Fragmentation and Optimal Deployment for IoT-Aware Business Process

Hou, Shoulu; Zhao, Shuai; Cheng, Bo; Cheng, Yongyang; Chen, Junliang

doi:10.1109/scc.2016.91

Cited by 12 publications

(6 citation statements)

References 16 publications

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“…BP fragmentation has been the subject of many studies shedding light on its rationales, techniques, challenges, benefits, and consequences. Hereafter, we briefly present the works of Cheikhrouhou et al [4] and Hou et al [6]. The first two carry out BP fragmentation over clouds/fogs and clouds, respectively, and the last one carries out BP fragmentation in the context of IoT.…”

Section: Types Of Placementsmentioning

confidence: 99%

An IoT Application Business-Model on Top of Cloud and Fog Nodes

Maamar

Al-Khafajiy

Dohan

2021

Advanced Information Networking and Applications

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This paper discusses the design of a business model dedicated for IoT applications that would be deployed on top of cloud and fog resources. This business model features 2 constructs, flow (specialized into data and collaboration) and placement (specialized into processing and storage). On the one hand, the flow construct is about who sends what and to whom, who collaborates with whom, and what restrictions exist on what to send, to whom to send, and with whom to collaborate. On the other hand, the placement construct is about what and how to fragment, where to store, and what restrictions exist on what and how to fragment, and where to store. The paper also discusses the development of a system built-upon a deep learning model that recommends how the different flows and placements should be formed. These recommendations consider the technical capabilities of cloud and fog resources as well as the networking topology connecting these resources to things.

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Section: Types Of Placementsmentioning

confidence: 99%

An IoT Application Business-Model on Top of Cloud and Fog Nodes

Maamar

Al-Khafajiy

Dohan

2021

Advanced Information Networking and Applications

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“…Complete list of surveyed papers, with scenario code and application classification Paper Application Scenario Paper Application Scenario [18] Smart agriculture 0000000 [19] Smart home 1032102A [20] Smart home 0000103 [21] Smart home 1101133 [22] Smart home 0011010 [23] Smart home 1132020 [24] Smart building 0020000 [25] Smart building 1132110 [26] Smart agriculture 0020000A [27] Smart home 1232120 [28] Water management 0021000 [29] Disaster management 1301210 [30] Smart home 0021000A [31] Smart vehicle 1332300 [32] Energy monitoring 0030000 [33] Playful furniture 2012010 [34] Energy monitoring 0031000 [35] Smart agriculture 2023200 [36] Smart home 0101003 [37] Smart city 2030103 [38] Playful furniture 0101123 [39] Smart city 2032013 [40] Smart healthcare 0130030 [41] Smart healthcare 2033020 [42] Smart healthcare 0131113 [43] Robot movement 2130033 [44] Learning device 0230023 [45] Smart healthcare 2133030 [46] Smart healthcare 0231020 [47] Smart building 3001200 [48] Smart healthcare 0312020 [49] Smart home 3022103 [50] Smart healthcare 0321210 [51] Smart city 3032100 [52] Smart healthcare 0322310 [53] Robot movement 3032200 [54] Smart healthcare 0331020 [55] Smart security 3032203 [56] Smart home 1002013...…”

Section: Table 13mentioning

confidence: 99%

An IoT sensor and scenario survey for data researchers

2019

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This work surveys Internet of Things (IoT) experimental research published since 2015. We summarize the IoT state of the art during the last 2 years and extract important data that we apply to enhance the analysis of IoT solutions. The IoT scenario presents a promising universe to data analysis. This raises a number of questions: which are its most popular applications? What is the definition of scale in an IoT application? What sensors are more often used and for what IoT applications? How can a researcher compare IoT scenarios? To investigate these concerns, this survey analyzes 2 years' worth of contributions made in three main scientific publishers. We focus on IoT experiments that were actually implemented using real equipment within one or more scenarios. Our first contribution is the classification of those IoT scenarios into seven main aspects. Each analyzed research study presents a specific configuration of a scenario's variables and sensors. Our second main contribution takes place after the scenario mapping phase. We identify as many as 19 common categories of data types in use. The interrelation among the scenarios and the data types from sensors should assist data researchers in understanding current IoT dynamics.

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“…These characteristics help identify the fragments along with the similarities that could exist between them. The aforementioned paragraphs discuss BP fragmentation from multiple perspectives for instance, why to fragment (rationale) 12 , how to fragment (techniques) 20,14,16,13,17 , how to identify fragments 18 , what to gain from fragmentation (benefits), and what could happen during fragmentation 15 . However, little interest is given to the concerns of BP owners who would like to achieve fragmentation (i) within a certain budget and/or a certain time frame based on what cloud providers would offer and (ii) in isolation from the competition so, that, the safety of their operations is ensured when their BPs are deployed on different clouds.…”

Section: Business-process Fragmentationmentioning

confidence: 99%

“…For the remaining steps of the fragmentation approach (i.e., announcement, pricing, allocation, and formation), BP designers and cloud providers use the Web application (www.redcad.org/projects/BPFragmentation/app) 13 to specify their needs and offers, respectively. First, the designer imports the specified BP, saved as an XML document by the plugin, into the Web application.…”

Section: System's Step-by-step Usementioning

confidence: 99%

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Restriction‐based fragmentation of business processes over the cloud

Kallel

Maamar

Sellami

et al. 2019

Concurrency and Computation

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Summary Despite the elasticity and pay‐per‐use benefits of cloud computing (aka fifth utility computing), organizations adopting clouds could be locked into single cloud providers, which is not always a “pleasant” experience when these providers stop operations. This is a serious concern for those organizations that who would like to deploy (core) business processes on the cloud along with tapping into these two benefits. To address the lock‐into concern, this paper proposes an approach for decomposing business processes into fragments that would run over multiple clouds and hence multiple providers. To develop fragments, the approach considers both restrictions over owners of business processes and potential competition among cloud providers. On the one hand, restrictions apply to each task in a business process and are specialized into budget to allocate, deadline to meet, and exclusivity to request. On the other hand, competition leads cloud providers to offer flexible pricing policies that would cater to the needs and requirements of each process owner. A policy handles certain clouds' properties referred to as limitedness, non‐renewability, and non‐shareability that impact the availability of cloud resources and hence the whole fragmentation. For instance, a non‐shareable resource could delay other processes should the current process do not release this resource on time. During fragmentation, interactions between owners of processes and providers of clouds happen according to two strategies referred to as global and partial. The former collects offers about cloud resources from all providers, while the latter collects such details from particular providers. To evaluate these strategies' pros and cons, a system implementing them, as well as demonstrating the technical feasibility of the fragmentation approach using credit‐application case study, is also presented in the paper. The system extends BPMN2‐modeler Eclipse plugin and supports interactions of processes' owners with clouds' providers that result to identifying the necessary fragments with focus on cost optimization.

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Fragmentation and Optimal Deployment for IoT-Aware Business Process

Cited by 12 publications

References 16 publications

An IoT Application Business-Model on Top of Cloud and Fog Nodes

An IoT Application Business-Model on Top of Cloud and Fog Nodes

An IoT sensor and scenario survey for data researchers

Restriction‐based fragmentation of business processes over the cloud

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