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.

scholarly journals Virtual Machine Placement via Bin Packing in Cloud Data Centers

Electronics ◽  
2018 ◽  
Vol 7 (12) ◽  
pp. 389 ◽  
Author(s):  
Aisha Fatima ◽  
Nadeem Javaid ◽  
Tanzeela Sultana ◽  
Waqar Hussain ◽  
Muhammad Bilal ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Data Centers ◽  
Bin Packing ◽  
Virtual Machines ◽  
Particle Swarm ◽  
Vm Placement ◽  
Swarm Optimization ◽  
Cloud Data ◽  
Cloud Data Centers ◽  
Vm Consolidation

With the increasing size of cloud data centers, the number of users and virtual machines (VMs) increases rapidly. The requests of users are entertained by VMs residing on physical servers. The dramatic growth of internet services results in unbalanced network resources. Resource management is an important factor for the performance of a cloud. Various techniques are used to manage the resources of a cloud efficiently. VM-consolidation is an intelligent and efficient strategy to balance the load of cloud data centers. VM-placement is an important subproblem of the VM-consolidation problem that needs to be resolved. The basic objective of VM-placement is to minimize the utilization rate of physical machines (PMs). VM-placement is used to save energy and cost. An enhanced levy-based particle swarm optimization algorithm with variable sized bin packing (PSOLBP) is proposed for solving the VM-placement problem. Moreover, the best-fit strategy is also used with the variable sized bin packing problem (VSBPP). Simulations are done to authenticate the adaptivity of the proposed algorithm. Three algorithms are implemented in Matlab. The given algorithm is compared with simple particle swarm optimization (PSO) and a hybrid of levy flight and particle swarm optimization (LFPSO). The proposed algorithm efficiently minimized the number of running PMs. VM-consolidation is an NP-hard problem, however, the proposed algorithm outperformed the other two algorithms.

scholarly journals On demand clock synchronization for live VM migration in distributed cloud data centers

2020 ◽  
Vol 138 ◽  
pp. 15-31 ◽  
Author(s):  
Yashwant Singh Patel ◽  
Aditi Page ◽  
Manvi Nagdev ◽  
Anurag Choubey ◽  
Rajiv Misra ◽  
...  
Keyword(s):  
Data Centers ◽  
Clock Synchronization ◽  
Vm Migration ◽  
Cloud Data ◽  
On Demand ◽  
Cloud Data Centers ◽  
Distributed Cloud

scholarly journals Low Power Consumption on Cloud Data Centers Using HSA Algorithm

2020 ◽  
pp. 78-84
Author(s):  
Louay Al Nuaimy ◽  
Tadele Debisa Deressa ◽  
Mohammad Mastan ◽  
Syed Umar
Keyword(s):  
Energy Consumption ◽  
Virtual Machine ◽  
Virtual Machines ◽  
Search Algorithm ◽  
Rapid Development ◽  
Electricity Consumption ◽  
Cloud Infrastructure ◽  
Virtual Machine Migration ◽  
Cloud Data ◽  
The Right

The rapid development of knowledge and communication has created a new processing style called cloud computing. One of the key issues facing cloud infrastructure providers is minimizing costs and maximizing profitability. Power management in cloud centres is very important to achieve this. Energy consumption can be reduced by releasing inactive nodes or by reducing the migration of virtual machines. The second is one of the challenges of choosing the virtual machine deployment method to migrate to the right node. This article proposes an approach to reduce electricity consumption in cloud centres. This approach adapts Harmony's search algorithm to move virtual machines. Positioning is done by sorting nodes and virtual machines according to their priorities in descending order. Priority is calculated based on the workload. The proposed procedure is envisaged. The evaluation results show less virtual machine migration, greater efficiency and lower energy consumption.

scholarly journals Placement for Intercommunicating Virtual Machines in Autoscaling Cloud Infrastructure

2021 ◽  
Vol 33 (2) ◽  
pp. 17-35
Author(s):  
Sridharan R. ◽  
Domnic S.
Keyword(s):  
Resource Utilization ◽  
Optimization Problem ◽  
Virtual Machines ◽  
Operating Cost ◽  
Cloud Infrastructure ◽  
Vm Placement ◽  
Application Performance ◽  
Processing Application ◽  
Computing Power ◽  
Additional Constraints

Due to pay-as-you-go style adopted by cloud datacenters (DC), modern day applications having intercommunicating tasks depend on DC for their computing power. Due to unpredictability of rate at which data arrives for immediate processing, application performance depends on autoscaling service of DC. Normal VM placement schemes place these tasks arbitrarily onto different physical machines (PM) leading to unwanted network traffic resulting in poor application performance and increases the DC operating cost. This paper formulates autoscaling and intercommunication aware task placements (AIATP) as an optimization problem, with additional constraints and proposes solution, which uses the placement knowledge of prior tasks of individual applications. When compared with well-known algorithms, CloudsimPlus-based simulation demonstrates that AIATP reduces the resource fragmentation (30%) and increases the resource utilization (18%) leading to minimal number of active PMs. AIATP places 90% tasks of an application together and thus reduces the number of VM migration (39%) while balancing the PMs.

scholarly journals Heuristic-based Approach for Dynamic Consolidation of Software Licenses in Cloud Data Centers

2021 ◽  
Vol 13 (6) ◽  
pp. 1-12
Author(s):  
Leila Helali ◽  
◽  
Mohamed Nazih Omri
Keyword(s):  
Cloud Computing ◽  
Data Centers ◽  
Virtual Machines ◽  
Heuristic Algorithms ◽  
Energy Aware ◽  
Computing Systems ◽  
Cloud Data ◽  
Software Licenses ◽  
Cloud Data Centers ◽  
Distributed Cloud

Since its emergence, cloud computing has continued to evolve thanks to its ability to present computing as consumable services paid by use, and the possibilities of resource scaling that it offers according to client’s needs. Models and appropriate schemes for resource scaling through consolidation service have been considerably investigated,mainly, at the infrastructure level to optimize costs and energy consumption. Consolidation efforts at the SaaS level remain very restrained mostly when proprietary software are in hand. In order to fill this gap and provide software licenses elastically regarding the economic and energy-aware considerations in the context of distributed cloud computing systems, this work deals with dynamic software consolidation in commercial cloud data centers 𝑫𝑺𝟑𝑪. Our solution is based on heuristic algorithms and allows reallocating software licenses at runtime by determining the optimal amount of resources required for their execution and freed unused machines. Simulation results showed the efficiency of our solution in terms of energy by 68.85% savings and costs by 80.01% savings. It allowed to free up to 75% physical machines and 76.5% virtual machines and proved its scalability in terms of average execution time while varying the number of software and the number of licenses alternately.

scholarly journals Growth Potential Aware Virtual Machine Consolidation Framework

2020 ◽  
Author(s):  
Swasthi Shetty ◽  
Annappa B
Keyword(s):  
Virtual Machine ◽  
Data Centers ◽  
Virtual Machines ◽  
Service Level Agreement ◽  
Detection Algorithm ◽  
Growth Potential ◽  
Vm Placement ◽  
Trade Off ◽  
Cloud Data ◽  
Virtual Machine Consolidation

<div> <div> <div> <p>Virtual machine consolidation techniques provide ways to save energy and cost in cloud data centers. However, aggressive packing of virtual machines can cause performance degradation. Therefore, it is essential to strike a trade-off between energy and performance in data centers. Achieving this trade-off has been an active research area in recent years. In this paper, a host underload detection algorithm and a new VM selection and VM placement techniques are proposed to consolidate Virtual machines based on the growth potential of VMs. Growth potential is calculated based on the utilization history of VMs. The interdependence of VM selection and VM placement techniques are also studied in the proposed model. The proposed algorithms are evaluated on real- world PlanetLab workload on Cloudsim. The experimental evaluation shows that our proposed technique reduces Service Level Agreement Violation (SLAV) and energy consumption compared to the existing algorithms. </p> </div> </div> </div>

scholarly journals A survey on application of machine learning to manage the virtual machines in cloud computing

2020 ◽  
Vol 11 (3) ◽  
pp. 197-208
Author(s):  
Varun Barthwal ◽  
M.M.S. Rauthan ◽  
Rohan Varma
Keyword(s):  
Machine Learning ◽  
Service Quality ◽  
Resource Utilization ◽  
Virtual Machines ◽  
Performance Parameters ◽  
Vm Placement ◽  
Vm Migration ◽  
Cloud Datacenter ◽  
Management Policies ◽  
Selection Of

AbstractVirtual machine (VM) management is a fundamental challenge in the cloud datacenter, as it requires not only scheduling and placement, but also optimization of the method to maintain the energy cost and service quality. This paper reviews the different areas of literature that deal with the resource utilization prediction, VM migration, VM placement and the selection of physical machines (PMs) for hosting the VMs. The main features of VM management policies were also examined using a comparative analysis of the current policies. Many research works include Machine Learning (ML) for detecting the PM overloading, the selection of VMs from over-utilized PM and VM placement as the main activities. This article aims to identify and classify research done in the area of scheduling and placement of VMs using the ML with resource utilization history. Energy efficiency, VM migration counts and Service quality were the key performance parameters that were used to assess the performance of the cloud datacenter.

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