Capacity assignment in multiservice packet networks with soft maximum waiting time guarantees

Sharafeddine, Sanaa

doi:10.1016/j.jnca.2010.09.004

Cited by 2 publications

(2 citation statements)

References 28 publications

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“…Sharafeddine (2011) focuses on maintaining the quality of voice traffic by proposing softened quality guarantees with controlled tolerance to service degradation in order to substantially reduce required network capacity. A common approach for session degradation in case of congestion is based on maximization of utilities of all the flows.…”

Section: Resource Managementmentioning

confidence: 99%

Cross-layer QoE-driven admission control and resource allocation for adaptive multimedia services in LTE

Ivesic

Skorin‐Kapov

Matijašević

2014

Journal of Network and Computer Applications

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Section: Resource Managementmentioning

confidence: 99%

Cross-layer QoE-driven admission control and resource allocation for adaptive multimedia services in LTE

Ivesic

Skorin‐Kapov

Matijašević

2014

Journal of Network and Computer Applications

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“…There are also similarities to the capacity assignment problem [22,23], where network capacity is assigned to services within networks. We however to not focus on how capacities are divided, but rather on whether there is sufficient capacity at any point in time to provide the service.…”

Section: Related Workmentioning

confidence: 99%

Shared resource network-aware impact determination algorithms for service workflow deployment with partial cloud offloading

Moens

Turck

2015

Journal of Network and Computer Applications

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Some service providers offer services for which physical devices, terminals and servers are installed at the customer's site, e.g. in a hospital setting where physical terminals are installed in patient rooms and used for communication. These devices work together to provide the service, forming service workflows. Due to a recent trend where parts of the management system for such services is offloaded to cloud environments, such services can no longer be isolated in a private subnet, that is specifically dimensioned for its purpose. Part of the network flows must then pass over a larger section of the customer network, and the network load can increase as new services are added, or when services are upgraded. Because of this, it is important to determine whether there is sufficient network and server capacity in the customer network to add new service workflows before they are deployed, or before an existing service is upgraded, ensuring a sufficient level of quality can be guaranteed.In this article we focus on how the impact of service workflows can be determined, ensuring service workflows do not negatively impact each other's execution. In particular, we determine an impact analysis strategy to evaluate the degree to which a given set of service workflows can be guaranteed in a given network topology. As not all flows are continuously active, the approach is designed to support the sharing of network and server resources using a hierarchically specified resource sharing model. We then evaluate the quality of the resulting solutions using two use cases, and the execution speed of the designed algorithms. For the two evaluation use cases, we find that the developed hierarchical algorithm requires ±42% and ±52% less resources than an approach without resource sharing, without any workflow failures occurring during the simulations.

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Capacity assignment in multiservice packet networks with soft maximum waiting time guarantees

Cited by 2 publications

References 28 publications

Cross-layer QoE-driven admission control and resource allocation for adaptive multimedia services in LTE

Cross-layer QoE-driven admission control and resource allocation for adaptive multimedia services in LTE

Shared resource network-aware impact determination algorithms for service workflow deployment with partial cloud offloading

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