The Experts below are selected from a list of 37266 Experts worldwide ranked by ideXlab platform
Adlen Ksentini - One of the best experts on this subject based on the ideXlab platform.
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CDN Slicing over a Multi-Domain Edge Cloud
IEEE Transactions on Mobile Computing, 2026Co-Authors: Tarik Taleb, Ilias Benkacem, Pantelis A. Frangoudis, Adlen KsentiniAbstract:We present an architecture for the provision of video Content Delivery Network (CDN) functionality as a Service over a multi-domain cloud. We introduce the concept of a CDN slice, that is, a CDN Service Instance which is created upon a content provider's request, is autonomously managed, and spans multiple potentially heterogeneous edge cloud infrastructures. Our design is tailored to a 5G mobile network context, building on its inherent programmability, management flexibility, and the availability of cloud resources at the mobile edge level, thus close to end users. We exploit Network Functions Virtualization (NFV) and Multi-access Edge Computing (MEC) technologies, proposing a system which is aligned with the recent NFV and MEC standards. To deliver a Quality-of-Experience (QoE) optimized video Service, we derive empirical models of video QoE as a function of Service workload, which, coupled with multi-level Service monitoring, drive our slice resource allocation and elastic management mechanisms. These management schemes feature autonomic compute resource scaling, and on-the-fly transcoding to adapt video bit-rate to the current network conditions. Their effectiveness is demonstrated via testbed experiments.
Tarik Taleb - One of the best experts on this subject based on the ideXlab platform.
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CDN Slicing over a Multi-Domain Edge Cloud
IEEE Transactions on Mobile Computing, 2026Co-Authors: Tarik Taleb, Ilias Benkacem, Pantelis A. Frangoudis, Adlen KsentiniAbstract:We present an architecture for the provision of video Content Delivery Network (CDN) functionality as a Service over a multi-domain cloud. We introduce the concept of a CDN slice, that is, a CDN Service Instance which is created upon a content provider's request, is autonomously managed, and spans multiple potentially heterogeneous edge cloud infrastructures. Our design is tailored to a 5G mobile network context, building on its inherent programmability, management flexibility, and the availability of cloud resources at the mobile edge level, thus close to end users. We exploit Network Functions Virtualization (NFV) and Multi-access Edge Computing (MEC) technologies, proposing a system which is aligned with the recent NFV and MEC standards. To deliver a Quality-of-Experience (QoE) optimized video Service, we derive empirical models of video QoE as a function of Service workload, which, coupled with multi-level Service monitoring, drive our slice resource allocation and elastic management mechanisms. These management schemes feature autonomic compute resource scaling, and on-the-fly transcoding to adapt video bit-rate to the current network conditions. Their effectiveness is demonstrated via testbed experiments.
Bofeng Zhang - One of the best experts on this subject based on the ideXlab platform.
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towards the optimality of Service Instance selection in mobile edge computing
Knowledge Based Systems, 2021Co-Authors: Guobing Zou, Zhen Qin, Shuiguang Deng, Yanglan Gan, Bofeng ZhangAbstract:Abstract Mobile edge computing (MEC) has been proposed to significantly reduce the response time of Service invocations for end users. In MEC environment, a Service provider can create multiple Instances from a Service and deploy them to different hired edge servers, where the deployed Instances can be selected and invoked to decrease the network latency by nearby users. However, Service Instance selection in MEC is a challenging research problem from threefold aspects. First, the limitations of an edge server in terms of computation capacity and coverage range result in serving for only a certain number of users at the same time. Second, due to variable geographical locations from user mobility paths in MEC, the mobility of edge users is highly related to data transmission rate and affects the delay of Service invocations. Furthermore, when many users in an edge server covered region request the same Service Instance at the same time, they interfere with each other and may reduce the experience of Service invocations if there is no effective strategy to distribute these requests to appropriate Instances deployed on different edge servers. To improve the user experience on Service invocations with a lower response time, we take the above three factors into account and model the Service Instance selection problem (SISP) in MEC as an optimization problem, and propose a novel genetic algorithm-based approach with a response time-aware mutation operation with normalization for Service Instance selection called GASISMEC to find approximately optimal solution. Extensive experiments are conducted on two widely-used real-world datasets. The results demonstrate that our approach significantly outperforms the six baseline competing approaches.
Pantelis A. Frangoudis - One of the best experts on this subject based on the ideXlab platform.
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CDN Slicing over a Multi-Domain Edge Cloud
IEEE Transactions on Mobile Computing, 2026Co-Authors: Tarik Taleb, Ilias Benkacem, Pantelis A. Frangoudis, Adlen KsentiniAbstract:We present an architecture for the provision of video Content Delivery Network (CDN) functionality as a Service over a multi-domain cloud. We introduce the concept of a CDN slice, that is, a CDN Service Instance which is created upon a content provider's request, is autonomously managed, and spans multiple potentially heterogeneous edge cloud infrastructures. Our design is tailored to a 5G mobile network context, building on its inherent programmability, management flexibility, and the availability of cloud resources at the mobile edge level, thus close to end users. We exploit Network Functions Virtualization (NFV) and Multi-access Edge Computing (MEC) technologies, proposing a system which is aligned with the recent NFV and MEC standards. To deliver a Quality-of-Experience (QoE) optimized video Service, we derive empirical models of video QoE as a function of Service workload, which, coupled with multi-level Service monitoring, drive our slice resource allocation and elastic management mechanisms. These management schemes feature autonomic compute resource scaling, and on-the-fly transcoding to adapt video bit-rate to the current network conditions. Their effectiveness is demonstrated via testbed experiments.
Ilias Benkacem - One of the best experts on this subject based on the ideXlab platform.
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CDN Slicing over a Multi-Domain Edge Cloud
IEEE Transactions on Mobile Computing, 2026Co-Authors: Tarik Taleb, Ilias Benkacem, Pantelis A. Frangoudis, Adlen KsentiniAbstract:We present an architecture for the provision of video Content Delivery Network (CDN) functionality as a Service over a multi-domain cloud. We introduce the concept of a CDN slice, that is, a CDN Service Instance which is created upon a content provider's request, is autonomously managed, and spans multiple potentially heterogeneous edge cloud infrastructures. Our design is tailored to a 5G mobile network context, building on its inherent programmability, management flexibility, and the availability of cloud resources at the mobile edge level, thus close to end users. We exploit Network Functions Virtualization (NFV) and Multi-access Edge Computing (MEC) technologies, proposing a system which is aligned with the recent NFV and MEC standards. To deliver a Quality-of-Experience (QoE) optimized video Service, we derive empirical models of video QoE as a function of Service workload, which, coupled with multi-level Service monitoring, drive our slice resource allocation and elastic management mechanisms. These management schemes feature autonomic compute resource scaling, and on-the-fly transcoding to adapt video bit-rate to the current network conditions. Their effectiveness is demonstrated via testbed experiments.