A Comprehensive Survey on Cloud Computing

Alam, Md. Imran; Pandey, Manjusha; Rautaray, Siddharth Swarup

doi:10.5815/ijitcs.2015.02.09

Cited by 32 publications

(19 citation statements)

References 6 publications

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“…In ① of Figure 6, a new divided computing service is allocated in proportion to the CPU speed of the current DSN according to Equation (4). The DSN is allocated by subdividing the divided computing service in the DRN to which it belongs.…”

Section: Nondisruptive Computing and Storage Service Schemementioning

confidence: 99%

“…In this paper, resources were allocated in a hierarchical manner based on the updated CPU information for each computing service request, as shown in Figure 6. In ① of Figure 6, a new divided computing service is allocated in proportion to the CPU speed of the current DSN according to Equation (4). The DSN is allocated by subdividing the divided computing service in the DRN to which it belongs.…”

Section: Nondisruptive Computing and Storage Service Schemementioning

confidence: 99%

“…This is repeated until all desktops connected to the on-premise-based integrated resource are included in the cluster set. 4 CHECK CLUSTER CENTER VALUE; the center of the distance value of the desktops in each cluster of cluster set C is calculated. The location of the closest desktop is selected as a new cluster for the corresponding set.…”

Section: Clustering Scheme For Integrated Resource Managementmentioning

confidence: 99%

“…Although traditional on-premise computing environments continue to be built, the need remains for improving the total cost (TC) of ownership and maintenance costs of cloud systems [1][2][3][4][5][6][7][8]. The only solution to this problem, however, is to discard surplus resources, which are connected to traditional on-premise systems, or to introduce virtualization technology for all surplus resources.…”

Section: Introductionmentioning

confidence: 99%

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DIaaS: Resource Management System for the Intra-Cloud with On-Premise Desktops

et al. 2017

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Infrastructure as a service with desktops (DIaaS) based on the extensible mark-up language (XML) is herein proposed to utilize surplus resources. DIaaS is a traditional surplus-resource integrated management technology. It is designed to provide fast work distribution and computing services based on user service requests as well as storage services through desktop-based distributed computing and storage resource integration. DIaaS includes a nondisruptive resource service and an auto-scalable scheme to enhance the availability and scalability of intra-cloud computing resources. A performance evaluation of the proposed scheme measured the clustering performance time for surplus resource utilization. The results showed improvement in computing and storage services in a connection of at least two computers compared to the traditional method for high-availability measurement of nondisruptive services. Furthermore, an artificial server error environment was used to create a clustering delay for computing and storage services and for nondisruptive services. It was compared to the Hadoop distributed file system (HDFS).

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Section: Nondisruptive Computing and Storage Service Schemementioning

confidence: 99%

Section: Nondisruptive Computing and Storage Service Schemementioning

confidence: 99%

Section: Clustering Scheme For Integrated Resource Managementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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DIaaS: Resource Management System for the Intra-Cloud with On-Premise Desktops

et al. 2017

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“…Cloud computing offers advantage by allowing users to use infrastructures (for example, servers, networks and storage facilities),operating systems (for example, middleware services and operating systems) and software (for example, applications) that are provided by cloud providers (e.g., Google, Amazon and Sales force) with low cost [7,8,9]. In addition, cloud computing enables users to use required resources flexibly.…”

Section: Introductionmentioning

confidence: 99%

An Energy-efficient Approach based on Learning Automata in Mobile Cloud Computing

Ghobaei‐Arani¹,

Moghadasi²

2015

IJGDC

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Applying queue theory for modeling of cloud computing: A systematic review

Ghomi

Rahmani

Qader

2019

Concurrency and Computation

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The cloud computing paradigm is an important service in the Internet for sharing and providing resources in a cost-efficient way. Modeling of a cloud system is not an easy task because of the complexity and large scale of such systems. Cloud reliability could be improved by modeling the various aspects of cloud systems, including scheduling, service time, wait time, and hardware and software failures. The aim of this study is to survey research studies done on the modeling of cloud computing using the queuing system in order to identify where more emphasis should be placed in both current and future research directions. This paper follows the goal by investigating the articles published between 2008 and January 2017 in journals and conferences. A systematic mapping study combined with a systematic literature review was performed to find the related literature, and 71 articles were selected as primary studies that were classified in relation to the focus, research type, and contribution type. We classified the modeling techniques of cloud computing using the queuing theory in seven categories based on their focus area: (1) performance, (2) quality of service, (3) workflow scheduling, (4) energy savings, (5) resource management, (6) priority-based servicing, and (7) reliability. A majority of the primary articles focus on performance (37%), 15% of them focus on resource management, 14% of them focus on quality of service, 13% of them focus on workflow scheduling, 13% of them focus on energy savings, 4% of them focus on priority-based servicing for requests, and 4% of them focus on reliability. This work summarizes and classifies the research efforts conducted on applying queue theory for modeling of cloud computing (AQTMCC), providing a good starting point for further research in this area. KEYWORDScloud computing, cloud modeling, queuing systems, queuing theory, systematic review INTRODUCTIONCloud computing is the recent evolution in the information technology and is growing very fast due to the extensive use of mobile devices, such as PDAs, cell phones, and tablets. 1 To emphasize the goals of cloud computing, we refer to the definition of NIST (National Institute of Standards and Technology); a cloud model is composed of five characteristics, three service models, and four deployment models. 2 Service models of cloud computing are as follows: (1) Software as a Service (SaaS), such as CRM (Customer Relationship Management), virtual desktop, communications, and games; (2) Platform as a Service (PaaS), such as databases, web servers, and deployment tools; and (3) Infrastructure as a Service (IaaS), such as virtual machines (VMs), storages, servers, and load balancers. Deployment models include private clouds, public clouds, community clouds, and hybrid clouds. The characteristics of a cloud are on-demand self-servicing, elasticity, broad network access, resource pooling, and measured service. 1 A data center in cloud computing consists of several servers that are organized in racks and connected through communicati...

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A Comprehensive Survey on Cloud Computing

Cited by 32 publications

References 6 publications

DIaaS: Resource Management System for the Intra-Cloud with On-Premise Desktops

DIaaS: Resource Management System for the Intra-Cloud with On-Premise Desktops

An Energy-efficient Approach based on Learning Automata in Mobile Cloud Computing

Applying queue theory for modeling of cloud computing: A systematic review

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