Auto-Scale Resource Provisioning In IaaS Clouds

2020 ◽  
Author(s):  
Zolfaghar Salmanian ◽  
Habib Izadkhah ◽  
Ayaz Isazadeh
Keyword(s):  
Cloud Computing ◽  
Virtual Machines ◽  
Queueing Systems ◽  
Threshold Value ◽  
Resource Provisioning ◽  
Death Process ◽  
New Host ◽  
Network Bandwidth ◽  
Computing Paradigm ◽  
Long Run

Abstract Users of cloud computing technology can lease resources instead of spending an excessive charge for their ownership. For service delivery in the infrastructure-as-a-service model of the cloud computing paradigm, virtual machines (VMs) are created by the hypervisor. This software is installed on a bare-metal server, called the host, and acted as a broker between the hardware of the host and its VMs. The host is responsible for the allocation of required resources, such as CPU, RAM and network bandwidth, for VMs. Therefore, allocating resources to a VM is equivalent to finding the location of the VM on the hosts. In this paper, we propose a model for resource allocation of a datacenter that includes clusters of hosts. This model is based on the birth–death process of queueing systems and continuous-time Markov chains. We will focus on RAM-intensive VMs and consider the allocation of RAM for a VM as a job in the queueing systems. The purpose of this modeling is to keep the number of running hosts minimum while guaranteeing the quality of service in terms of response. When the utilization of active hosts reaches a predefined threshold value, a new host is added to prevent response time violation, and when host utilization is reduced to a certain threshold, one of the hosts can be deactivated. The experimental results show that, in the long run, the odds of working with more jobs are increased.

Cloud-Based Platforms and Infrastructures

2017 ◽  
pp. 47-90
Author(s):  
Marcus Tanque
Keyword(s):  
Cloud Computing ◽  
Virtual Machines ◽  
Cloud Service ◽  
Resource Provisioning ◽  
Virtual Networks ◽  
Network Resources ◽  
Computing Services ◽  
Service Models ◽  
Cloud Computing Services ◽  
Shared Network

Cloud computing consists of three fundamental service models: infrastructure-as-a-service, platform-as-a service and software-as-a-service. The technology “cloud computing” comprises four deployment models: public cloud, private cloud, hybrid cloud and community cloud. This chapter describes the six cloud service and deployment models, the association each of these services and models have with physical/virtual networks. Cloud service models are designed to power storage platforms, infrastructure solutions, provisioning and virtualization. Cloud computing services are developed to support shared network resources, provisioned between physical and virtual networks. These solutions are offered to organizations and consumers as utilities, to support dynamic, static, network and database provisioning processes. Vendors offer these resources to support day-to-day resource provisioning amid physical and virtual machines.

A Value Based Dynamic Resource Provisioning Model in Cloud

2013 ◽  
Vol 3 (2) ◽  
pp. 35-46 ◽  
Author(s):  
Sandeep K. Sood
Keyword(s):  
Resource Utilization ◽  
Virtual Machines ◽  
Fault Tolerant ◽  
Resource Provisioning ◽  
Resource Usage ◽  
Resource Manager ◽  
Usage Rate ◽  
Computing Paradigm ◽  
A Value ◽  
Dynamic Resource Provisioning

Cloud computing has become an innovative computing paradigm, which aims at providing reliable, customized, Quality of Service (QoS) and guaranteed computing infrastructures for users. Efficient resource provisioning is required in cloud for effective resource utilization. For resource provisioning, cloud provides virtualized computing resources that are dynamically scalable. This property of cloud differentiates it from the traditional computing paradigm. But the initialization of a new virtual instance causes a several minutes delay in the hardware resource allocation. Furthermore, cloud provides a fault tolerant service to its clients using the virtualization. But, in order to attain higher resource utilization over this technology, a technique or a strategy is needed using which virtual machines can be deployed over physical machines by predicting its need in advance so that the delay can be avoided. To address these issues, a value based prediction model in this paper is proposed for resource provisioning in which a resource manager is used for dynamically allocating or releasing a virtual machine depending upon the resource usage rate. In order to know the recent resource usage rate, the resource manager uses sliding window to analyze the resource usage rate and to predict the system behavior in advance. By predicting the resource requirements in advance, a lot of processing time can be saved. Earlier, a server has to perform all the calculations regarding the resource usage that in turn wastes a lot of processing power thus decreasing its overall capacity to handle the incoming request. The main feature of the proposed model is that a lot of load is being shifted from the individual server to the resource manager as it performs all the calculations and therefore the server is free to handle the incoming requests to its full capacity.

Optimal Management of Cloud Centers with Different Arrival Modes for Cloud Computing Environment

2012 ◽  
Vol 2 (3) ◽  
pp. 86-97
Author(s):  
Veena Goswami ◽  
Sudhansu Shekhar Patra ◽  
G. B. Mund
Keyword(s):  
Cloud Computing ◽  
Virtual Machines ◽  
Service Level Agreement ◽  
Service Level ◽  
Finite Buffer ◽  
Web Browser ◽  
Computing Paradigm ◽  
Computing Services ◽  
Multi Server

Cloud computing is a new computing paradigm in which information and computing services can be accessed from a Web browser by clients. Understanding of the characteristics of computer service performance has become critical for service applications in cloud computing. For the commercial success of this new computing paradigm, the ability to deliver guaranteed Quality of Services (QoS) is crucial. Based on the Service level agreement, the requests are processed in the cloud centers in different modes. This paper analyzes a finite-buffer multi-server queuing system where client requests have two arrival modes. It is assumed that each arrival mode is serviced by one or more Virtual machines, and both the modes have equal probabilities of receiving service. Various performance measures are obtained and optimal cost policy is presented with numerical results. The genetic algorithm is employed to search the optimal values of various parameters for the system.

A Secured Real Time Scheduling Model for Cloud Hypervisor

2016 ◽  
Vol 6 (4) ◽  
pp. 97-110
Author(s):  
Rekha Kashyap ◽  
Deo Prakash Vidyarthi
Keyword(s):  
Cloud Computing ◽  
Success Rate ◽  
Real Time ◽  
Virtual Machines ◽  
Resource Provisioning ◽  
Real Time Scheduling ◽  
Scheduling Model ◽  
Physical Resources ◽  
Time Scheduling ◽  
Deadline Constraints

Virtualization is critical to cloud computing and is possible through hypervisors, which maps the Virtual machines((VMs) to physical resources but poses security concerns as users relinquish physical possession of their computation and data. Good amount of research is initiated for resource provisioning on hypervisors, still many issues need to be addressed for security demanding and real time VMs. First work SRT-CreditScheduler (Secured and Real-time), maximizes the success rate by dynamically prioritizing the urgency and the workload of VMs but ensures highest security for all. Another work, SA-RT-CreditScheduler (Security-aware and Real-time) is a dual objective scheduler, which maximizes the success rate of VMs in best possible security range as specified by the VM owner. Though the algorithms can be used by any hypervisor, for the current work they have been implemented on Xen hypervisor. Their effectiveness is validated by comparing it with Xen's, Credit and SEDF scheduler, for security demanding tasks with stringent deadline constraints.

Resource Provisioning Analysis in Virtual Machines Using Statistical Performance of Workloads for Business Applications

2020 ◽  
Vol 17 (9) ◽  
pp. 4156-4161
Author(s):  
Jeny Varghese ◽  
S. Jagannatha
Keyword(s):  
Cloud Computing ◽  
Large Scale ◽  
Virtual Machines ◽  
Resource Provisioning ◽  
Business Applications ◽  
Cloud Federation ◽  
Cloud Datacenter

Cloud Federation is the interconnection of two or more cloud computing settings in order to share configurable processing components such as networks, servers, apps that can be dynamically delivered to customers. Virtualization has been an integral part of cloud computing which provides manageability and utilization of resources. This paper analyses on how jobs of business applications demand and efficiently use the capacity of the resources that are provisioned by the VMs, thereby managing the performance of the applications. The in-depth assessment is based on two large-scale and constant performance traces gathered in a cloud datacenter that host company tools for running distinct apps with regard to requested and used resources.

The Study on Cloud Computing Resource Allocation Method

2012 ◽  
Vol 198-199 ◽  
pp. 1506-1513 ◽  
Author(s):  
Ling Yan Wang ◽  
Ai Min Liu
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Virtual Machines ◽  
Optimization Methods ◽  
Resource Provisioning ◽  
Scheduling Problems ◽  
Multi Agent Systems ◽  
Optimization Simulation ◽  
Multi Agent ◽  
Fair Scheduler

Resource allocation and scheduling problems in the field of cloud computing can be classified into two major groups. The first one is in the area of MapReduce task scheduling. The default scheduler is the FIFO one. Two other schedulers that are available as plug-in for Hadoop: Fair scheduler and Capacity scheduler. We presented recent research in this area to enhance performance or to better suit a specific application. MapReduce scheduling research involves introducing alternative schedulers, or proposing enhancements for existing schedulers such as streaming and input format specification. The second problem is the provisioning of virtual machines and processes to the physical machines and its different resources. We presented the major cloud hypervisors available today. We described the different methods used to solve the resource allocation problem including optimization, simulation, distributed multi-agent systems and SoA. Finally, we presented the related topic of connecting clouds which uses similar resource provisioning methods. The above two scheduling problems are often mixed up, yet they are related. For example, MapReduce benchmarks can be used to evaluate VM provisioning methods. Enhancing the solution to one problem can affect the other. Similar methods can be used in solving both problems, such as optimization methods. Cloud computing is a platform that hosts applications and services for businesses and users to accesses computing as a service. In this paper, we identify two scheduling and resource allocation problems in cloud computing.

scholarly journals Finding the Optimal Value for Threshold Cryptography on Cloud Computing

2016 ◽  
Vol 6 (6) ◽  
pp. 2979
Author(s):  
Weena Janratchakool ◽  
Sirapat Boonkrong ◽  
Sucha Smanchat
Keyword(s):  
Cloud Computing ◽  
Virtual Machine ◽  
Virtual Machines ◽  
Threshold Value ◽  
Optimal Threshold ◽  
Threshold Cryptography ◽  
Cloud Environment ◽  
Reconstruction Process ◽  
Private Key ◽  
Optimal Value

<p>The objective of using threshold cryptography on cloud environment is to protect the keys, which are the most important elements in cryptographic systems. Threshold cryptography works by dividing the private key to a number of shares, according to the number of virtual machines, then distributing them each share to each virtual machine. In order to generate the key back, not all the shares are needed. Howerver, the problem is that there has been no research attemping to find a suitable threshold value for key reconstruction. Therefore, this paper presented a guildline designed and implemented that can assist to choose such value. The experiment was setup using CloudSim to simulate cloud environment and collecting time taken in key distribution and key reconstruction process to achieve the optimal threshold value.</p>

scholarly journals Automated Clustering of Virtual Machines based on Correlation of Resource Usage

2012 ◽  
Vol 8 (4) ◽  
pp. 102 ◽  
Author(s):  
Claudia Canali ◽  
Riccardo Lancellotti
Keyword(s):  
Cloud Computing ◽  
High Performance ◽  
Large Scale ◽  
Virtual Machines ◽  
Resource Usage ◽  
Cloud Data ◽  
Multiple Resources ◽  
Computing Paradigm ◽  
Innovative Methodology ◽  
Cloud Data Centers

The recent growth in demand for modern applicationscombined with the shift to the Cloud computing paradigm have led to the establishment of large-scale cloud data centers. The increasing size of these infrastructures represents a major challenge in terms of monitoring and management of the system resources. Available solutions typically consider every Virtual Machine (VM) as a black box each with independent characteristics, and face scalability issues by reducing the number of monitored resource samples, considering in most cases only average CPU usage sampled at a coarse time granularity. We claim that scalability issues can be addressed by leveraging thesimilarity between VMs in terms of resource usage patterns.In this paper we propose an automated methodology to cluster VMs depending on the usage of multiple resources, both systemand network-related, assuming no knowledge of the services executed on them. This is an innovative methodology that exploits the correlation between the resource usage to cluster together similar VMs. We evaluate the methodology through a case study with data coming from an enterprise datacenter, and we show that high performance may be achieved in automatic VMs clustering. Furthermore, we estimate the reduction in the amount of data collected, thus showing that our proposal may simplify the monitoring requirements and help administrators totake decisions on the resource management of cloud computing datacenters.

scholarly journals A Comparative Analysis of NSGA-II and NSGA-III for Autoscaling Parameter Sweep Experiments in the Cloud

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Virginia Yannibelli ◽  
Elina Pacini ◽  
David Monge ◽  
Cristian Mateos ◽  
Guillermo Rodriguez
Keyword(s):  
Cloud Computing ◽  
Optimization Algorithm ◽  
Virtual Machines ◽  
State Of The Art ◽  
Multiobjective Evolutionary Algorithms ◽  
Nsga Ii ◽  
Trade Off ◽  
Computing Paradigm ◽  
Virtual Infrastructure ◽  
And Storage

The Cloud Computing paradigm is focused on the provisioning of reliable and scalable virtual infrastructures that deliver execution and storage services. This paradigm is particularly suitable to solve resource-greedy scientific computing applications such as parameter sweep experiments (PSEs). Through the implementation of autoscalers, the virtual infrastructure can be scaled up and down by acquiring or terminating instances of virtual machines (VMs) at the time that application tasks are being scheduled. In this paper, we extend an existing study centered in a state-of-the-art autoscaler called multiobjective evolutionary autoscaler (MOEA). MOEA uses a multiobjective optimization algorithm to determine the set of possible virtual infrastructure settings. In this context, the performance of MOEA is greatly influenced by the underlying optimization algorithm used and its tuning. Therefore, we analyze two well-known multiobjective evolutionary algorithms (NSGA-II and NSGA-III) and how they impact on the performance of the MOEA autoscaler. Simulated experiments with three real-world PSEs show that MOEA gets significantly improved when using NSGA-III instead of NSGA-II due to the former provides a better exploitation versus exploration trade-off.

SAMEVED

2013 ◽  
Vol 5 (2) ◽  
pp. 27-42 ◽  
Author(s):  
Shao-Jui Chen ◽  
Jing-Ying Huang ◽  
Cheng-Ta Huang ◽  
Wei-Jen Wang
Keyword(s):  
Cloud Computing ◽  
Virtual Machines ◽  
Cloud Service ◽  
Web Interface ◽  
Computing Paradigm ◽  
Elastic Computing ◽  
Virtual Routers ◽  
User Friendly ◽  
Cloud Applications ◽  
Remote Invocation

Cloud computing is an emerging computing paradigm that provides all kinds of services through the Internet. Existing elastic computing approaches are popular in cloud computing. They can fulfill the requirements of some cloud applications, but usually fail to provide an isolated computing environment consisting of connected virtual machines over a user-defined network topology. This paper presents a system architecture, namely SAMEVED, which exposes a cloud service that can allocate and manage a private, virtual elastic datacenter by integrating VPN and virtual routers into existing virtualization technologies. Authentication is required by user login while using SAMEVED. A user-friendly web interface and remote invocation interface are provided to support different operations for different users with different privileges.

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