestrators allow for shifting the accidental complexity from inside of an application into the automated cloud infrastructure. Existing container orchestrators are centralized systems that schedule containers to the cloud servers only at their startup. In this paper, we propose a swarm-like distributed cloud management system that uses live migration of containers to dynamically reassign application components to the different servers. It is based on the idea of “pheromone” robots. An additional mobile agent process is placed inside each application container to control the migration process. The number of parallel container migrations needed to reach an optimal state of the cloud is obtained using models, experiments, and simulations. We show that in the most common scenarios the proposed swarm-like algorithm performs better than existing systems, and due to its architecture it is also more scalable and resilient to container death. It also adapts to the influx of containers and addition of new servers to the cloud automatically.

A Multi-Objective Fuzzy Ant Colony Optimization Algorithm for Virtual Machine Placement

2016 ◽  
Vol 5 (4) ◽  
pp. 165-191 ◽  
Author(s):  
Boominathan Perumal ◽  
Aramudhan M.
Keyword(s):  
Ant Colony Optimization ◽  
Virtual Machine ◽  
Bin Packing ◽  
Virtual Machines ◽  
Transition Probability ◽  
Virtual Machine Placement ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Placement Problem ◽  
Multi Objective

In cloud computing, the most important challenge is to enforce proper utilization of physical resources. To accomplish the mentioned challenge, the cloud providers need to take care of optimal mapping of virtual machines to a set of physical machines. In this paper, the authors address the mapping problem as a multi-objective virtual machine placement problem (VMP) and propose to apply multi-objective fuzzy ant colony optimization (F-ACO) technique for optimal placing of virtual machines in the physical servers. VMP-F-ACO is a combination of fuzzy logic and ACO, where we use fuzzy transition probability rule to simulate the behaviour of the ants and the authors apply the same for virtual machine placement problem. The results of fuzzy ACO techniques are compared with five variants of classical ACO, three bin packing heuristics and two evolutionary algorithms. The results show that the fuzzy ACO techniques are better than the other optimization and heuristic techniques considered.

Online Allocation of Virtual Machines in a Distributed Cloud

2017 ◽  
Vol 25 (1) ◽  
pp. 238-249 ◽  
Author(s):  
Fang Hao ◽  
Murali Kodialam ◽  
T. V. Lakshman ◽  
Sarit Mukherjee
Keyword(s):  
Virtual Machines ◽  
Distributed Cloud

Clustering based virtual machines placement in distributed cloud computing

2017 ◽  
Vol 66 ◽  
pp. 1-10 ◽  
Author(s):  
Jiangtao Zhang ◽  
Xuan Wang ◽  
Hejiao Huang ◽  
Shi Chen
Keyword(s):  
Cloud Computing ◽  
Virtual Machines ◽  
Distributed Cloud
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