Mobile Cloud Business Process Management System for the Internet of Things

Chang, Chii; Srirama, Satish Narayana; Buyya, Rajkumar

doi:10.1145/3012000

Cited by 90 publications

(55 citation statements)

References 128 publications

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“…Given that the concept of IIoT systems is still evolving, complete integration and implementation of BDA processes in IIoT systems are unavailable yet [32,33]. Existing surveys on IIoT systems focus on concepts related to adoption of IIoTs [34,35], the integration of IIoTs and edge cloud computing systems [36], industrial marketplaces for IIoTs [4], big data and virtualisation technologies for IIoT systems [37], technological advancements relevant to CPS in IIoT systems [38], smart manufacturing [39] and big data applications for business operations [40][41][42]. We introduced the concept of the concentric computing model (CCM) for BDA in IIoT in our previous work [32] whereby we outlined the discussion on different layers of CCM and discussed the relevant research challenges that must be addressed to fully enable CCM for BDA in IIoT.…”

Section: Motivationmentioning

confidence: 99%

The role of big data analytics in industrial Internet of Things

Rehman

Yaqoob

Salah

et al. 2019

Future Generation Computer Systems

306

117

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Big data production in industrial Internet of Things (IIoT) is evident due to the massive deployment of sensors and Internet of Things (IoT) devices. However, big data processing is challenging due to limited computational, networking and storage resources at IoT device-end. Big data analytics (BDA) is expected to provide operational-and customer-level intelligence in IIoT systems. Although numerous studies on IIoT and BDA exist, only a few studies have explored the convergence of the two paradigms. In this study, we investigate the recent BDA technologies, algorithms and techniques that can lead to the development of intelligent IIoT systems. We devise a taxonomy by classifying and categorising the literature on the basis of important parameters (e.g. data sources, analytics tools, analytics techniques, requirements, industrial analytics applications and analytics types). We present the frameworks and case studies of the various enterprises that have benefited from BDA. We also enumerate the considerable opportunities introduced by BDA in IIoT. We identify and discuss the indispensable challenges that remain to be addressed as future research directions as well.

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Section: Motivationmentioning

confidence: 99%

The role of big data analytics in industrial Internet of Things

Rehman

Yaqoob

Salah

et al. 2019

Future Generation Computer Systems

306

117

View full text Add to dashboard Cite

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“…IoT services can often be choreographed through workflow or task graphs to assemble different IoT applications [119,40]. In some domains, the orchestration is supplied with a plethora of candidate devices with different geographical locations and attributes.…”

Section: Dynamismmentioning

confidence: 99%

Research challenges in nextgen service orchestration

Vaquero

Cuadrado

Elkhatib

et al. 2019

Future Generation Computer Systems

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Fog/edge computing, function as a service, and programmable infrastructures, like software-defined networking or network function virtualisation, are becoming ubiquitously used in modern Information Technology infrastructures. These technologies change the characteristics and capabilities of the underlying computational substrate where services run (e.g. higher volatility, scarcer computational power, or programmability). As a consequence, the nature of the services that can be run on them changes too (smaller codebases, more fragmented state, etc.). These changes bring new requirements for service orchestrators, which need to evolve so as to support new scenarios where a close interaction between service and infrastructure becomes essential to deliver a seamless user experience. Here, we present the challenges brought forward by this new breed of technologies and where current orchestration techniques stand with regards to the new challenges. We also present a set of promising technologies that can help tame this brave new world.

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“…Autonomous deployment solutions have been already proposed by means of business process orientation as pointed out the survey of Chang et al [11]. Approaches such as [10,12] focus mostly on the (deployment of) the execution workflow that the already deployed components must follow or are following based on a BPMN events representation.…”

Section: Related Workmentioning

confidence: 99%

“…In fact, solutions for such autonomous deployment have been already proposed in the realm of business process orientation [10,11]. Nevertheless, such approaches may render too heavyweight for being deployed on resource-constrained devices [7].…”

Section: Introductionmentioning

confidence: 99%

GoalD: A Goal-Driven deployment framework for dynamic and heterogeneous computing environments

Rodrigues

Guimarães²,

Rodrigues

et al. 2019

Information and Software Technology

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Context: Emerging paradigms like Internet of Things and Smart Cities utilize advanced sensing and communication infrastructures, where heterogeneity is an inherited feature. Applications targeting such environments require adaptability and context-sensitivity to uncertain availability and failures in resources and their ad-hoc networks. Such heterogeneity is often hard to predict, making the deployment process a challenging task. Objective: This paper proposes GoalD as a goal-driven framework to support autonomous deployment of heterogeneous computational resources to fulfill requirements, seen as goals, and their correlated components on one hand, and the variability space of the hosting computing and sensing environment on the other hand. Method: GoalD comprises an offline and an online stage to fulfill autonomous deployment by leveraging the use of goals. Deployment configuration strategies arise from the variability structure of the Contextual Goal Model as an underlying structure to guide autonomous planning by selecting available as well as suitable resources at runtime. Results: We evaluate GoalD on an existing exemplar from the selfadaptive systems community -the Tele Assistance Service provided by Weyns and Calinescu [1]. Furthermore, we evaluate the scalability of GoalD on a repository consisting of 430,500 artifacts. The evaluation results demonstrate the usefulness and scalability of GoalD in planning the deployment of a system with thousands of components in a few milliseconds.Corresponding author -Postal Address: Dept. de Ciência da Computação -Campus Darcy Ribeiro, Edifício CIC/EST, Asa Norte, Brasília -DF -Brasil, Postal Code: 70910-900. Conclusions:GoalD is a framework to systematically tackle autonomous deployment in highly heterogeneous computing environments, partially unknown at design-time following a goal-oriented approach to achieve the user goals in a target environment. GoalD has demonstrated itself able to scale for deployment planning dealing with thousands of components in a few milliseconds.

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Mobile Cloud Business Process Management System for the Internet of Things

Cited by 90 publications

References 128 publications

The role of big data analytics in industrial Internet of Things

The role of big data analytics in industrial Internet of Things

Research challenges in nextgen service orchestration

GoalD: A Goal-Driven deployment framework for dynamic and heterogeneous computing environments

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