A Virtual Machine Placement and Reconfiguration Framework for Cloud Computing Platforms

2014 ◽  
Vol 5 (2) ◽  
pp. 1-22
Author(s):  
Wei Chen ◽  
Xiaoqiang Qiao ◽  
Jun Wei ◽  
Hua Zhong ◽  
Tao Huang
Keyword(s):  
Cloud Computing ◽  
Resource Utilization ◽  
Optimization Method ◽  
Vm Placement ◽  
On Demand ◽  
Virtual Infrastructure ◽  
Runtime Reconfiguration ◽  
Computing Platforms ◽  
Multiple Resource ◽  
The Cost

As a rising application paradigm and technology, cloud computing can leverage the efficient pooling of on-demand, self-managed virtual infrastructure. How to maximize the resource utilization and how to reduce the cost of configuration are essential issues in cloud computing. In this paper, the authors propose a framework to achieve these objectives by optimizing VM placement and deciding when and how to perform the VM reconfigurations. The authors leverage the vector arithmetic to model the objective of balancing the multiple resource utilization and propose an optimization method for the static VM placement. Then the authors propose a two-level runtime reconfiguration policy, including the local adjustment and the parallel migration, to minimize the reconfiguration cost. Finally, the authors implement a prototype to validate and evaluate the proposed mechanism with a set of preliminary experiments, which shows that our work can maximize the resource utilization while effectively reducing the cost of the runtime reconfiguration.

Communication Cost Aware Resource Efficient Load Balancing (CARE-LB) Framework for Cloud Datacenter

2020 ◽  
Vol 13 ◽  
Author(s):  
Deepika Saxena ◽  
Ashutosh Kumar Singh
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Load Balancing ◽  
Power Consumption ◽  
Resource Utilization ◽  
Network Traffic ◽  
Communication Cost ◽  
Vm Placement ◽  
Multi Objective ◽  
On Demand

Background: Load balancing of communication-intensive applications, allowing efficient resource utilization and minimization of power consumption is a challenging multi-objective virtual machine (VM) placement problem. The communication among inter-dependent VMs, raises network traffic, hampers cloud client's experience and degrades overall performance, by saturating the network. Introduction: Cloud computing has become an indispensable part of Information Technology (IT), which supports the backbone of digitization throughout the world. It provides shared pool of IT resources, which are: always on, accessible from anywhere, at anytime and delivered on demand, as a service. The scalability and pay-per-use benefits of cloud computing has driven the entire world towards on-demand IT services that facilitates increased usage of virtualized resources. The rapid growth in the demands of cloud resources has amplified the network traffic in and out of the datacenter. Cisco Global Cloud Index predicts that by the year 2021, the network traffic among the devices within the datacenter will grow at Compound Annual Growth Rate (CAGR) of 23.4% Methods: To address these issues, a communication cost aware and resource efficient load balancing (CARE-LB) framework is presented, that minimizes communication cost, power consumption and maximize resource utilization. To reduce the communication cost, VMs with high affinity and inter-dependency are intentionally placed closer to each other. The VM placement is carried out by applying the proposed integration of Particle Swarm Optimization and non-dominated sorting based Genetic Algorithm i.e. PSOGA algorithm encoding VM allocation as particles as well as chromosomes. Results: The performance of proposed framework is evaluated by the execution of numerous experiments in the simulated datacenter environment and it is compared with the state-of-the-art methods like, Genetic Algorithm, First-Fit, Random-Fit and Best-Fit heuristic algorithms. The experimental outcome reveals that the CARE-LB framework improves 11% resource utilization, minimize 4.4% power consumption, 20.3% communication cost with reduction of execution time up to 49.7% over Genetic Algorithm based Load Balancing framework. Conclusion: The proposed CARE-LB framework provides promising solution for faster execution of data-intensive applications with improved resource utilization and reduced power consumption. Discussion: In the observed simulation, we analyze all the three objectives, after execution of the proposed multi-objective VM allocations and results are shown in Table 4. To choose the number of users for analysis of communication cost, the experiments are conducted with different number of users. For instance, for 100 VMs we choose 10, 20,...,80 users, and their request for VMs (number of VMs and type of VMs) are generated randomly, such that the total number of requested VMs do not exceed number of available VMs.

Resource Allocation Policies in Cloud Computing Environment

2017 ◽  
pp. 115-132 ◽  
Author(s):  
Suvendu Chandan Nayak ◽  
Sasmita Parida ◽  
Chitaranjan Tripathy ◽  
Prasant Kumar Pattnaik
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Resource Utilization ◽  
Service Level Agreement ◽  
Service Level ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
On Demand ◽  
Different Types ◽  
Cloud Datacenter

The basic concept of cloud computing is based on “Pay per Use”. The user can use the remote resources on demand for computing on payment basis. The on-demand resources of the user are provided according to a Service Level Agreement (SLA). In real time, the tasks are associated with a time constraint for which they are called deadline based tasks. The huge number of deadline based task coming to a cloud datacenter should be scheduled. The scheduling of this task with an efficient algorithm provides better resource utilization without violating SLA. In this chapter, we discussed the backfilling algorithm and its different types. Moreover, the backfilling algorithm was proposed for scheduling tasks in parallel. Whenever the application environment is changed the performance of the backfilling algorithm is changed. The chapter aims implementation of different types of backfilling algorithms. Finally, the reader can be able to get some idea about the different backfilling scheduling algorithms that are used for scheduling deadline based task in cloud computing environment at the end.

Efficient on Demand Dynamic Availability-Distribution-Span Scheduling and Load Balancing Scheme for Cloud Computing

2016 ◽  
Vol 13 (10) ◽  
pp. 7655-7660 ◽  
Author(s):  
V Jeyakrishnan ◽  
P Sengottuvelan
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Resource Utilization ◽  
Scheduling Algorithms ◽  
Time Factor ◽  
Cloud Environment ◽  
On Demand ◽  
Availability Factor ◽  
Scheduling Method

The problem of load balancing in cloud environment has been approached by different scheduling algorithms. Still the performance of cloud environment has not been met to the point and to overcome these issues, we propose a naval ADS (Availability-Distribution-Span) Scheduling method to perform load balancing as well as scheduling the resources of cloud environment. The method performs scheduling and load balancing in on demand nature and takes dynamic actions to fulfill the request of users. At the time of request, the method identifies set of resources required by the process and computes Availability Factor, Distributional Factor and Span Time factor for each of the resource available. Based on all these factors computed, the method schedules the requests to be processed in least span time. The proposed method produces efficient result on scheduling as well as load balancing to improve the performance of resource utilization in the cloud environment.

scholarly journals A review on various optimization techniques of resource provisioning in cloud computing

2019 ◽  
Vol 9 (1) ◽  
pp. 629
Author(s):  
K. Sumalatha ◽  
M. S. Anbarasi
Keyword(s):  
Cloud Computing ◽  
Resource Provisioning ◽  
Optimization Techniques ◽  
Application Performance ◽  
On Demand ◽  
Wide Range ◽  
Go Model ◽  
Uncertainty Parameters ◽  
The Cost ◽  
Research Avenue

<span lang="EN-US">Cloud computing is </span><span lang="EN-AU">the provision of IT resources (IaaS) on-demand using a pay as you go model over the internet</span><span lang="EN-US">.It is a</span><span lang="EN-AU"> broad and deep platform that helps customers builds sophisticated, scalable applications.</span><span lang="EN-US"> To get the full benefits, research on a wide range of topics is needed. While resource over-provisioning can cost users more than necessary, resource under provisioning hurts the application performance. The cost effectiveness of cloud computing highly depends on how well the customer can optimize the cost of renting resources (VMs) from cloud providers. The issue of resource provisioning optimization from cloud-consumer potential is a complicated optimization issue, which includes much uncertainty parameters. There is a much research avenue available for solving this problem as it is in the real-world. Here, in this paper we provide details about various optimization techniques for resource provisioning.</span>

scholarly journals Optimization of System Framework for Secure Communication and Data Virtualization on Cloud Computing

2020 ◽  
Vol 9 (11) ◽  
pp. 399-405
Keyword(s):  
Cloud Computing ◽  
Access Control ◽  
Data Storage ◽  
Resource Utilization ◽  
Secure Communication ◽  
Communication Model ◽  
Dynamic Allocation ◽  
Computing Model ◽  
Virtual Infrastructure ◽  
Data Virtualization

The consequent deployment of vital infrastructure to provide the secure communication and virtualization not only rectifies the challenges and difficulties but also benefits with saving the process of digitization. With a bang in evolution of cloud computing the uniqueness of every organization affects the virtualization of its information communication technology and applications. The answer for such organizations is to stipulate the precise cloud computing model equivalent to the deliberated and operational goals. This research article emphasized at budding and optimizing a framework for virtual infrastructure and cloud computing model to support the organization stakeholders. Moreover it also improves the cloud security with a protected virtual infrastructure and communication model with better performance. On account of data storage security necessities and distinctiveness of cloud computing environment, a method for secured data & storage based on dynamic allocation and access control mechanism is presented. Dynamic resource allocation is applied for resource utilization that focused on data virtualization and memory virtualization, for protection and access control a modified KPABE algorithm is integrated. The combination of these methods resulting on the optimize resource use, centralizing of storage. The implementation and comparison of results revealed that the proposed modified KP-ABE has performed effectively than the other security standards and especially for resource utilization. The proposed method serves for efficient data storage, access control solutions and computation in cloud environment

scholarly journals A Game Theory Approach to Fair and Efficient Resource Allocation in Cloud Computing

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
Xin Xu ◽  
Huiqun Yu
Keyword(s):  
Cloud Computing ◽  
Resource Utilization ◽  
Resource Sharing ◽  
Virtual Machines ◽  
Utilization Rate ◽  
Resources Allocation ◽  
Theory Approach ◽  
On Demand ◽  
Efficient Resource ◽  
Game Theoretic

On-demand resource management is a key characteristic of cloud computing. Cloud providers should support the computational resource sharing in a fair way to ensure that no user gets much better resources than others. Another goal is to improve the resource utilization by minimizing the resource fragmentation when mapping virtual machines to physical servers. The focus of this paper is the proposal of a game theoretic resources allocation algorithm that considers the fairness among users and the resources utilization for both. The experiments with an FUGA implementation on an 8-node server cluster show the optimality of this algorithm in keeping fairness by comparing with the evaluation of the Hadoop scheduler. The simulations based on Google workload trace demonstrate that the algorithm is able to reduce resource wastage and achieve a better resource utilization rate than other allocation mechanisms.

scholarly journals A Study on Data Security In Fog Computing

2018 ◽  
Vol 7 (2.19) ◽  
pp. 50
Author(s):  
P S.Apirajitha
Keyword(s):  
Cloud Computing ◽  
Internet Of Things ◽  
Data Security ◽  
Fog Computing ◽  
Daily Basis ◽  
Long Distance ◽  
Computing Power ◽  
On Demand ◽  
Iot Devices ◽  
The Cost

During the years, Cloud Computing is a popular paradigm which provide access to configurable resources on devices at any time,with on demand. Cloud Computing provides many benefits to enterprises by reducing the cost and allowing them to concentrate on their core business. Apart from this , the Development of Internet of Things came into existence, where the cloud divulge a long distance between users and its environment. Cloud Computing is also referred as heavy computing and dense form of computing power. In Spite of this  a new computing has been proposed called Fog Computing also known as Fogging, which overcomes the problem of cloud. Fog computing which majority supports the concepts of Internet of Things(IoT), where many  IoT devices are used by users on daily basis which are connected to each other. Fog Computing is also an extended version of cloud computing.  

scholarly journals Power Management as a Service (Pmaas) – An Energy Saving Service by ―Auto-Fit VM Placement‖ Algorithm in Cloud Computing for Maximum Resource Utilization at Minimum Energy Consumption Without Live-Migrating the Virtual Machines by using the Concept of Virtual-Servers

2019 ◽  
Vol 9 (2) ◽  
pp. 1437-1446
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Power Consumption ◽  
Power Management ◽  
Resource Utilization ◽  
Virtual Machine ◽  
Virtual Machines ◽  
Memory Storage ◽  
Vm Placement ◽  
Placement Algorithm

Now a day Energy Consumption is one of the most promising fields amongst several computing services of cloud computing. A maximum amount of Power resources are absorbed by the data centre because of huge amount of data processing which is increased abnormally. So it’s the time to think about the energy consumption in cloud environment. Existing Energy Consumption systems are limited in terms of virtualization because improper virtualization leads to loads imbalance and excessive power consumption and inefficiency in terms of computational power. Billing[1,2 ] is another exciting feature that is closely related to energy consumption, because higher or lesser billing depends on energy consumption somehow-as we know that cloud providers allow cloud users to access resources as pay-per-use, so these resources need to be optimally selected to process the user request to maximize user satisfaction in the distributed virtualized environment. There may be an inequity between the actual power consumption by the users and the provided billing records by the providers, So any false accusation that may claimed by each other to get illegal compensations. To avoid such accusation, we propose a work to consolidate the VMs using the Power Management as a Service (PMaaS) model in such a way, to reduce power consumption by maximum resource utilization without live-migration of the virtual machines by using the concept of Virtual Servers. The proposed PMaaS model uses a new “Auto-fit VM placement algorithm”, which computes tasks resource demands, models a Virtual Machine that fits those demands, and places the Virtual Machines on a Virtual server made by the collective resources (CPU, Memory, Storage and Bandwidth) from the respective schedulers directly connected to the actual physical servers and that has the minimum remaining resources which is large enough to accommodate such a Virtual Machine.

scholarly journals Secure Dynamic Resource Provisioning Cost by Optimized Placement of Virtual Machines in Cloud Computing

2013 ◽  
Vol 4 (1) ◽  
pp. 88-93
Author(s):  
Aarthee S ◽  
Venkatesan R
Keyword(s):  
Cloud Computing ◽  
Service Management ◽  
Data Centers ◽  
Virtual Machines ◽  
Service Providers ◽  
Cloud Service ◽  
Resource Provisioning ◽  
On Demand ◽  
Dynamic Resource Provisioning ◽  
The Cost

Cloud computing provides pay-as-you-go computing resources and accessing services are offered from data centers all over the world as the cloud. Consumers may find that cloud computing allows them to reduce the cost of information management as they are not required to own their servers and can use capacity leased from third parties or cloud service providers. Cloud consumers can successfully reduce total cost of resource provisioning using Optimal Cloud Resource Provisioning (OCRP) algorithm in cloud computing environment. The two provisioning plans are reservation and on-demand, used for computing resources which is offered by cloud providers to cloud consumers. The cost of utilizing computing resources provisioned by reservation plan is cheaper than that provisioned by on-demand plan, since a cloud consumer has to pay to provider in advance. This project proposes that the OCRP algorithm associated with rule based resource manager technique is used to increase the scalability of cloud on-demand services by dynamic placement of virtual machines to reduce the cost and also endow with secure accessing of resources from data centers and parameters like virtualized platforms, data or service management are monitored in the cloud environment.

scholarly journals General Collective Intelligence and the Cloud

2021 ◽  
Author(s):  
Andy E Williams
Keyword(s):  
Cloud Computing ◽  
Collective Intelligence ◽  
Cloud Service ◽  
Cloud Services ◽  
Cognitive Computing ◽  
Computing Platform ◽  
On Demand ◽  
Exponential Increase ◽  
Functional Models ◽  
Computing Platforms

The resources that can be made available on-demand through cloud computing are continually increasing. One potential addition is General Collective Intelligence or GCI, which has been defined as a platform that combines individuals into a single intelligence with the potential for exponentially greater general problem-solving ability (intelligence) than any individual. The concept of a cognitive computing platform involves leveraging GCI to orchestrate cooperation between any entities that are required in order to create the capacity to maximize any collective outcome targeted. Rather than executing programming code, a cognitive computing platform must execute functional models in which each of the functional operations composing that model is implemented in some programming language. All services that run on the cloud, as well as the cloud itself, can potentially be offered as cognitive computing platforms. Where current cloud computing limits customers to a particular cloud service vendor, cloud computing as a cognitive computing platform has the potential to completely decouple users from any such dependencies, while at the same time creating the potential for an exponential increase in demand for cloud services from those vendors that participate by decoupling their services in this way.

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