scholarly journals Backup Resource Allocation of Virtual Machines for Probabilistic Protection under Capacity Uncertainty

2022 ◽  
Author(s):  
Mitsuki ITO ◽  
Fujun HE ◽  
Eiji OKI
Keyword(s):  
Resource Allocation ◽  
Virtual Machines ◽  
Capacity Uncertainty

Distance Aware VM allocation process to minimize energy consumption in cloud computing

2019 ◽  
Vol 12 ◽  
Author(s):  
Gurpreet Singh ◽  
Manish Mahajan ◽  
Rajni Mohana
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Energy Consumption ◽  
Virtual Machine ◽  
Virtual Machines ◽  
Research Work ◽  
Service Level Agreement ◽  
Service Level ◽  
Physical Machine ◽  
Allocation Process

BACKGROUND: Cloud computing is considered as an on-demand service resource with the applications towards data center on pay per user basis. For allocating the resources appropriately for the satisfaction of user needs, an effective and reliable resource allocation method is required. Because of the enhanced user demand, the allocation of resources has now considered as a complex and challenging task when a physical machine is overloaded, Virtual Machines share its load by utilizing the physical machine resources. Previous studies lack in energy consumption and time management while keeping the Virtual Machine at the different server in turned on state. AIM AND OBJECTIVE: The main aim of this research work is to propose an effective resource allocation scheme for allocating the Virtual Machine from an ad hoc sub server with Virtual Machines. EXECUTION MODEL: The execution of the research has been carried out into two sections, initially, the location of Virtual Machines and Physical Machine with the server has been taken place and subsequently, the cross-validation of allocation is addressed. For the sorting of Virtual Machines, Modified Best Fit Decreasing algorithm is used and Multi-Machine Job Scheduling is used while the placement process of jobs to an appropriate host. Artificial Neural Network as a classifier, has allocated jobs to the hosts. Measures, viz. Service Level Agreement violation and energy consumption are considered and fruitful results have been obtained with a 37.7 of reduction in energy consumption and 15% improvement in Service Level Agreement violation.

Cloud Adaptive Resource Allocation Mechanism for Efficient Parallel Processing

2014 ◽  
Vol 4 (4) ◽  
pp. 1-6 ◽  
Author(s):  
Manisha Malhotra ◽  
Rahul Malhotra
Keyword(s):  
Resource Allocation ◽  
Parallel Processing ◽  
Virtual Machines ◽  
Resource Provisioning ◽  
Cloud Provider ◽  
Adaptive Resource Allocation ◽  
Cloud Environment ◽  
Allocation Mechanism ◽  
Adaptive Resource ◽  
Resource Allocation Mechanism

As cloud based services becomes more assorted, resource provisioning becomes more challenges. This is an important issue that how resource may be allocated. The cloud environment offered distinct types of virtual machines and cloud provider distribute those services. This is necessary to adjust the allocation of services with the demand of user. This paper presents an adaptive resource allocation mechanism for efficient parallel processing based on cloud. Using this mechanism the provider's job becomes easier and having the least chance for the wastage of resources and time.

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 OLB: A Nature Inspired Approach for Load Balancing in Cloud Computing

2015 ◽  
Vol 15 (4) ◽  
pp. 138-148 ◽  
Author(s):  
B. Mallikarjuna ◽  
P. Venkata Krishna
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Distributed Systems ◽  
Load Balancing ◽  
Virtual Machines ◽  
Simulation Analysis ◽  
Hash Table ◽  
Distributed Hash Table ◽  
Efficient Resource

Abstract Load balancing is treated as one of the important mechanisms for efficient resource allocation in cloud computing. In future there will appear a necessity of fully autonomic distributed systems to address the load balancing issues. With reference to this, we proposed a load balancing mechanism called Osmosis Load Balancing (OLB). OLB works on the principle of osmosis to reschedule the tasks in virtual machines. The solution is based on the Distributed Hash Table (DHT) with a chord overlay mechanism. The Chord overlay is used for managing bio inspired agents and status of the cloud. By simulation analysis, the proposed algorithm has shown better performance in different scenarios, both in heterogeneous and homogeneous clouds.

scholarly journals Deployment of Applications Using Nginx Ingress Controller

2021 ◽  
Vol 23 (07) ◽  
pp. 352-357
Author(s):  
Gautham S ◽  
◽  
Maddula Abhijit ◽  
Prof. Sahana. B ◽  
◽  
...  
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Traffic Management ◽  
Virtual Machines ◽  
Storage Capacity ◽  
Production Environment ◽  
Wide Range ◽  
Software Code ◽  
Ease Of Access

Cloud computing is a method of storing and manipulating data by utilizing a network of remote servers. Cloud computing is becoming increasingly popular owing to its large storage capacity, ease of access, and wide range of services. Virtualization entered the picture when cloud computing progressed, and technologies or software such as virtual machines emerged. However, when customers’ computational needs for storage and servers rose, virtual machines were unable to meet those expectations owing to scalability and resource allocation limitations. As a result, containerization came into the picture. Containerization refers to the packaging of software code together with all of its necessary elements such as frameworks, libraries, and other dependencies such that they are isolated or segregated in their own container. Kubernetes used as an orchestration tool implements an ingress controller to route external traffic to deployments running on pods via ingress resource. This enables effective traffic management among the running applications avoiding unwanted blackouts in the production environment.

scholarly journals Resource Allocation in Cloud Computing using Heuristic Load Balancing Algorithm

2020 ◽  
Author(s):  
Anup Shrestha ◽  
Suriayati Chuprat ◽  
Nandini Mukherjee
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Load Balancing ◽  
Completion Time ◽  
Virtual Machines ◽  
It Services ◽  
Computing Environment ◽  
Load Balancing Algorithm ◽  
Better Than

Cloud computing is becoming more popular, unlike conventional computing, due to its added advantages. This is because it offers utility-based services to its subscribers upon their demand. Furthermore, this computing environment provides IT services to its users where they pay for every use. However, the increasing number of tasks requires virtual machines for them to be accomplished quickly. Load balancing a critical concern in cloud computing due to the massive increase in users' numbers. This paper proposes the best heuristic load balancing algorithm that will schedule a strategy for resource allocation that will minimize make span (completion time) in any technology that involves use cloud computing. The proposed algorithm performs better than other load balancing algorithms.

Application of Particle Swarm Optimisation-R Factor Optimisation Algorithm for Efficient Resource Allocation and Scheduling in Cloud Environment

2020 ◽  
Vol 17 (4) ◽  
pp. 1990-1998
Author(s):  
R. Valarmathi ◽  
T. Sheela
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Resource Management ◽  
Task Scheduling ◽  
Virtual Machines ◽  
Cost Optimization ◽  
Optimal Solution ◽  
Service Needs ◽  
Cloud Environment ◽  
Efficient Resource

Cloud computing is a powerful technology of computing which renders flexible services anywhere to the user. Resource management and task scheduling are essential perspectives of cloud computing. One of the main problems of cloud computing was task scheduling. Usually task scheduling and resource management in cloud is a tough optimization issue at the time of considering quality of service needs. Huge works under task scheduling focuses only on deadline issues and cost optimization and it avoids the significance of availability, robustness and reliability. The main purpose of this study is to develop an Optimized Algorithm for Efficient Resource Allocation and Scheduling in Cloud Environment. This study uses PSO and R factor algorithm. The main aim of PSO algorithm is that tasks are scheduled to VM (virtual machines) to reduce the time of waiting and throughput of system. PSO is a technique inspired by social and collective behavior of animal swarms in nature and wherein particles search the problem space to predict near optimal or optimal solution. A hybrid algorithm combining PSO and R-factor has been developed with the purpose of reducing the processing time, make span and cost of task execution simultaneously. The test results and simulation reveals that the proposed method offers better efficiency than the previously prevalent approaches.

Metaheuristic based auto-scaling for microservices in cloud environment: a new container-aware application scheduling

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Subramonian Krishna Sarma
Keyword(s):  
Resource Allocation ◽  
Data Storage ◽  
Virtual Machines ◽  
Search Algorithm ◽  
Case Scenario ◽  
Content Type ◽  
Dragonfly Algorithm ◽  
Platform As A Service ◽  
Adopted Model ◽  
Optimal Resource

Purpose The cloud is a network of servers to share computing resources to run applications and data storage that offers services in various flavours, namely, infrastructure as a service, platform as a service and software as a service. The containers in the cloud are defined as “standalone and self-contained units that package software and its dependencies together”. Similar to virtual machines, the virtualization method facilitates the resource on a specific server that could be used by numerous appliances. Design/methodology/approach This study introduces a new Dragon Levy updated squirrel algorithm (DLU-SA) for container aware application scheduling. Furthermore, the solution of optimal resource allocation is attained via defining the objective function that considers certain criteria such as “total network distance (TND), system failure (SF), balanced cluster use (BC) and threshold distance (TD)”. Eventually, the supremacy of the presented model is confirmed over existing models in terms of cost and statistical analysis. Findings On observing the outcomes, the total cost of an adopted model for Experimentation 1 has attained a lesser cost value, and it was 0.97%, 10.45% and 10.37% superior to traditional velocity updated grey wolf (VU-GWO), squirrel search algorithm (SSA) and dragonfly algorithm (DA) models, respectively, for mean case scenario. Especially, under best case scenario, the implemented model has revealed a minimal cost value of 761.95, whereas, the compared models such as whale random update assisted lion algorithm, VU-GWO, SSA and DA has revealed higher cost value of 761.98, 779.46, 766.62 and 766.51, respectively. Thus, the enhancement of the developed model has been validated over the existing works. Originality/value This paper proposes a new DLU-SA for container aware application scheduling. This is the first work that uses the DLU-SA model for optimal container resource allocation by taking into consideration of certain constraints such as TND, SF, BC and TD.

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