Example of IEEE Transactions on Cloud Computing format
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Example of IEEE Transactions on Cloud Computing format Example of IEEE Transactions on Cloud Computing format Example of IEEE Transactions on Cloud Computing format Example of IEEE Transactions on Cloud Computing format Example of IEEE Transactions on Cloud Computing format Example of IEEE Transactions on Cloud Computing format Example of IEEE Transactions on Cloud Computing format Example of IEEE Transactions on Cloud Computing format Example of IEEE Transactions on Cloud Computing format Example of IEEE Transactions on Cloud Computing format Example of IEEE Transactions on Cloud Computing format
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Example of IEEE Transactions on Cloud Computing format Example of IEEE Transactions on Cloud Computing format Example of IEEE Transactions on Cloud Computing format Example of IEEE Transactions on Cloud Computing format Example of IEEE Transactions on Cloud Computing format Example of IEEE Transactions on Cloud Computing format Example of IEEE Transactions on Cloud Computing format Example of IEEE Transactions on Cloud Computing format Example of IEEE Transactions on Cloud Computing format Example of IEEE Transactions on Cloud Computing format Example of IEEE Transactions on Cloud Computing format
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open access Open Access ISSN: 21687161
recommended Recommended

IEEE Transactions on Cloud Computing — Template for authors

Publisher: IEEE
Categories Rank Trend in last 3 yrs
Information Systems #19 of 329 down down by 8 ranks
Computer Science Applications #40 of 693 down down by 23 ranks
Hardware and Architecture #10 of 157 down down by 4 ranks
Computer Networks and Communications #21 of 334 down down by 7 ranks
Software #33 of 389 down down by 15 ranks
journal-quality-icon Journal quality:
High
calendar-icon Last 4 years overview: 294 Published Papers | 3174 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 16/06/2020
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General info
Top papers
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FAQ

Journal Performance & Insights

  • CiteRatio
  • SJR
  • SNIP

CiteRatio is a measure of average citations received per peer-reviewed paper published in the journal.

10.8

8% from 2019

CiteRatio for IEEE Transactions on Cloud Computing from 2016 - 2020
Year Value
2020 10.8
2019 10.0
2018 8.5
2017 9.5
2016 9.5
graph view Graph view
table view Table view

insights Insights

  • CiteRatio of this journal has increased by 8% in last years.
  • This journal’s CiteRatio is in the top 10 percentile category.

SCImago Journal Rank (SJR) measures weighted citations received by the journal. Citation weighting depends on the categories and prestige of the citing journal.

1.075

10% from 2019

SJR for IEEE Transactions on Cloud Computing from 2016 - 2020
Year Value
2020 1.075
2019 0.974
2018 0.921
2017 1.038
2016 1.127
graph view Graph view
table view Table view

insights Insights

  • SJR of this journal has increased by 10% in last years.
  • This journal’s SJR is in the top 10 percentile category.

Source Normalized Impact per Paper (SNIP) measures actual citations received relative to citations expected for the journal's category.

2.756

17% from 2019

SNIP for IEEE Transactions on Cloud Computing from 2016 - 2020
Year Value
2020 2.756
2019 2.352
2018 2.727
2017 3.512
2016 3.68
graph view Graph view
table view Table view

insights Insights

  • SNIP of this journal has increased by 17% in last years.
  • This journal’s SNIP is in the top 10 percentile category.

Related Journals

open access Open Access ISSN: 15741192
recommended Recommended

Elsevier

CiteRatio: 8.7 | SJR: 0.687 | SNIP: 1.553
open access Open Access e-ISSN: 2297198X

Frontiers Media

CiteRatio: 6.2 | SJR: 0.427 | SNIP: 1.319
open access Open Access ISSN: 1383469X e-ISSN: 15728153

Springer

CiteRatio: 5.0 | SJR: 0.445 | SNIP: 1.076
open access Open Access ISSN: 9236082 e-ISSN: 15730824

Springer

CiteRatio: 4.1 | SJR: 0.337 | SNIP: 0.919

IEEE Transactions on Cloud Computing

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IEEE

IEEE Transactions on Cloud Computing

Approved by publishing and review experts on SciSpace, this template is built as per for IEEE Transactions on Cloud Computing formatting guidelines as mentioned in IEEE author instructions. The current version was created on 15 Jun 2020 and has been used by 119 authors to write and format their manuscripts to this journal.

Computer Science

i
Last updated on
15 Jun 2020
i
ISSN
2168-7161
i
Open Access
No
i
Sherpa RoMEO Archiving Policy
Green faq
i
Plagiarism Check
Available via Turnitin
i
Endnote Style
Download Available
i
Bibliography Name
IEEEtran
i
Citation Type
Numbered
[25]
i
Bibliography Example
C. W. J. Beenakker, “Specular andreev reflection in graphene,” Phys. Rev. Lett., vol. 97, no. 6, p.

Top papers written in this journal

Journal Article DOI: 10.1109/TCC.2015.2485206
Assessment of the Suitability of Fog Computing in the Context of Internet of Things
Subhadeep Sarkar1, Subarna Chatterjee1, Sudip Misra1

Abstract:

This work performs a rigorous, comparative analysis of the fog computing paradigm and the conventional cloud computing paradigm in the context of the Internet of Things (IoT), by mathematically formulating the parameters and characteristics of fog computing—one of the first attempts of its kind. With the rapid increase in the... This work performs a rigorous, comparative analysis of the fog computing paradigm and the conventional cloud computing paradigm in the context of the Internet of Things (IoT), by mathematically formulating the parameters and characteristics of fog computing—one of the first attempts of its kind. With the rapid increase in the number of Internet-connected devices, the increased demand of real-time, low-latency services is proving to be challenging for the traditional cloud computing framework. Also, our irreplaceable dependency on cloud computing demands the cloud data centers (DCs) always to be up and running which exhausts huge amount of power and yield tons of carbon dioxide ( $\text{CO}_2$ ) gas. In this work, we assess the applicability of the newly proposed fog computing paradigm to serve the demands of the latency-sensitive applications in the context of IoT. We model the fog computing paradigm by mathematically characterizing the fog computing network in terms of power consumption, service latency, $\text{CO}_2$ emission, and cost, and evaluating its performance for an environment with high number of Internet-connected devices demanding real-time service. A case study is performed with traffic generated from the $100$ highest populated cities being served by eight geographically distributed DCs. Results show that as the number of applications demanding real-time service increases, the fog computing paradigm outperforms traditional cloud computing. For an environment with $50$ percent applications requesting for instantaneous, real-time services, the overall service latency for fog computing is noted to decrease by $50.09$ percent. However, it is mentionworthy that for an environment with less percentage of applications demanding for low-latency services, fog computing is observed to be an overhead compared to the traditional cloud computing. Therefore, the work shows that in the context of IoT, with high number of latency-sensitive applications fog computing outperforms cloud computing. read more read less

Topics:

Utility computing (64%)64% related to the paper, Cloud computing (63%)63% related to the paper
458 Citations
Journal Article DOI: 10.1109/TCC.2015.2449834
Optimal Cloudlet Placement and User to Cloudlet Allocation in Wireless Metropolitan Area Networks
Mike Jia1, Jiannong Cao2, Weifa Liang1

Abstract:

Mobile applications are becoming increasingly computation-intensive, while the computing capability of portable mobile devices is limited. A powerful way to reduce the completion time of an application in a mobile device is to offload its tasks to nearby cloudlets, which consist of clusters of computers. Although there is a s... Mobile applications are becoming increasingly computation-intensive, while the computing capability of portable mobile devices is limited. A powerful way to reduce the completion time of an application in a mobile device is to offload its tasks to nearby cloudlets, which consist of clusters of computers. Although there is a significant body of research in mobile cloudlet offloading technology, there has been very little attention paid to how cloudlets should be placed in a given network to optimize mobile application performance. In this paper we study cloudlet placement and mobile user allocation to the cloudlets in a wireless metropolitan area network (WMAN). We devise an algorithm for the problem, which enables the placement of the cloudlets at user dense regions of the WMAN, and assigns mobile users to the placed cloudlets while balancing their workload. We also conduct experiments through simulation. The simulation results indicate that the performance of the proposed algorithm is very promising. read more read less

Topics:

Cloudlet (68%)68% related to the paper, Mobile cloud computing (62%)62% related to the paper, Mobile computing (59%)59% related to the paper, Mobile technology (56%)56% related to the paper, Mobile telephony (53%)53% related to the paper
292 Citations
Journal Article DOI: 10.1109/TCC.2016.2551747
Energy-Efficient Adaptive Resource Management for Real-Time Vehicular Cloud Services
Mohammad Shojafar1, Nicola Cordeschi1, Enzo Baccarelli1

Abstract:

Providing real-time cloud services to Vehicular Clients (VCs) must cope with delay and delay-jitter issues. Fog computing is an emerging paradigm that aims at distributing small-size self-powered data centers (e.g., Fog nodes) between remote Clouds and VCs, in order to deliver data-dissemination real-time services to the conn... Providing real-time cloud services to Vehicular Clients (VCs) must cope with delay and delay-jitter issues. Fog computing is an emerging paradigm that aims at distributing small-size self-powered data centers (e.g., Fog nodes) between remote Clouds and VCs, in order to deliver data-dissemination real-time services to the connected VCs. Motivated by these considerations, in this paper, we propose and test an energy-efficient adaptive resource scheduler for Networked Fog Centers (NetFCs). They operate at the edge of the vehicular network and are connected to the served VCs through Infrastructure-to-Vehicular (I2V) TCP/IP-based single-hop mobile links. The goal is to exploit the locally measured states of the TCP/IP connections, in order to maximize the overall communication-plus-computing energy efficiency, while meeting the application-induced hard QoS requirements on the minimum transmission rates, maximum delays and delay-jitters. The resulting energy-efficient scheduler jointly performs: (i) admission control of the input traffic to be processed by the NetFCs; (ii) minimum-energy dispatching of the admitted traffic; (iii) adaptive reconfiguration and consolidation of the Virtual Machines (VMs) hosted by the NetFCs; and, (iv) adaptive control of the traffic injected into the TCP/IP mobile connections. The salient features of the proposed scheduler are that: (i) it is adaptive and admits distributed and scalable implementation; and, (ii) it is capable to provide hard QoS guarantees, in terms of minimum/maximum instantaneous rates of the traffic delivered to the vehicular clients, instantaneous rate-jitters and total processing delays. Actual performance of the proposed scheduler in the presence of: (i) client mobility; (ii) wireless fading; and, (iii) reconfiguration and consolidation costs of the underlying NetFCs, is numerically tested and compared against the corresponding ones of some state-of-the-art schedulers, under both synthetically generated and measured real-world workload traces. read more read less

Topics:

Quality of service (54%)54% related to the paper, Admission control (52%)52% related to the paper, Cloud computing (52%)52% related to the paper, Control reconfiguration (52%)52% related to the paper, Adaptive control (50%)50% related to the paper
251 Citations
open accessOpen access Journal Article DOI: 10.1109/TCC.2016.2522439
Joint Energy Minimization and Resource Allocation in C-RAN with Mobile Cloud
Kezhi Wang1, Kun Yang1, Chathura M. Sarathchandra Magurawalage1

Abstract:

Cloud radio access network (C-RAN) has emerged as a potential candidate of the next generation access network technology to address the increasing mobile traffic, while mobile cloud computing (MCC) offers a prospective solution to the resource-limited mobile user in executing computation intensive tasks. Taking full advantage... Cloud radio access network (C-RAN) has emerged as a potential candidate of the next generation access network technology to address the increasing mobile traffic, while mobile cloud computing (MCC) offers a prospective solution to the resource-limited mobile user in executing computation intensive tasks. Taking full advantages of above two cloud-based techniques, C-RAN with MCC are presented in this paper to enhance both performance and energy efficiencies. In particular, this paper studies the joint energy minimization and resource allocation in C-RAN with MCC under the time constraints of the given tasks. We first review the energy and time model of the computation and communication. Then, we formulate the joint energy minimization into a non-convex optimization with the constraints of task executing time, transmitting power, computation capacity and fronthaul data rates. This non-convex optimization is then reformulated into an equivalent convex problem based on weighted minimum mean square error (WMMSE). The iterative algorithm is finally given to deal with the joint resource allocation in C-RAN with mobile cloud. Simulation results confirm that the proposed energy minimization and resource allocation solution can improve the system performance and save energy. read more read less

Topics:

Mobile cloud computing (66%)66% related to the paper, Resource allocation (59%)59% related to the paper, C-RAN (58%)58% related to the paper, Radio access network (57%)57% related to the paper, Mobile computing (56%)56% related to the paper
View PDF
179 Citations
Journal Article DOI: 10.1109/TCC.2017.2702586
Cloud Container Technologies: A State-of-the-Art Review
Claus Pahl1, Antonio Brogi2, Jacopo Soldani2, Pooyan Jamshidi3

Abstract:

Containers as a lightweight technology to virtualise applications have recently been successful, particularly to manage applications in the cloud. Often, the management of clusters of containers becomes essential and the orchestration of the construction and deployment becomes a central problem. This emerging topic has been t... Containers as a lightweight technology to virtualise applications have recently been successful, particularly to manage applications in the cloud. Often, the management of clusters of containers becomes essential and the orchestration of the construction and deployment becomes a central problem. This emerging topic has been taken up by researchers, but there is currently no secondary study to consolidate this research. We aim to identify, taxonomically classify and systematically compare the existing research body on containers and their orchestration and specifically the application of this technology in the cloud. We have conducted a systematic mapping study of 46 selected studies. We classified and compared the selected studies based on a characterisation framework. This results in a discussion of agreed and emerging concerns in the container orchestration space, positioning it within the cloud context, but also moving it closer to current concerns in cloud platforms, microservices and continuous development. read more read less

Topics:

Orchestration (computing) (61%)61% related to the paper, Cloud computing (56%)56% related to the paper, Microservices (54%)54% related to the paper
177 Citations
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IEEE Transactions on Cloud Computing format uses IEEEtran citation style.

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One little Google search can get you the Word template for any journal. However, why do you need a Word template when you can write your entire manuscript on SciSpace, autoformat it as per IEEE Transactions on Cloud Computing's guidelines and download the same in Word, PDF and LaTeX formats? Try us out!.

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SHERPA/RoMEO Database

We have extracted this data from Sherpa Romeo to help our researchers understand the access level of this journal. The following table indicates the level of access a journal has as per Sherpa Romeo Archiving Policy.

RoMEO Colour Archiving policy
Green Can archive pre-print and post-print or publisher's version/PDF
Blue Can archive post-print (ie final draft post-refereeing) or publisher's version/PDF
Yellow Can archive pre-print (ie pre-refereeing)
White Archiving not formally supported
FYI:
  1. Pre-prints as being the version of the paper before peer review and
  2. Post-prints as being the version of the paper after peer-review, with revisions having been made.

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