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Improving latency in Internet-of-Things and cloud computing for real-time data transmission: a systematic literature review (SLR)

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Abstract

To store, analyse and process the large volume of data generated by IoT traditional cloud computing, is used everywhere. However, the traditional cloud data centres have their limitations to handle high latency issues in time-critical applications of IoT and cloud. Their applications are computer gaming, e-healthcare, telemedicine and robot surgery. The high latency in IoTs and cloud includes high computational, communication latency (service) and network latencies. The vital requirement of IoT is to have minimum network, service and computation latencies for real-time applications. Network latency causes a delay in transmitting a message or communication from one location to another. Services that require data in real-time are almost impossible to access the data via the cloud. Traditional cloud computing approaches are unable to fulfil the quality-of-service (QoS) requirements in IoT devices. Researches related to latency reduction techniques are still in infancy. Some new approaches to minimize the latency for transmitting time-sensitive data in real-time are discussed in this paper for cloud and IoT devices. This research will help the researchers and industries to identify the techniques and technologies to minimize the latencies in IoT and cloud. The paper also discusses the research trends and the technical differences between the various technologies and techniques. With the increasing interest in the literature on latency minimization and its requirements for time-sensitive applications; it is important to systematically review and synthesize the approaches, tools, challenges and techniques to minimize latencies in IoT and cloud. This paper aims at systematically reviewing the state of the art of latency minimization to classify approaches, and techniques. The paper uses a PRISMA technique for a systematic review. The paper further identifies challenges and gaps in this regard for future research. We have identified 23 approaches and 32 technologies associated with latencies in the cloud and IoT. A total of 112 papers on latency reduction have been examined under this study. The existing research gaps and works for latency reduction in IoTs are discussed in detail. There are several challenges and gaps, which requires future research work for improving the latency minimization techniques and technologies. Finally, we present some open issues which will determine the future research direction.

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Abbreviations

IoT:

Internet-of-Things

FIS:

Fuzzy inference system

FC:

Fog computing

MDP:

Markov decision process

RL:

Reinforcement learning

NN:

Neural network

IDC:

International Data Corporation

NFV:

Network function virtualization

CDC:

Cloud data centers

ICSN:

Information-centric social networks

ICN:

Information-centric network

VNF-RM:

Virtual network function real-time migration

CDN:

Content delivery network

VMM:

Virtual machine migration

ECG:

Electrocardiogram

SFC:

Software function chaining

SPSRP:

Service popularity-based smart resources partitioning

GAP:

Generalized assignment problem

VNF:

Virtual network function

EEG:

Electroencephalogram

NP:

Nondeterministic polynomial time

QoS:

Quality of service

IRC:

Information Resource Center

MS:

Milli seconds

KB:

Kilobytes

MB:

Megabytes

KJ:

Kilojoules

F-RAN:

Fog-radio access networks

VM:

Virtual machines

CBR:

Constant bit rate

VBR:

Variable bit rate

FCSS:

Fog computing security service

CORD:

Central Office Re-architected as a Datacenter

LR:

Literature review

TCP:

Transmission control protocol

EMG:

Electromyography

KBPS:

KiloBytes per second

RAM:

Random access memory

WBAN:

Wireless body area network

SDN:

Software-defined network

PoP:

Post office protocol

SFC:

Software function chaining

WAN:

Wide area network

LAN:

Local area network

F2C:

Fog-to-cloud

LOCPART:

Latency optimized cache partitioning for cloud datacenters

IP:

Internet Protocol

DCQCN:

Datacentre quantized congestion notification

RDMA:

Remote Direct Memory Access Technology

FDM:

Frequency division multiplexing

MEC:

Mobile edge computing

IRS:

Intelligently reflecting surfaces

DDDPG:

Double-duelling-deterministic policy gradient

DDQS:

Double deep Q-learning scheduling

PFC:

Priority-based flow control

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Acknowledgements

The author would like to thank the Centre of Graduate Studies, Computer and Information Science Department, Universiti Teknologi PETRONAS, Malaysia for their expertise and cooperation in this research.

Funding

Using project allowance and self-finance.

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Authors and Affiliations

  1. Electrical and Electronics Department, Data Science Institute (DSI), National University of Ireland Galway (NUIG), Galway, Ireland

    Saurabh Shukla

  2. Department of Computer and Information Sciences, CERDAS, Universiti Teknologi PETRONAS (UTP), 32610, Seri Iskandar, Perak Darul Ridzuan, Malaysia

    Mohd. Fadzil Hassan

  3. Computing Fundamental Department, FPT University, Hanoi, Vietnam

    Duc Chung Tran

  4. Faculty of Information and Communication Technology, University Tunku Abdul Rahman, Kampar, Malaysia

    Rehan Akbar

  5. Informatics Department, Universitas Islam Indonesia, Yogyakarta, Indonesia

    Irving Vitra Paputungan

  6. College of Computing and Information Sciences, PAF Karachi Institute of Economics and Technology, Karachi, Pakistan

    Muhammad Khalid Khan

Authors
  1. Saurabh Shukla
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  2. Mohd. Fadzil Hassan
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  3. Duc Chung Tran
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  4. Rehan Akbar
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  5. Irving Vitra Paputungan
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  6. Muhammad Khalid Khan
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Contributions

SS, MFH, DCT: Conceptualization. SS, MFH, DCT: Formal analysis. SS, MFH, IVP: Investigation. MFH, MKK: Supervision. SS: Writing-original draft. SS, MFH, DCT, RA: Writing-review & editing.

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Correspondence to Saurabh Shukla.

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Conflict of interest

Saurabh Shukla, Mohd. Fadzil Hassan, Duc Chung Tran, Rehan Akbar, Irving Vitra Paputungan and Muhammad Khalid Khan declare that they have no competing interests.

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Shukla, S., Hassan, M.F., Tran, D.C. et al. Improving latency in Internet-of-Things and cloud computing for real-time data transmission: a systematic literature review (SLR). Cluster Comput 26, 2657–2680 (2023). https://doi.org/10.1007/s10586-021-03279-3

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  • DOI: https://doi.org/10.1007/s10586-021-03279-3

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