![]() ![]() Depending on the precedence among tenants or QoS constraints on service layer agreements (SLAs) between the tenants and infrastructure providers, slices may have different priorities. These users are called tenants, and multiple slices that serve the same type of applications can be owned by different tenants. Depending on the demands from users, slices can be dedicated to some specific users. Using SDN, slices can be constructed as virtual networks on the data plane, and an SDN controller connected to switches on the data plane can manage traffic flows of the slices in a centralized view. This flexible configuration can be implemented by software-defined networking (SDN), which is a mechanism that separates the control plane from the data plane in a network to manage the data plane flexibly. Since a slice concentrates on serving the same type of applications with a similar QoS requirement, the configuration or operation of the slice can be tailored to satisfy the QoS requirement. For example, one slice can be dedicated to providing reliable and low-latency communications to real-time interactive applications, while another slice is configured for providing high throughput communications for massive IoT applications. With the network slicing, each type of application can be served on a different slice. ![]() Network slicing enables a physical infrastructure to be sliced into logical networks (called slices), which have their own customized topologies and dedicated resources and perform network functions and management policies such as routing, resource scheduling, and admission control. However, it is highly complicated in general to satisfy the QoS requirements of individual applications such as smartphone, automobile, and massive internet-of-things (IoT) applications on single network infrastructure. In 5G networks, diverse applications with different quality of service (QoS) requirements are required to serve on the same infrastructure at the same time. Network slicing is one of the most important technologies that enable the upcoming fifth generation (5G) mobile networks. Simulation results indicate that the proposed slice management increased the average throughput of slices up to 6%, 13%, and 7% and reduced the average delay of slices up to 14%, 15%, and 11% in comparison with the NSM method. ![]() The simulations were conducted in grid network topologies with 16 and 100 nodes and a random network topology with 200 nodes. We conducted some simulations and the simulation results show that our proposed management scheme not only differentiates the QoS of slices according to their priorities but also enhances the average throughput and delay performance of slices remarkably compared to that of the NSM method. We compared the proposed slice management scheme with a naïve slice management (NSM) method that differentiates QoS among slices by priority queuing. As a result, the QoS of slices is differentiated according to their priorities while the interference imposed on slices is reduced. Since higher-priority slices have higher interference weights, they receive lower interference from other slices. In each lower-priority slice, the routing of traffic flows is conducted while minimizing a weighted summation of interference to other slices. Traffic flows in the slice with the highest priority are routed into shortest paths. In this paper, we propose a slice management scheme that mitigates the interference imposed on each slice according to their priorities by determining routes of flows with a different routing policy. However, in wireless networks, resource isolation may be challenging because the interference between links affects the actual bandwidths of slices and degrades their QoS. In wired networks, this resource isolation is enabled by allocating dedicated data bandwidths to slices. Generally, to provide the guaranteed QoS in applications, resources of slices are isolated. Network slicing is a technology that virtualizes a single infrastructure into multiple logical networks (called slices) where resources or virtualized functions can be flexibly configured by demands of applications to satisfy their quality of service (QoS) requirements.
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