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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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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Related Journals

open access Open Access
recommended Recommended

Elsevier

Quality:  
High
CiteRatio: 8.7
SJR: 0.687
SNIP: 1.553
open access Open Access

Frontiers Media

Quality:  
High
CiteRatio: 6.2
SJR: 0.427
SNIP: 1.319
open access Open Access

Springer

Quality:  
High
CiteRatio: 5.0
SJR: 0.445
SNIP: 1.076
open access Open Access

Springer

Quality:  
High
CiteRatio: 4.1
SJR: 0.337
SNIP: 0.919

Journal Performance & Insights

CiteRatio

SCImago Journal Rank (SJR)

Source Normalized Impact per Paper (SNIP)

A measure of average citations received per peer-reviewed paper published in the journal.

Measures weighted citations received by the journal. Citation weighting depends on the categories and prestige of the citing journal.

Measures actual citations received relative to citations expected for the journal's category.

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

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

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

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

insights Insights

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

insights Insights

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

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
580 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
412 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
299 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
267 Citations
Journal Article DOI: 10.1109/TCC.2016.2560808
Optimal Joint Scheduling and Cloud Offloading for Mobile Applications
S. Eman Mahmoodi1, R. N. Uma2, Koduvayur P. Subbalakshmi1

Abstract:

Cloud offloading is an indispensable solution to supporting computationally demanding applications on resource constrained mobile devices. In this paper, we introduce the concept of wireless aware joint scheduling and computation offloading (JSCO) for multi-component applications, where an optimal decision is made on which co... Cloud offloading is an indispensable solution to supporting computationally demanding applications on resource constrained mobile devices. In this paper, we introduce the concept of wireless aware joint scheduling and computation offloading (JSCO) for multi-component applications, where an optimal decision is made on which components need to be offloaded as well as the scheduling order of these components. The JSCO approach allows for more degrees of freedom in the solution by moving away from a compiler pre-determined scheduling order for the components towards a more wireless aware scheduling order. For some component dependency graph structures, the proposed algorithm can shorten execution times by parallel processing appropriate components in the mobile and cloud. We define a net utility that trades-off the energy saved by the mobile, subject to constraints on the communication delay, overall application execution time, and component precedence ordering. The linear optimization problem is solved using real data measurements obtained from running multi-component applications on an HTC smartphone and the Amazon EC2, using WiFi for cloud offloading. The performance is further analyzed using various component dependency graph topologies and sizes. Results show that the energy saved increases with longer application runtime deadline, higher wireless rates, and smaller offload data sizes. read more read less

Topics:

Computation offloading (67%)67% related to the paper, Dynamic priority scheduling (62%)62% related to the paper, Fair-share scheduling (62%)62% related to the paper, Mobile cloud computing (61%)61% related to the paper, Earliest deadline first scheduling (61%)61% related to the paper
219 Citations
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IEEE Transactions on Cloud Computing format uses IEEEtran citation style.

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Frequently asked questions

1. Can I write IEEE Transactions on Cloud Computing in LaTeX?

Absolutely not! Our tool has been designed to help you focus on writing. You can write your entire paper as per the IEEE Transactions on Cloud Computing guidelines and auto format it.

2. Do you follow the IEEE Transactions on Cloud Computing guidelines?

Yes, the template is compliant with the IEEE Transactions on Cloud Computing guidelines. Our experts at SciSpace ensure that. If there are any changes to the journal's guidelines, we'll change our algorithm accordingly.

3. Can I cite my article in multiple styles in IEEE Transactions on Cloud Computing?

Of course! We support all the top citation styles, such as APA style, MLA style, Vancouver style, Harvard style, and Chicago style. For example, when you write your paper and hit autoformat, our system will automatically update your article as per the IEEE Transactions on Cloud Computing citation style.

4. Can I use the IEEE Transactions on Cloud Computing templates for free?

Sign up for our free trial, and you'll be able to use all our features for seven days. You'll see how helpful they are and how inexpensive they are compared to other options, Especially for IEEE Transactions on Cloud Computing.

5. Can I use a manuscript in IEEE Transactions on Cloud Computing that I have written in MS Word?

Yes. You can choose the right template, copy-paste the contents from the word document, and click on auto-format. Once you're done, you'll have a publish-ready paper IEEE Transactions on Cloud Computing that you can download at the end.

6. How long does it usually take you to format my papers in IEEE Transactions on Cloud Computing?

It only takes a matter of seconds to edit your manuscript. Besides that, our intuitive editor saves you from writing and formatting it in IEEE Transactions on Cloud Computing.

7. Where can I find the template for the IEEE Transactions on Cloud Computing?

It is possible to find the Word template for any journal on Google. However, why use a template when you can write your entire manuscript on SciSpace , auto format it as per IEEE Transactions on Cloud Computing's guidelines and download the same in Word, PDF and LaTeX formats? Give us a try!.

8. Can I reformat my paper to fit the IEEE Transactions on Cloud Computing's guidelines?

Of course! You can do this using our intuitive editor. It's very easy. If you need help, our support team is always ready to assist you.

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SciSpace's IEEE Transactions on Cloud Computing is currently available as an online tool. We're developing a desktop version, too. You can request (or upvote) any features that you think would be helpful for you and other researchers in the "feature request" section of your account once you've signed up with us.

10. I cannot find my template in your gallery. Can you create it for me like IEEE Transactions on Cloud Computing?

Sure. You can request any template and we'll have it setup within a few days. You can find the request box in Journal Gallery on the right side bar under the heading, "Couldn't find the format you were looking for like IEEE Transactions on Cloud Computing?”

11. What is the output that I would get after using IEEE Transactions on Cloud Computing?

After writing your paper autoformatting in IEEE Transactions on Cloud Computing, you can download it in multiple formats, viz., PDF, Docx, and LaTeX.

12. Is IEEE Transactions on Cloud Computing's impact factor high enough that I should try publishing my article there?

To be honest, the answer is no. The impact factor is one of the many elements that determine the quality of a journal. Few of these factors include review board, rejection rates, frequency of inclusion in indexes, and Eigenfactor. You need to assess all these factors before you make your final call.

13. What is Sherpa RoMEO Archiving Policy for IEEE Transactions on Cloud Computing?

SHERPA/RoMEO Database

We extracted this data from Sherpa Romeo to help researchers understand the access level of this journal in accordance with the Sherpa Romeo Archiving Policy for IEEE Transactions on Cloud Computing. The table below indicates the level of access a journal has as per Sherpa Romeo's 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.

14. What are the most common citation types In IEEE Transactions on Cloud Computing?

The 5 most common citation types in order of usage for IEEE Transactions on Cloud Computing are:.

S. No. Citation Style Type
1. Author Year
2. Numbered
3. Numbered (Superscripted)
4. Author Year (Cited Pages)
5. Footnote

15. How do I submit my article to the IEEE Transactions on Cloud Computing?

It is possible to find the Word template for any journal on Google. However, why use a template when you can write your entire manuscript on SciSpace , auto format it as per IEEE Transactions on Cloud Computing's guidelines and download the same in Word, PDF and LaTeX formats? Give us a try!.

16. Can I download IEEE Transactions on Cloud Computing in Endnote format?

Yes, SciSpace provides this functionality. After signing up, you would need to import your existing references from Word or Bib file to SciSpace. Then SciSpace would allow you to download your references in IEEE Transactions on Cloud Computing Endnote style according to Elsevier guidelines.

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