For beyond 5G systems, several key issues related to network slicing are being discussed in the 3rd Generation Partnership Project (3GPP). Among them, the issue of moving traffic from one slice to another slice with minimum service interruptions and quality of experience (QoE) degradations are important but yet to be resolved.To this end, we propose a network architecture where a slice steering function (SSF) is introduced to steer the traffic from one slice to another slice with minimum service interruptions by establishing a new session in the target network slice via which the ongoing traffic can be sent before releasing the session in the source network slice. Also, to minimize the QoE degradation, we propose an optimal scheme for selecting the target UE (i.e., UEs to be moved from one slice to other slices) and the target slice (i.e., TU-TTS), which minimizes the total degraded QoE of target UEs and the control plane signalling for steering by considering application types of UEs and by minimizing the number of target UEs that are to be moved, respectively.The simulation results are shown that TU-TTS reduces the normalized total degraded QoE and the normalized control plane signaling cost by 15.1-18.8% and 2.5-9.1%, respectively when compared to the conventional schemes.

Service function chaining (SFC) is an emerging technique that provides steering of traffic flows through an ordered set of service functions (SFs). In SFC, high availability is one of the most important issues to be addressed. SF instances within the chain can become unavailable when SF instances are overloaded or failed (e.g., power outage). Therefore, in order to realize highly available SFC, load balancing and fault management for SF instances must be provided. In this paper, we propose an overload and failure management (OFM) module in SFC that consists of the overload management (OM) module and the failure management (FM) module. In the OM module, when the current load at an SF instance exceeds a low-level threshold, a backup SF instance is prepared in advance. Meanwhile, if the current load further exceeds a high-level threshold, flow migration from the current SF instance to the backup SF instance is triggered. The FM module detects the failure of the SF instance by using a failure alarm. Upon detecting the failure, flow migration to the backup SF instance is triggered. We implement the OFM module in OpenDaylight (ODL) and present the experimental validation results.

In this paper, we consider the problem of optimal dynamic service function (SF) placement and flow routing in a SF chaining (SFC) enabled network. We formulate a multi-objective optimization problem to maximize the acceptable flow rate and to minimize the energy cost for multiple service chains. We transform the multi-objective optimization problem into a single-objective mixed integer linear programming (MILP) problem, and prove that the problem is NP-hard. We propose a polynomial time algorithm based on linear relaxation and rounding to approximate the optimal solution of the MILP. Extensive simulations are conducted to evaluate the effects of the energy budget, the network topology, and the amount of server resources on the acceptable flow rate. The results demonstrate that the proposed algorithm can achieve near-optimal performance and can significantly increase the acceptable flow rate and the service capacity compared to other algorithms under an energy cost budget.

Mobile collaborative community (MCC) is an emerging technology that allows multiple mobile nodes (MNs) to perform a resource intensive task, such as large content download, in a cooperative manner. In this paper, we introduce a proxy-based collaboration system for the MCC where a content proxy (CProxy) determines the amount of chunks and the sharing order scheduled to each MN, and the received chunks are shared among MNs via Wi-Fi Direct. We formulate a multi-objective optimization problem to minimize both the collaborative content download time and the energy consumption in an MCC, and propose a heuristic algorithm for solving the optimization problem. Extensive simulations are carried out to evaluate the effects of the number of MNs, the wireless bandwidth, the content size, and dynamic channel conditions on the content download time and the energy consumption. Our results demonstrate that the proposed algorithm can achieve near-optimal performance and significantly reduce the content download time and has an energy consumption comparable to that of other algorithms.

Mobile collaborative community (MCC) is an emerging technology where multiple mobile nodes (MNs) conduct a job (e.g., large content download) in a cooperative manner. In this paper, we consider a scenario in which multiple MNs form MCC for content download through wireless wide area network (WWAN) and share of the downloaded content through wireless local area network (WLAN). In the collaborative content download for MCC, the content chunk size assigned to an MN and the sharing order of the received chunk should be carefully determined to reduce the content download time. Therefore, we formulate an optimization problem that jointly considers the chunk size and the sharing order to minimize the content download time. Specifically, the optimization problem is formulated as a mixed integer non-linear programming (MINLP) problem that is known as NP-hard. The original optimization problem is relaxed into a linear programming (LP) problem and a heuristic algorithm minimizing the content download time and operating in a polynomial time is proposed based on the 2-opt algorithm. Simulation results demonstrate that the proposed algorithm can achieve near-optimal performance to the MINLP optimal solution and can reduce the content download time compared with other algorithms by choosing proper chunk size and sharing order.