scholarly journals Virtual Machine Consolidation with Minimization of Migration Thrashing for Cloud Data Centers

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Xialin Liu ◽  
Junsheng Wu ◽  
Gang Sha ◽  
Shuqin Liu
Keyword(s):  
Virtual Machine ◽  
Carbon Dioxide Emissions ◽  
Data Centers ◽  
Virtual Machines ◽  
Service Level Agreement ◽  
High Capacity ◽  
Electrical Energy ◽  
Cloud Data ◽  
Cloud Data Centers ◽  
Vm Consolidation

Cloud data centers consume huge amount of electrical energy bringing about in high operating costs and carbon dioxide emissions. Virtual machine (VM) consolidation utilizes live migration of virtual machines (VMs) to transfer a VM among physical servers in order to improve the utilization of resources and energy efficiency in cloud data centers. Most of the current VM consolidation approaches tend to aggressive-migrate for some types of applications such as large capacity application such as speech recognition, image processing, and decision support systems. These approaches generate a high migration thrashing because VMs are consolidated to servers according to VM’s instant resource usage without considering their overall and long-term utilization. The proposed approach, dynamic consolidation with minimization of migration thrashing (DCMMT) which prioritizes VM with high capacity, significantly reduces migration thrashing and the number of migrations to ensure service-level agreement (SLA) since it keeps VMs likely to suffer from migration thrashing in the same physical servers instead of migrating. We have performed experiments using real workload traces compared to existing aggressive-migration-based solutions; through simulations, we show that our approach improves migration thrashing metric by about 28%, number of migrations metric by about 21%, and SLAV metric by about 19%.

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 Server Consolidation Based on Culture Multiple-Ant-Colony Algorithm in Cloud Computing

Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2724 ◽  
Author(s):  
Yuan ◽  
Sun
Keyword(s):  
Energy Consumption ◽  
Virtual Machine ◽  
Ant Colony Algorithm ◽  
Data Centers ◽  
Virtual Machines ◽  
Ant Colony ◽  
Virtual Machine Migration ◽  
Server Consolidation ◽  
Cloud Data ◽  
Cloud Data Centers

High-energy consumption in data centers has become a critical issue. The dynamic server consolidation has significant effects on saving energy of a data center. An effective way to consolidate virtual machines is to migrate virtual machines in real time so that some light load physical machines can be turned off or switched to low-power mode. The present challenge is to reduce the energy consumption of cloud data centers. In this paper, for the first time, a server consolidation algorithm based on the culture multiple-ant-colony algorithm was proposed for dynamic execution of virtual machine migration, thus reducing the energy consumption of cloud data centers. The server consolidation algorithm based on the culture multiple-ant-colony algorithm (CMACA) finds an approximate optimal solution through a specific target function. The simulation results show that the proposed algorithm not only reduces the energy consumption but also reduces the number of virtual machine migration.

scholarly journals Virtual Machine Consolidation using Roulette wheel selection Strategy

2022 ◽  
Vol 06 (01) ◽  
Author(s):  
Raghi K.R K R
Keyword(s):  
Cloud Computing ◽  
Virtual Machine ◽  
Carbon Dioxide Emissions ◽  
Data Centers ◽  
High Energy ◽  
Selection Strategy ◽  
Cloud Data ◽  
Roulette Wheel Selection ◽  
Roulette Wheel ◽  
Vm Consolidation

Cloud computing data centers are growing rapidly in both number and capacity to meet the increasing demands for highly-responsive computing and massive storage. Such data centers consume enormous amounts of electrical energy resulting in high operating costs and carbon dioxide emissions. The reason for this extremely high energy consumption is not just the quantity of computing resources and the power inefficiency of hardware, but rather lies in the inefficient usage of these resources. Virtual Machine [VM] consolidation involves live migration of VMs hence the capability of transferring a VM between physical servers with a close to zero down time. It is an effective way to improve the utilization of resources and increase energy efficiency in cloud data centers. VM consolidation consists of host overload/under load detection, VM selection and VM placement. In Our Proposed Model We are going to use Roulette-Wheel Selection Strategy, Where the VM selects the Instance type and Physical Machine [PM] using Roulette-Wheel Selection Mechanism Keywords—searchable encryption, dynamic update, cloud computing

Optimizing the Migration of Virtual Machines in Cloud Data Centers

2022 ◽  
Vol 13 (1) ◽  
pp. 0-0
Keyword(s):  
Data Centers ◽  
Bin Packing ◽  
Virtual Machines ◽  
Service Level Agreement ◽  
Service Level ◽  
Virtual Machine Migration ◽  
Cloud Data ◽  
Time Simulation ◽  
Cloud Data Centers ◽  
Dynamic Resource

Dynamic resource allocation of cloud data centers is implemented with the use of virtual machine migration. Selected virtual machines (VM) should be migrated on appropriate destination servers. This is a critical step and should be performed according to several criteria. It is proposed to use the criteria of minimum resource wastage and service level agreement violation. The optimization problem of the VM placement according to two criteria is formulated, which is equivalent to the well-known main assignment problem in terms of the structure, necessary conditions, and the nature of variables. It is suggested to use the Hungarian method or to reduce the problem to a closed transport problem. This allows the exact solution to be obtained in real time. Simulation has shown that the proposed approach outperforms widely used bin-packing heuristics in both criteria.

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 Decomposed multi-objective bin-packing for virtual machine consolidation

2016 ◽  
Vol 2 ◽  
pp. e47 ◽  
Author(s):  
Eli M. Dow
Keyword(s):  
Virtual Machine ◽  
High Performance ◽  
Bin Packing ◽  
Virtual Machines ◽  
Management Problem ◽  
Cloud Data ◽  
Virtual Machine Consolidation ◽  
Solution Generation ◽  
Cloud Data Centers ◽  
Vm Consolidation

In this paper, we describe a novel solution to the problem of virtual machine (VM) consolidation, otherwise known as VM-Packing, as applicable to Infrastructure-as-a-Service cloud data centers. Our solution relies on the observation that virtual machines are not infinitely variable in resource consumption. Generally, cloud compute providers offer them in fixed resource allocations. Effectively this makes all VMs of that allocation type (or instance type) generally interchangeable for the purposes of consolidation from a cloud compute provider viewpoint. The main contribution of this work is to demonstrate the advantages to our approach of deconstructing the VM consolidation problem into a two-step process of multidimensional bin packing. The first step is to determine the optimal, but abstract, solution composed of finite groups of equivalent VMs that should reside on each host. The second step selects concrete VMs from the managed compute pool to satisfy the optimal abstract solution while enforcing anti-colocation and preferential colocation of the virtual machines through VM contracts. We demonstrate our high-performance, deterministic packing solution generation, with over 7,500 VMs packed in under 2 min. We demonstrating comparable runtimes to other VM management solutions published in the literature allowing for favorable extrapolations of the prior work in the field in order to deal with larger VM management problem sizes our solution scales to.

scholarly journals A Novel Physical Machine Overload Detection Algorithm Combined with Quiescing for Dynamic Virtual Machine Consolidation in Cloud Data Centers

2019 ◽  
Vol 17 (3) ◽  
pp. 358-366
Author(s):  
Loiy Alsbatin ◽  
Gürcü Öz ◽  
Ali Ulusoy
Keyword(s):  
Virtual Machine ◽  
Data Centers ◽  
Virtual Machines ◽  
Energy Performance ◽  
Detection Algorithm ◽  
Vm Migration ◽  
Cloud Data ◽  
Detection Algorithms ◽  
Physical Machine ◽  
Cloud Data Centers

Further growth of computing performance has been started to be limited due to increasing energy consumption of cloud data centers. Therefore, it is important to pay attention to the resource management. Dynamic virtual machines consolidation is a successful approach to improve the utilization of resources and energy efficiency in cloud environments. Consequently, optimizing the online energy-performance trade off directly influences Quality of Service (QoS). In this paper, a novel approach known as Percentage of Overload Time Fraction Threshold (POTFT) is proposed that decides to migrate a Virtual Machine (VM) if the current Overload Time Fraction (OTF) value of Physical Machine (PM) exceeds the defined percentage of maximum allowed OTF value to avoid exceeding the maximum allowed resulting OTF value after a decision of VM migration or during VM migration. The proposed POTFT algorithm is also combined with VM quiescing to maximize the time until migration, while meeting QoS goal. A number of benchmark PM overload detection algorithms is implemented using different parameters to compare with POTFT with and without VM quiescing. We evaluate the algorithms through simulations with real world workload traces and results show that the proposed approaches outperform the benchmark PM overload detection algorithms. The results also show that proposed approaches lead to better time until migration by keeping average resulting OTF values less than allowed values. Moreover, POTFT algorithm with VM quiescing is able to minimize number of migrations according to QoS requirements and meet OTF constraint with a few quiescings.

scholarly journals MULTI-OBJECTIVE OPTIMIZATION OF VIRTUAL MACHINE PLACEMENT ON PHYSICAL SERVERS IN CLOUD DATA CENTERS

T-Comm ◽  
2021 ◽  
Vol 15 (1) ◽  
pp. 28-34
Author(s):  
Andrew V. Toutov ◽  
◽  
Natalia V. Toutova ◽  
Anatoly S. Vorozhtsov ◽  
Iliya A. Andreev ◽  
...  
Keyword(s):  
Virtual Machine ◽  
Assignment Problem ◽  
Data Centers ◽  
Virtual Machines ◽  
Heuristic Algorithms ◽  
Mathematical Formulation ◽  
Cloud Services ◽  
Virtual Machine Placement ◽  
Cloud Data ◽  
Cloud Data Centers

The problem of virtual machine placement on physical servers in cloud data centers is considered. The resource management system has a two-level architecture consisting of global and local controllers. Local controllers analyze the state of the physical servers on which they are located and determine possible underloading, overloading, and overheating states based on the forecast for the next observation window. If one of the listed states is detected, the local controller notifies the global controller, which selects the destination servers to host the virtual machines via migration. It is proposed to place virtual machines based on the criteria of minimum remaining unused resources and violation of SLA agreements. A mathematical formulation of the optimization problem is given, which is equivalent to the known main assignment problem in terms of structure, necessary conditions, and the nature of variables. Reducing the assignment problem to a closed transport problem allowed us to solve the problem of hosting virtual machines under many criteria in real time and significantly increase its dimension in comparison with heuristic algorithms, which makes it possible to maintain the quality of modern cloud services in the conditions of rapid growth of physical and virtual resources of data centers. The developed mathematical formulation of the problem and the results of computational experiments can be included in the mathematical software of virtual machine live migration.

scholarly journals AN ENHANCED TASK ALLOCATION STRATEGY IN CLOUD ENVIRONMENT

2017 ◽  
Vol 16 (6) ◽  
pp. 6953-6961
Author(s):  
Kavita Redishettywar ◽  
Prof. Rafik Juber Thekiya
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Virtual Machine ◽  
Data Centers ◽  
Job Scheduling ◽  
Virtual Machines ◽  
Cloud Environment ◽  
Cloud Data ◽  
Cloud Data Centers ◽  
Load Balancing Algorithm

Cloud computing is a vigorous technology by which a user can get software, application, operating system and hardware as a service without actually possessing it and paying only according to the usage. Cloud Computing is a hot topic of research for the researchers these days. With the rapid growth of Interne technology cloud computing have become main source of computing for small as well big IT companies. In the cloud computing milieu the cloud data centers and the users of the cloud-computing are globally situated, therefore it is a big challenge for cloud data centers to efficiently handle the requests which are coming from millions of users and service them in an efficient manner. Load balancing ensures that no single node will be overloaded and used to distribute workload among multiple nodes. It helps to improve system performance and proper utilization of resources. We propose an improved load balancing algorithm for job scheduling in the cloud environment using K-Means clustering of cloudlets and virtual machines in the cloud environment. All the cloudlets given by the user are divided into 3 clusters depending upon client’s priority, cost and instruction length of the cloudlet. The virtual machines inside the datacenter hosts are also grouped into multiple clusters depending upon virtual machine capacity in terms of processor, memory, and bandwidth. Sorting is applied at both the ends to reduce the latency. Multiple number of experiments have been conducted by taking different configurations of cloudlets and virtual machine. Various parameters like waiting time, execution time, turnaround time and the usage cost have been computed inside the cloudsim environment to demonstrate the results. Compared with the other job scheduling algorithms, the improved load balancing algorithm can outperform them according to the experimental results.

scholarly journals Energy efficiency in virtual machines allocation for cloud data centers with lottery algorithm

2019 ◽  
Vol 9 (1) ◽  
pp. 546
Author(s):  
Mehran Tarahomi ◽  
Mohammad Izadi
Keyword(s):  
Virtual Machine ◽  
Data Centers ◽  
Virtual Machines ◽  
Complex Issue ◽  
New Approach ◽  
Cloud Data ◽  
Time Period ◽  
Cloud Data Centers ◽  
Machine Allocation ◽  
Virtual Machine Allocation

<p>Energy usage of data centers is a challenging and complex issue because computing applications and data are growing so quickly that increasingly larger servers and disks are needed to process them fast enough within the required time period. In the past few years, many approaches to virtual machine placement have been proposed. This study proposes a new approach for virtual machine allocation to physical hosts. Either minimizes the physical hosts and avoids the SLA violation. The proposed method in comparison to the other algorithms achieves better results.</p>

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