A comprehensive survey on container resource allocation approaches in cloud computing: State-of-the-art and research challenges

2022 ◽  
pp. 1-22
Author(s):  
Vhatkar Kapil Netaji ◽  
G.P. Bhole
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Virtual Machines ◽  
State Of The Art ◽  
Research Work ◽  
Research Papers ◽  
Virtualization Technology ◽  
Comprehensive Survey ◽  
Novel Algorithms ◽  
Allocation Algorithms

The allocation of resources in the cloud environment is efficient and vital, as it directly impacts versatility and operational expenses. Containers, like virtualization technology, are gaining popularity due to their low overhead when compared to traditional virtual machines and portability. The resource allocation methodologies in the containerized cloud are intended to dynamically or statically allocate the available pool of resources such as CPU, memory, disk, and so on to users. Despite the enormous popularity of containers in cloud computing, no systematic survey of container scheduling techniques exists. In this survey, an outline of the present works on resource allocation in the containerized cloud correlative is discussed. In this work, 64 research papers are reviewed for a better understanding of resource allocation, management, and scheduling. Further, to add extra worth to this research work, the performance of the collected papers is investigated in terms of various performance measures. Along with this, the weakness of the existing resource allocation algorithms is provided, which makes the researchers to investigate with novel algorithms or techniques.

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.

scholarly journals Taxonomy of resource allocation algorithms for inner IaaS cloud data centres

2013 ◽  
Author(s):  
Dang Minh Quan
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Virtual Machines ◽  
Computer Hardware ◽  
Infrastructure As A Service ◽  
Cloud Data ◽  
Cloud Infrastructures ◽  
Allocation Algorithms ◽  
Data Centres ◽  
Cloud User

Cloud computing has become more and more popular  with  the  widely  deployment  of  several  cloud infrastructures.  Infrastructure-as-a-service  (IaaS) Cloud  computing  replaces  bare  computer hardware. The cloud user  will use the virtual  machines (VMs)  to  fullfil  their  computing  requirements.  Among the  components  of  IaaS  cloud  software  stack,  the resource  allocation  module  is  very  important  as  it selects suitable VMs and the place to execute VMs. This paper  focuses  on  studying  and  classifying  algorithms used  in  the  resource  allocation  module.  The  issues  of how to apply those algorithms are also discussed.

scholarly journals Minimizing Resource Waste in Heterogeneous Resource Allocation for Data Stream Processing on Clouds

2020 ◽  
Vol 11 (1) ◽  
pp. 149
Author(s):  
Wu-Chun Chung ◽  
Tsung-Lin Wu ◽  
Yi-Hsuan Lee ◽  
Kuo-Chan Huang ◽  
Hung-Chang Hsiao ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Data Stream ◽  
Limited Attention ◽  
Heterogeneous Servers ◽  
Resource Allocation Algorithm ◽  
System Utilization ◽  
Allocation Algorithms ◽  
Considerable Resource ◽  
Heterogeneous Resources

Resource allocation is vital for improving system performance in big data processing. The resource demand for various applications can be heterogeneous in cloud computing. Therefore, a resource gap occurs while some resource capacities are exhausted and other resource capacities on the same server are still available. This phenomenon is more apparent when the computing resources are more heterogeneous. Previous resource-allocation algorithms paid limited attention to this situation. When such an algorithm is applied to a server with heterogeneous resources, resource allocation may result in considerable resource wastage for the available but unused resources. To reduce resource wastage, a resource-allocation algorithm, called the minimizing resource gap (MRG) algorithm, for heterogeneous resources is proposed in this study. In MRG, the gap between resource usages for each server in cloud computing and the resource demands among various applications are considered. When an application is launched, MRG calculates resource usage and allocates resources to the server with the minimized usage gap to reduce the amount of available but unused resources. To demonstrate MRG performance, the MRG algorithm was implemented in Apache Spark. CPU- and memory-intensive applications were applied as benchmarks with different resource demands. Experimental results proved the superiority of the proposed MRG approach for improving the system utilization to reduce the overall completion time by up to 24.7% for heterogeneous servers in cloud computing.

A Comprehensive Survey on Techniques Based on TPM for Ensuring the Confidentiality in Cloud Data Centers

2019 ◽  
pp. 446-458
Author(s):  
Arun Fera M. ◽  
M. Saravanapriya ◽  
J. John Shiny
Keyword(s):  
Cloud Computing ◽  
Data Storage ◽  
Research Work ◽  
Computing Systems ◽  
Trusted Platform Module ◽  
Cloud Data ◽  
Secure Storage ◽  
Comprehensive Survey ◽  
Cloud Data Centers ◽  
Trusted Platform

Cloud computing is one of the most vital technology which becomes part and parcel of corporate life. It is considered to be one of the most emerging technology which serves for various applications. Generally these Cloud computing systems provide a various data storage services which highly reduces the complexity of users. we mainly focus on addressing in providing confidentiality to users' data. We are proposing one mechanism for addressing this issue. Since software level security has vulnerabilities in addressing the solution to our problem we are dealing with providing hardware level of security. We are focusing on Trusted Platform Module (TPM) which is a chip in computer that is used for secure storage that is mainly used to deal with authentication problem. TPM which when used provides a trustworthy environment to the users. A detailed survey on various existing TPM related security and its implementations is carried out in our research work.

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.

Novel resource allocation algorithms to performance and energy efficiency in cloud computing

2014 ◽  
Vol 69 (3) ◽  
pp. 1445-1461 ◽  
Author(s):  
Abbas Horri ◽  
Mohammad Sadegh Mozafari ◽  
Gholamhossein Dastghaibyfard
Keyword(s):  
Resource Allocation ◽  
Energy Efficiency ◽  
Cloud Computing ◽  
Allocation Algorithms

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.

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