STEP-ONE: Simulated testbed for Edge-Fog processes based on the Opportunistic Network Environment simulator

Mass, Jakob; Srirama, Satish Narayana; Chang, Chii

doi:10.1016/j.jss.2020.110587

Cited by 15 publications

(7 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…MobFogSim 37 is a simulator that supports mobility and VM/container migration. Mobility and BPM are combined in STEP‐ONE, 40 which supports modeling fog computing applications as business processes and the processes can be live migrated across fog/edge nodes. Gill and Singh, 41 in the process of providing a comprehensive review of fog simulators, categorized all the simulators that support mobility.…”

Section: Fog Computing Challenges and Solutionsmentioning

confidence: 99%

A decade of research in fog computing: Relevance, challenges, and future directions

Srirama

2023

Softw Pract Exp

Self Cite

View full text Add to dashboard Cite

Recent developments in the Internet of Things (IoT) and real‐time applications, have led to the unprecedented growth in the connected devices and their generated data. Traditionally, this sensor data is transferred and processed at the cloud, and the control signals are sent back to the relevant actuators, as part of the IoT applications. This cloud‐centric IoT model, resulted in increased latencies and network load, and compromised privacy. To address these problems, Fog Computing was coined by Cisco in 2012, a decade ago, which utilizes proximal computational resources for processing the sensor data. Ever since its proposal, fog computing has attracted significant attention and the research fraternity focused at addressing different challenges such as fog frameworks, simulators, resource management, placement strategies, quality of service aspects, fog economics and so forth. However, after a decade of research, we still do not see large‐scale deployments of public/private fog networks, which can be utilized in realizing interesting IoT applications. In the literature, we only see pilot case studies and small‐scale testbeds, and utilization of simulators for demonstrating scale of the specified models addressing the respective technical challenges. There are several reasons for this, and most importantly, fog computing did not present a clear business case for the companies and participating individuals yet. This article summarizes the technical, non‐functional, and economic challenges, which have been posing hurdles in adopting fog computing, by consolidating them across different clusters. The article also summarizes the relevant academic and industrial contributions in addressing these challenges and provides future research directions in realizing real‐time fog computing applications, also considering the emerging trends such as federated learning and quantum computing.

show abstract

Section: Fog Computing Challenges and Solutionsmentioning

confidence: 99%

A decade of research in fog computing: Relevance, challenges, and future directions

Srirama

2023

Softw Pract Exp

Self Cite

View full text Add to dashboard Cite

show abstract

“…To overcome the challenges, several authors address the role of process modeling in the context of the Fourth Industrial Revolution and emphasize the need to develop more robust, complete and flexible modeling languages that meet the dynamism of I4.0 (Seiger et al , 2015). Other authors have created extensions of current languages to model processes that use emerging technologies (Cheng et al , 2019) or have complemented process modeling with computer simulation (Gorecki et al , 2020; Mass et al , 2020).…”

Section: Process Modelingmentioning

confidence: 99%

Process modeling for smart factories: using science mapping to understand the strategic themes, main challenges and future trends

Sott

Furstenau

Kipper

et al. 2021

BPMJ

View full text Add to dashboard Cite

PurposeThe purpose of this paper is to identify the relationships between process modeling and Industry 4.0, the strategic themes and the most used process modeling language in smart factories. The study also presents the growth of the field of study worldwide, the perspectives, main challenges, trends and suggestions for future works.Design/methodology/approachTo do this, a science mapping was performed using the software SciMAT, supported by VOS viewer.FindingsThe results show that the Business Process Model and Notation (BPMN), Unified Modelling Language (UML) and Petri Net are the most relevant languages to smart manufacturing. The authors also highlighted the need to develop new languages or extensions capable of representing the dynamism, interoperability and multiple technologies of smart factories.Originality/valueIt was possible to identify the most used process modeling languages in smart environments and understand how these languages assist control and manage smart processes. Besides, the authors highlighted challenges, new perspectives and the need for future works in the field.

show abstract

“…It also has limitations in forming node clusters and augmenting microservice management techniques. Conversely, Mass et al [15] developed the STEP-ONE simulator to imitate the operations of Fogbased opportunistic network environments. STEP-ONE extends the conventional ONE simulator with advanced mobility and messaging interfaces and primarily focuses on modeling simple business processes.…”

Section: Related Workmentioning

confidence: 99%

“…All nodes but the proxy server within a block act as the gateway for the IoT devices. As a means of notations, proxy servers are specified as the tier-1 nodes [10] FogNetSim++ [11] IoTSim-Edge [12] MobFogSim [13] PureEdgeSim [14] STEP-ONE [15] YAFS [16] IoTNetSim [17] SatEdgeSim [18] ECSNeT++ [19] IoTSim-Osmosis [20] However, while parsing the location information of both Edge/Fog nodes and users/IoT devices, the DataParser class creates separate Location objects for each coordinate. The block-wise information of the respective entities (servers and mobile objects) can also be included in a Location object.…”

Section: Mobilitymentioning

confidence: 99%

“…Similarly, the heterogeneity in terms of resource architecture, communication standards, and operating principles predominantly exist among these nodes. Because of such constraints, the centralised Cloud-based resource provisioning and application placement techniques have compatibility issues with Edge/Fog computing environments and cannot be applied directly to regulate the respective services [5,6]. Identifying this potential research gap, a significant number of initiatives have been taken to develop efficient service management policies for Edge/Fog computing.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

IFogSim2: An Extended iFogSim Simulator for Mobility, Clustering, and Microservice Management in Edge and Fog Computing Environments

Mahmud¹,

Pallewatta²,

Goudarzi³

et al. 2021

Preprint

View full text Add to dashboard Cite

Internet of Things (IoT) has already proven to be the building block for next-generation Cyber-Physical Systems (CPSs). The considerable amount of data generated by the IoT devices needs latency-sensitive processing, which is not feasible by deploying the respective applications in remote Cloud datacentres. Edge/Fog computing, a promising extension of Cloud at the IoT-proximate network, can meet such requirements for smart CPSs. However, the structural and operational differences of Edge/Fog infrastructure resist employing Cloud-based service regulations directly to these environments. As a result, many research works have been recently conducted, focusing on efficient application and resource management in Edge/Fog computing environments. Scalable Edge/Fog infrastructure is a must to validate these policies, which is also challenging to accommodate in the real-world due to high cost and implementation time. Considering simulation as a key to this constraint, various software has been developed that can imitate the physical behaviour of Edge/Fog computing environments. Nevertheless, the existing simulators often fail to support advanced service management features because of their monolithic architecture, lack of actual dataset, and limited scope for a periodic update. To overcome these issues, we have developed multiple simulation models for service migration, dynamic distributed cluster formation, and microservice orchestration for Edge/Fog computing in this work and integrated with the existing iFogSim simulation toolkit for launching it as iFogSim2. The performance of iFogSim2 and its built-in policies are evaluated using three use case scenarios and compared with the contemporary simulators and benchmark policies under different settings. Results indicate that the proposed solution outperform others in service management time, network usage, ram consumption, and simulation time.

show abstract

STEP-ONE: Simulated testbed for Edge-Fog processes based on the Opportunistic Network Environment simulator

Cited by 15 publications

References 20 publications

A decade of research in fog computing: Relevance, challenges, and future directions

A decade of research in fog computing: Relevance, challenges, and future directions

Process modeling for smart factories: using science mapping to understand the strategic themes, main challenges and future trends

IFogSim2: An Extended iFogSim Simulator for Mobility, Clustering, and Microservice Management in Edge and Fog Computing Environments

Contact Info

Product

Resources

About