Workflow-Net Based Service Composition Using Mobile Edge Nodes

Ridhawi, Ismaeel Al; Kotb, Yehia; Ridhawi, Yousif Al

doi:10.1109/access.2017.2766068

Cited by 39 publications

(21 citation statements)

References 41 publications

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“…Workflows are an extension to petri-nets, and are comprised of places, transitions, tokens, and arcs. Details in regards to petri-net and workflow modeling are out of the scope of this article and can be found in [9]. The workflow composition process is dependent on a reinforcement learning approach that takes into consideration previous successful workflow adaptations.…”

Section: Solution Overviewmentioning

confidence: 99%

A Profitable and Energy-Efficient Cooperative Fog Solution for IoT Services

Ridhawi

Kotb

Aloqaily

et al. 2020

IEEE Trans. Ind. Inf.

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Fog-to-Fog (F2F) communication has been introduced to deliver services to clients with minimal reliance on the cloud through resource and capability sharing of cooperative fogs. Current solutions assume full cooperation among the fogs to deliver simple and composite services. Realistically, each fog might belong to a different network operator or service provider and thus will not participate in any form of collaboration unless self-monetary profit is incurred. In this paper, we introduce a fog collaboration approach for simple and complex multimedia service delivery to cloud subscribers while achieving shared profit gains for the cooperating fogs. The proposed work dynamically creates short-term service-level-agreements (SLA) offered to cloud subscribers for service delivery while maximizing user satisfaction and fog profit gains. The solution provides a learning mechanism that relies on online and offline simulation results to build guaranteed workflows for new service requests. The configuration parameters of the short-term SLAs are obtained using a modified tabu-based search mechanism that uses previous solutions when selecting new optimal choices. Performance evaluation results demonstrate significant gains in terms of service delivery success rate, service quality, reduced power consumption for fog and cloud datacenters, and increased fog profits.

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Section: Solution Overviewmentioning

confidence: 99%

A Profitable and Energy-Efficient Cooperative Fog Solution for IoT Services

Ridhawi

Kotb

Aloqaily

et al. 2020

IEEE Trans. Ind. Inf.

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“…In the current field of information technology [28]- [31], information security is an extremely significant aspect that needs to be considered, and the protection of privacy information is an elementary need of the masses [32]- [34]. Outsourcing data security is one of our main research directions at present.…”

Section: Related Workmentioning

confidence: 99%

Privacy-Protection Path Finding Supporting the Ranked Order on Encrypted Graph in Big Data Environment

Chen

Zhang

et al. 2020

IEEE Access

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Since data outsourcing in big data environment become popular and become a trend, large quantities of graph data are outsourced to the big data server for saving cost. As the big data server can not be fully reliable, people usually encrypt the graph data before they are outsourced to the big data platform for the privacy protection. The path finding is a frequently-used action and can be useful for production and living. The path finding supporting the ranked order is a more useful operation, and a user can obtain a ranked search result set. Because of the outsourced graph data being encrypted on the big data server, the path finding supporting the ranked order becomes a task with enough challenge. In this paper, we propose a solution to perform privacy-protection path finding supporting the ranked order on encrypted graph in big data environment (PPFR). Our research uses an encryption mechanism and a ranking strategy to achieve path finding supporting the ranked order. We formally analyze the security of our scheme. We demonstrate the efficiency of our proposed scheme on a real graph data set by experiment. INDEX TERMS path finding, encrypted graph, ranked order, searchable encryption, big data

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“…The second type of service composition assumes that the requested composite service is not available from the set of originally decomposed services and requires a more complex approach towards composition. The adopted solution is based on an earlier work introduced by Al Ridhawi et al The solution assumes that a user request is formulated as a set of service descriptions and the associated QoS levels such as delay, jitter, and cost. The values range from a minimum acceptable value to a maximum acceptable value.…”

Section: Service Compositionmentioning

confidence: 99%

“…The learning process relies on selecting candidate events, then calculating the probability of the events' occurrences, and then finally merging service tasks (ie, actions) into a composition plan (ie, workflow‐net). Details pertaining to the composition learning process are outlined in other works …”

Section: Service Compositionmentioning

confidence: 99%

A collaborative mobile edge computing and user solution for service composition in 5G systems

Ridhawi

Aloqaily

Kotb

et al. 2018

Trans Emerging Tel Tech

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Mobile edge computing (MEC) is an emergent technology that has revolutionized traditional cloud service solutions. Mobile edge computing extends cloud computing by providing processing, storage, and networking capabilities at the edge of the mobile network. Delay‐sensitive and context‐aware applications are able to execute within close proximity of mobile users. Additionally, today's cloud services are not tailored to user specifications, but rather diversified toward a group of users. To guarantee delivery of user‐specific services in 5G networks, service composition techniques should be incorporated. This article envisions a real‐time, context‐aware, service‐composition collaborative framework that lies at the edge of the network, comprising MEC and user devices for fast composite service delivery. The proposed solution decomposes cloud data into a set of files and services, which are then replicated to MEC nodes. Frequently requested files and services are further cached onto user mobile devices for faster access. Both MEC nodes and mobile users advertise their services onto the collaborative edge/user space, where services are delivered either composite or unrendered according to users' requests. Service composition is achieved through a learning‐based workflow‐net approach that relies on previous composition results to build service composition models to be used for new compositions. The presented solution provides guaranteed and fast delivery of the requested cloud composite services to end users while sustaining QoS requirements and load balancing among edge and mobile nodes.

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Workflow-Net Based Service Composition Using Mobile Edge Nodes

Cited by 39 publications

References 41 publications

A Profitable and Energy-Efficient Cooperative Fog Solution for IoT Services

A Profitable and Energy-Efficient Cooperative Fog Solution for IoT Services

Privacy-Protection Path Finding Supporting the Ranked Order on Encrypted Graph in Big Data Environment

A collaborative mobile edge computing and user solution for service composition in 5G systems

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