ArticlesAll Issue
ArticlesScalable Edge Computing for IoT and Multimedia Applications Using Machine Learning
• Mohammad Babar1, *, Muhammad Sohail Khan1, Usman Habib2, Babar Shah3, Farman Ali4, and Dongho Song5, *

Human-centric Computing and Information Sciences volume 11, Article number: 41 (2021)
https://doi.org/10.22967/HCIS.2021.11.041

Abstract

Keywords

Introduction

Future generation smart systems are expected to thrive through the Internet of Things (IoT). Interconnected smart IoT gadgets have expedited the evolution of smart cities, healthcare, transportation, and smart healthcare solutions [1]. However, these intelligent equipment’s are mainly resource-limited and unable to handle complex tasks. Cloud computing was assumed to be a resource-rich solution for smart devices. But, the inherent latencies within cloud computing make these devices impractical [2]. The applications of smart devices require real-time response and cannot afford excessive latencies. Edge computing has recently been introduced to integrate cloud services with smart devices [3].
Edge computing enfolds the characteristics of abundant bandwidth, reliability, and ultra-low latency, which increase the lifetime of IoT devices using computation offload facility [4]. Computation offloading is a process of migrating full or a portion of computationally complex task for distant processing that is very unlikely to handle by IoT. The edge server executes it, and the execution results are sent to the IoT devices [5, 6]. Computation offloading minimizes energy consumption, reduces latency, and strengthen the performance of IoT. Smart devices tend to generate huge data in a small interval of time. However, processing this large amount of data on a cloud platform consumes high energy, utilizes high bandwidth, and creates unnecessary congestion over back-haul links. Edge computing can work in close proximity of users, filter-out unnecessary traffic, and help to integrate cloud services with smart devices by taking timely decisions. Further, it can also protect the back-haul links resulting from congestion.

Fig. 1. A 3-tier edge computing framework.

Edge computing provides storage, computation, and other cloud services at the network edge. Edge based computing platform not only facilitates IoT devices with high bandwidth, and low latency, but also saves energy using computation offloading facility. However, offloading requests on large-scale create congestion over server resides at the edge, and originate the scalability issue [7]. To resolve the afore-mentioned issue, this article proposes a 3-tier edge computing framework using edge server, cloud, and IoT as presented in Fig.1. The designed framework efficiently addresses the scalability issue and manages the edge server resources. The following are the primary contributions of our research.
We developed a classical three-tier edge-cloud combination framework to simulate a realistic computation offloading scenario observing stringent energy and latency restraint for latency-critical tasks. The edge-cloud integration strengthen the edge performance by utilizing cloud resources in busy hours, that guards the edge server from congestion and further scales the edge server.
A large amount of IoT devices are utilized, and a SIoT clustering technique is implemented, which prioritize the DS tasks for offloading to the edge server, and facilitate DT tasks on the first-come first-serve (FCFS) basis. The scalability at both the IoT layer and edge layer is efficiently achieved using the SIoT clustering technique, which control the amount of offloading request and ensure to keep functioning the edge server smoothly.
The reminder of the article is structured as. Section 2 presents the relevant literature, where the major contributions of the published articles are highlighted. Section 3 discusses a 3-tier framework, which includes SIoT clustering, the edge orchestration and resource management, and a latency critical computation offloading algorithm. Section 4 include the results achieved and performance comparison with the state-of-the-art published articles. Finally, Section 5 concludes the research study with future research directions.

Related Work

Smart systems are still the center of attraction for researchers in academia and industry. The deployment of the smart systems is the dire need of the day. Applications pertaining to smart-systems require stringent QoS requirements, mainly ultra-low latency, crisp response, a smart network, an intelligent system, and first-line security. Nevertheless, traditional cloud architecture cannot meet their required QoS because the cloud is several hopes away from user premises, incurring long latency [8].
Edge computing restructures the entire technical landscape of internet of things. It corrects the flaws of these low-resource devices and encapsulates the features of proximity and energy efficiency. The proximity guarantees ultra-low latency and computation offloading leads to energy efficiency for the IoT device [4, 7]. Despite, edge computing is rapidly developing, but it cannot keep their cloud counterpart. When a user request on the large scale reached to edge server simultaneously, it forms congestion on the edge server and originates scalability issue. In [9], a smart health infrastructure using edge computing is designed to provide affordable and scalable amenities to patients.This existing work efficiently discovered and compressed the data, and extracted features for event detection to accomplish the objective of in-network context aware processing for smart health [10].
The computation offloading technique is discussed in the existing studies to resolve scalability in edge computing [15, 16]. Using this technique an entire application is sent for processing to a remote server. The edge system is scaled using these methods, nonetheless incurring additional latency, consume more power in the task preparation for offloading, and coordinate among different IoT devices. For computation offloading, a virtual machine (VM) migration strategy is suggested, that focuses the efficient exploitation of edge system’s available resources and regulates the workload on the edge system [17, 18]. The resource allocation and synchronization are streamlined by the proposed techniques but leads to the technicalities of distant execution control and context gathering [19, 20].

Edge-Cloud Integration Framework

In this section, the structure of the proposed framework is categorically divided into three different tiers. The first tier is about the IoT clustering, where the IoT devices are clustered. The second tier is the edge orchestration and resource management, which entertains the offloaded tasks. The third tier is a resource-rich cloud, which is capable of executing heterogeneous task and services. The flow chart of the 3-tier edge-cloud integration framework is shown in Fig. 2.

Fig. 2. Flow chart of the 3-tier edge-cloud integration framework.

Smart systems based on the IoT are made up of a huge number of devices, and searching of right device to provide the desired service is a difficult task. To deal with the service discovery problem, we have deployed SIoT clustering at IoT layer [52]. The SIoT initiates an association between these IoT devices on the basis of services, location, and ownership. This improve the service discovery in the IoT based systems because it provides the right service to the right device. However, the edge server is aware of SIoT paradigm and grouped these IoT into virtual clusters following their services, location, and ownership information. The devices inside the cluster sense and aggregates the data, and sends it to the cluster head for computation offloading. The SIoT and edge computing together produces a low latency architecture for resource-limited IoT devices.
The proposed algorithm works independently on IoT devices and edge server, and eases synchronization between them. The algorithm accompanying with request and admission control cycle makes every device independent. The SMP is responsible for task partitioning and offloading. The BS is responsible for providing the best channel. The edge server ensures the affective utilization of edge resources. This distribution reduces the signaling overhead of the offloading process, resulting in low latency communications. The hierarchical design of the proposed framework ensures efficient service discovery, work-load aggregation, and high-performance computing as a service, that further scales the edge server.

Edge Orchestration and Resource Management
The edge layer consists of BS, the edge server, and interconnecting cloud. The geographically distributed edge servers are placed hierarchically in the close proximity of the users, which are single hop away from the BS. The edge server in the close proximity brings down the cloud computing capabilities closer to the users. Its hierarchical structure enables edge computing tier to ensure efficient utilization of resources, aggregate the services, and protect the edge from the bottleneck in busy hours [53]. Alongside, edge offers low latency and enhances service visibility to interconnect geographically distributed, resource-limited, and heterogeneous IoT devices [36, 54, 55]. A traditional client-server networking architecture has been implemented, where smart mobile phone (SMP) works as a client and BS works as a server. The network-computation architecture enables the intercommunication model to reduce the communication overhead [37]. A holistic view of the computation offloading process is depicted in Fig. 3.

$Ω_{d,j} = \begin{cases} 1, & if(M_j < M_d )||(w_j<E_{d,j})\cr 0, & otherwise \end{cases}$(1)

where $Ω_{d,j},w_j,M_j,M_d$,and $E_{d,j}$ denote a decision parameter for computation offloading, the parameter of the time delay of the task $j$, the memory required by task $j$, the offered storage capacity on the device $d$, and job $j$ is the projected processing time on device d, respectively. When the edge e receives the offloaded job $j$, then the server executes it if free resources are available. However, if the server faces congestion, then the offloaded task is executed over cloud, as shown in Equation (2).

$Ω_{e,j} = \begin{cases} 1,& if(w_j<E_{e,j}+ Q_{e,j})\cr 0,& otherwise \end{cases}$(2)

where $E_{e,j}$ and $Q_{e,j}$ are the estimated execution time and the queueing delay of job $j$ on the edge e, respectively.

The proposed LACCoA completes the offloading process mentioned in Subsection 3.2. The proposed algorithm makes the offloading decision while satisfying the requirements of the task (i.e., minimizing the energy consumption of the servers and achieving low latency). In the proposed LACCoA scheme, we consider the latency, which is the sum of the transmission latency $L_{tran}$, Computation latency $L_{comp}$, and downloading latency $L_{down}$, as shown in Equation (3) [6265].

$L_{total} = L_{tran} + L_{comp} + L_{down}$(3)

where $L_{tran}$, and $L_{comp}$ are the time taken to prepare the task for offloading, execute the task over the remote server, and send back the execution result to the cluster head (CH), respectively. The LACCoA yielded the improved and realistic results. We have considered the $L_{downtime}$ though it has a marginal effect on the transmission latency because the output size produced is much smaller than input. Therefore, many studies have ignored it[57, 64, 65]. The transmission latency is shown in Equation (4).

$L_{tran} = \frac{D_n}{Wlog_2 (1+\frac{Pu*Los}{N})}$(4)

In the above equation, $D_n$ is the task size selected for offloading, $W$ is the bandwidth of the communication channel, Pu is the maximum transmission power, which is configured by the offloading device [25], Losis the channel gain, and Nis the Gaussian noise power. As per Equation (4), the CH can adjust its data rate from 0 to $Wlog_2⁡(1+\frac{Pu*Los}{N})$ by controlling its transmission power. The computation latency is the task execution time, as shown in Equations(5) and (6).

$L_{comp} = \frac{C_n}{C_e}$(5)

$L_{down} = t_{down}$(6)

where $C_e$ is the execution capacity of the server for the task $C_n$, $t_{down}$ is the time taken to receive the execution results by the offloading device called download latency $L_{down}$. The total latency incurred by a computational offloading process can be achieved by combining Equations(4)–(6), as shown in Equation (7). However, the output size produced after the task execution on the remote server is much smaller than the offloaded task. Though, it has a marginal effect on the total latency of the computation offloading process [58].

$L_{total}=\frac{D_n}{Wlog_2 (1+\frac{Pu*Los}{N})}+\frac{C_n}{C_e} + t_{down}$(7)

The total latency incurring by the offloaded task is expressed in Equation (7). However, the task can be either executed locally, or it can be offloaded to the edge server for execution. Therefore, the latency incurs locally or by remote execution can be represented as Equation (8).

$D_{offload} = \cases{1 \cr 0}$(8)

where $D_{offload}$ shows the offloading decision. If the $D_{offload}$ is 0, then the task will be executed locally. Hence, no $L_{down}$ is considered and the total latency incurs will be computed as $L_{tran}$ and $L_{comp}$. If the $D_{offload}$ is 1, then the task will be offloaded for computation, and the total latency incurs will be $L_{tran}$, $L_{comp}$, and $L_{down}$. The energy consumption is the second objective considered in the LACCoA algorithm, which can be calculated using Equation (9) [64].

$E_{total} = E_{tran} + E_{comp}$(9)

$E_{total}$ is the total energy consumed by a task, which is the combination of the energy consumed in the transmission task $E_{tranand}$ during execution task $E_{comp}$. The energies consumed in the task offloading $E_{tran}$, and the task on the remote server $E_{comp}$ are shown in Equations(10) and (11), respectively.

$E_{tran}=\frac{D_t*P_{up}}{R_{(t,s)}}$(10)

$E_{comp}=\frac{C_t*S_{comp}}{S_{cpu}}$(11)

where $D_t$ is the data size of the task, $P_{up}$ is the energy consumption to upload a task, $R_{(t,s)}$ is the rate of server for the task $t$, $C_t$ is the CPU cycles required for the task, and $S_{compis}$ the energy consumed per second by the Server $S$. The energy consumption during the task execution is expressed in Equation (8). The total energy consumed in the task execution is obtained by considering both the energy consumptions, as shown in Equation (12).

$E_{total}=\begin{cases} 1, &E_{tran} + E_{comp} \cr 0, &E_{comp} \end{cases}$(12)

The goal is to minimize both the latency and energy consumption simultaneously. Therefore, it is multi-objective optimization problem, which can be defined as follow:

$χ(Ω)=δ_a L_{total} + δ_b E_{total}$(13)

where $δ_a$ and $δ_b$ are scalar weights, which are utilized to make adjustment between the latency and energy consumption, and $δ_a, δ_b∈$ [0,1]. Here, the weighted sum approach is used, which combines the total energy and latency with varying values of $δ_a$ and $δ_b$. This composite objective function must be optimized. Therefore, it is formulated as follows.

$\displaystyle\min_{\rm Ω} ⁡χ(Ω) \\ s.t. Ω_n ∈ {0,1} ,∀_n ∈N$(14)

We used CE, which is a probabilistic optimization and learning technique. CE is a distance measure between two probabilities $j_{xand}$ $k_x$, as shown in the following equations.

$D(j||k)=H(j)-H(j,k)$(15)

where,

$H(j)= \sum j_x ln ⁡j_x$(16)

$H(j,k)= \sum j_x ln k_x$(17)

where $j_x$ is a distribution to find optimal solution, and $k_x$ is empirical distribution, which characterize the distribution of optimal solution. The CE was proposed for estimation of rare event problems; therefore, it can model as Bernoulli distribution given in the following equations.

$p(x,v)= \displaystyle∏_{m=1}^M (1 - v_m)^{(1-x)} v_m^{(x)}$(18)

vhas mean and variance. Minimum of χ(Ω) is denoted by Υ^*.

$Υ^*= \displaystyle\min_Ω χ(Ω)$(19)

The indicator functions${I_{χ(Ω)>Υ}}$ define various threshold level of Υ∈ Ɍ. A family of probability distribution functions $(pdfs){f(.;Ρ)}$ is used with the indicator function to randomize the problem. These pdfs are Gaussian distribution and linked with associated stochastic problem (ASP), as shown below.

$l_Υ= Ρ_q (χ(Ω)>Υ= E_q (I_{χ(Ω)>Υ})$(20)

where, $Ρ_u$ is a probability measure and $E_u$ is an expectation. In addition, the $l_Υ$ can be estimated using LR estimator defined as follow.

$v^*= \underset {v}{argmax} E_q (I_{χ(Ω)>Υ)} ln ⁡f(Ω;Ρ) 〗$(21)

Furthermore, it can be estimated by

$\hat{v^*}= \underset {v}{argmax} \frac{1}{L}\prod_{t=1}^L (I_{χ(Ω_t)>Υ}) ln ⁡f(Ω;Ρ)$(22)

where $Ω_t$ is generated from the pdf by the${f(.;Ρ)}$. The $\hat v_t$ and $\hat v_{t+1}$ cannot be calculated directly from Equation (23), but they can be updated using smoothed updating function.

$\hat v_{t+1}=τ v^*+(1- τ) \hat v_t$(23)

Here, parameter $τ$ is a learning rate. The value of $τ$ is a small number between 0 and 1. The algorithm based on CE method is shown in Algorithm 1. In general, this algorithm is coverage to an optimal solution [26].
Algorithm 1. Cross entropy based LACCoA algorithm
1: Initialization-Step:
2:   set t=0
3:   set ℵ, T         // N is the number of samples and T is number of Iterations
4:   End Initialization-Step
5:   While (t < T):
6:     Generate {$Ω_h^1, Ω_h^2,…,Ω_h^ℵ$} using {$f(.;Ρ)Ρ ∈ ζ$};// Draw population from {$f(.;Ρ)$}
7:     Compute {$χ(Ω_h^1), χ(Ω_h^2),…,χ(Ω_h^ℵ)$}; // Calculate /evaluate the objectives for the feasible samples generated in last step
8:     Sort {$χ(Ω_h^1), χ(Ω_h^2),…,χ(Ω_h^ℵ)$}; // Sort the Samples
9:     Select the minimum $χ(Ω_h^s)$ as a best-solution; // Select the minimum yielding objectives as elites of best solutions.
10:     update $\hat v_{t+1}$ using Equation (23)
11:     t=t+1
12: End While
13: Output $Ω_h$
The framework of edge-cloud integration is deliberately designed hierarchically over cloud, edge, and IoT layer. The hierarchical distribution ensures the efficient utilization of resources and distinguishes the responsibility of each layer. The scalability issue is addressed at both the IoT layer and edge layer. At the IoT layer, the DS task is offloaded on a priority basis, and the delay tolerant is aggregated and offloaded on FCFS basis. This tactic protects avoids congestion, and scales edge for maximum performance.

Results and Discussion

Table 1. Simulation parameters

S.no Parameters Value
1 Mobile device 0.5–2 GHz
2 Application Face recognition
3 Input task size 420 kB
4 Number of IoT devices 250–2,000
5 Communication parameters 3GPP specifications
6 Edge server 10 GHz
9 Latency requirement of task DS = 100 ms, DT = 150 ms

Fig. 5. Performance comparison of latency with different number of tasks.

As it can be seen from Fig. 6, the proposed framework saves more energy than their counterpart standard PSO, and adaptive PSO [37]. This is because of the LACCoA algorithm, which makes efficient utilization of both edge and cloud resources. Edge architecture in conjunction with cloud not only reduces the energy consumption, but also scales the edge server to handle more tasks without violating the latency requirement of the task. We noticed that the average energy consumed for the DT task is 0.73j, which saves 25% energy compared to the standard PSO. In addition, we also noticed that more energies are saved for resource-constrained IoT devices when the DT tasks are offloaded to the edge server for execution. However, the energy consumption for DS tasks is increased by 10% when the number of tasks reached to 45.

Fig. 6. Energy consumed when number of tasks varies.

This increase is due to resource scarcity in IoT devices. When the work load of edge server is increases, the DT tasks are offloaded to the cloud to ensure timely execution of DS task. This also prolongs the operation of the IoT. However, the proposed solution yielded the energy efficiency to some extent in processing of DS and DT task. Moreover, the energy intake upsurges as tasks exceeded to 50, as we cannot minimize energy and latency concurrently.

Fig. 7. Number of tasks execution using different computing resources.

Fig. 7 depicts the number of tasks executed using different computing resources. While considering 30 tasks, the standard PSO and adaptive PSO execute more tasks on the SMP than the LACCoA. Therefore, they are incurring more latencies and consuming more energy. However, the selective offloading strategy of LACCoA executed the least number of tasks on SMP, and made a good use of resourceful edge and cloud resources, which leads to save energy, reduce latency, and protect the edge server from the bottleneck. Our proposed 3-tier framework along with LACCoAalgorithm scales the edge server to entertain the maximum number of simultaneous tasks. However, the scalability of the edge computing is also dependent on the resource capacity of the edge server [6467].
As it can be seen in Fig. 8, the LACCoA algorithm performed well with different task size using a single edge server in the user’s proximity, and saves 34.5% energy compared to with MTMS method (27.74%) [68]. The SIoT-based clustering technique possesses prior knowledge of the location and services of the IoT devices, which reduces the communication overhead of IoT devices, and further minimizes the energy consumption.

Fig. 8. Total energy consumption over different task size.

Fig. 9. Energy and time saved over different battery levels of mobile devices.

The results in Fig. 9 describes the amount of energy and time saved under different battery levels of SMP devices using the proposed framework. A notable improvement is witnessed using the SIoT clustering technique that combines the services, devices, and interaction between IoT devices and edge server [52]. This association minimizes the overhead of intercommunication [69].
The above results show the performance improvement in terms of minimizing energy consumption, lowering down latency, and scaling the edge server to handle maximum numbers of offloading requests. However, the proposed framework pinpoints an abnormal case. The LACCoA algorithm sometimes selects a random channel, which finishes the execution task earlier than the offloading one. The LACCoA algorithm performs at the suboptimal level when the number of devices is changing rapidly. This effect increases the time taken for computation offloading decision in comparison to normal conditions, which are the challenging task of the proposed study.

Fig. 10. Comparison of average response time of DS task.

Conclusion

Acknowledgements

This research was a part of the project titled, Smart port IoT convergence and operation technology development (No. 20190399), funded by the Ministry of Ocean and Fisheries, Korea. This research work was also supported by the Research Incentive Grant R20129 of Zayed University, UAE.

Author’s Contributions

Conceptualization, MB. Funding acquisition, BS, DS. Investigation and methodology, MB, MSK, FA. Project administration, DS. Resources, MSK. Supervision, MSK, DS. Writing of the original draft, MB, FA. Writing of the review and editing, UH, FA. Software, MB. Validation, MSK. Formal analysis, BS. Visualization, MB.

Funding

Not applicable.

Competing Interests

The authors declare that they have no competing interests.

1st (Principal Author) and Corresponding Author

Mr. MohammadBabar hasreceivedan honor’s degree in Computer Science in 2004, following aMaster’sdegreeinDataTelecommunicationandNetworksfromUniversityofSalford, UK,in2010 and now, pursuing his PhD degree from University of Engineering and Technology Mardan. He remains “System Engineer” in National Database & Registration authority (NADRA), Pakistan.Afterword’s, join Comsats University Islamabad as a lecturer in Computer Science. Besides,serves IBM (ERRA), UNHCR and many national and international level organizations. His areaofinterestsiscloudcomputing,edge computing, FogandIoT.
E-mail: mbabarcs@gmail.com
Contact: 923133000099.

E-mail: Sohailkhan@uetmardan.edu.pk
Contact:923469358864

FARMAN ALI is an Assistant Professor in the Department of Software at Sejong University, South Korea. He received his B.S. degree in computer science from the University of Peshawar, Pakistan, in 2011, M.S. degree in computer science from Gyeongsang National University, South Korea, in 2015, and a Ph.D. degree in information and communication engineering from Inha University, South Korea, in 2018, where he worked as a Post-Doctoral Fellow at the UWB Wireless Communications Research Center from September 2018 to August 2019. His current research interests include sentiment analysis / opinion mining, information extraction, information retrieval, feature fusion, artificial intelligence in text mining, ontology-based recommendation systems, healthcare monitoring systems, deep learning-based data mining, fuzzy ontology, fuzzy logic, and type-2 fuzzy logic. He has registered over 4 patents and published more than 50 research articles in peer-reviewed international journals and conferences. He has been awarded with Outstanding Research Award (Excellence of Journal Publications-2017), and the President Choice of the Best Researcher Award during graduate program at Inha University.

Usman Habib is currently serving as an Associate Professor & head of Computer Science Department, FAST National University of Computers & Emerging Sciences (NUCES), Peshawar campus. Before joining FAST-NUCES, he has also served as an Assistant Professor at COMSATS Institute of Information Technology, Abbottabad, Pakistan. Along with teaching and research, he has also worked and successfully completed different industrial projects, such as project “eXtract” funded by the Austrian Funding Agency, under the funding programmee!MISSION. Dr. Usman has been actively involved in research as well, and has authored several conference and journal publications. He was also a member of organizing committee of a international conference, Frontiers of Information Technology (FIT) for several years (FIT’2006, FIT’2007, FIT’2010, FIT’2011, FIT’2012).

Dong Ho Song is a Professor at the Department of Computer Engineering, Korea Aerospace Univ. Seoul, Korea. His research interest is in the areas including cloud computing and distributed operating systems. He holds a Ph.D. in Computer Science from Univ. of Newcastle in England. He founded SoftonNet Inc. in 1999 and has been working on virtualization technologies. Over 10 years before founding the company, he had worked on development and application of cutting edge computing and information technologies in Stanford Research Institute (SRI) in USA as well as ETRI (Electronics and Telecommunications Research Institute) in Korea.

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Mohammad Babar1, *, Muhammad Sohail Khan1, Usman Habib2, Babar Shah3, Farman Ali4, and Dongho Song5, *, Scalable Edge Computing for IoT and Multimedia Applications Using Machine Learning, Article number: 11:41 (2021) Cite this article 2 Accesses