Multiobjective shortest path problem (MSPP) is one of the most critical issues in network optimization, aimed at identifying all efficient paths across conflicting objectives. Nowadays, existing methods face substantial bottlenecks in addressing the diverse preferences of decision makers and high spatiotemporal overhead caused by the calculation process, particularly in cases with large-scale networks. To overcome these obstacles, a generalized MSPP in large-scale networks is investigated with the aim of solving it with diverse preferences of decision makers satisfied and low spatiotemporal overhead. Toward this end, with a novel concept, the generalized dominance relation is introduced, and the generalized multiobjective shortest path algorithm via the generalized dynamic programming approach is developed. Moreover, the H-reducible technique is further employed to accelerate the convergence of the proposed algorithm. Additionally, several rigorous proofs are provided for the conclusions that all efficient paths could be found within a tolerable time by the developed algorithm and the algorithm could be implemented in a distributed manner under mild assumptions. Finally, numerous routing experiments are conducted on large-scale communication networks for demonstrating the effectiveness and competitiveness of our approach.

Cloud storage systems have commonly used erasure coding that encodes data in stripes of blocks as a low-cost redundancy method for data reliability. Relative to traditional erasure coding, wide-stripe erasure coding that increases the stripe size has been recently proposed and explored to achieve lower redundancy. We observe that wide-stripe erasure coding makes multi-block failures occur much more frequently than traditional erasure coding in cloud storage systems.However, how to efficiently repair multiple blocks in wide-stripe erasure-coded storage systems remains unexplored. The conventional multi-block repair method sends available blocks from surviving nodes to one single new node to repair all failed blocks in a centralized way, which may cause the new node to be the bottleneck; recent multi-block repair methods follow pipelined single-block repair methods and the former are simply built on the latter in an independent way, which may cause the surviving nodes with limited bandwidth to be bottlenecks.In this paper, we first analyze the effects of both centralized and independent ways on the multi-block repair and then propose HMBR, a hybrid multi-block repair mechanism that combines centralized and independent multi-block repairs to tradeoff the bandwidth bottlenecks caused by the new and surviving nodes, thus optimizing the multi-block repair performance. We further extend HMBR for hierarchical network topology and multi-node failures. We prototype HMBR and show via Amazon EC2 that the repair time of a multi-block failure can be reduced by up to 64.8% over state-of-the-art schemes.

Erasure coding is commonly used as a storage-efficient redundancy method for fault tolerance in cold storage. Recent studies have begun to explore the use of erasure coding in hot storage, which requires fast online recovery to preserve read performance. However, existing erasure-coded repair strategies cannot effectively handle frequent and rapidly-changing network congestions in hot storage clusters. In this paper, we present the notion of pivots, which refer to the storage nodes with sufficient available downlink and uplink bandwidths in a congested hot storage network. We propose PivotRepair, a pivot-based pipelined single-chunk repair technique that leverages pivots for enabling the fast construction of a pipelined repair tree that bypasses congested links. We further propose an adaptive scheduling strategy to improve full-node repair performance. We prototype PivotRepair and show that the repair time of a single-chunk repair and a full-node repair can be reduced by up to 71.27% and 16.50%, respectively, over state-of-the-art repair schemes.

The soaring mobile data traffic demands have spawned the innovative concept of mobile edge caching in ultra-dense next-generation networks, which mitigates their heavy traffic burden. We conceive cooperative content sharing between base stations (BSs) for improving the exploitation of the limited storage of a single edge cache. We formulate the cooperative caching problem as a partially observable Markov decision process (POMDP) based multi-agent decision problem, which jointly optimizes the costs of fetching contents from the local BS, from the nearby BSs and from the remote servers. To solve this problem, we devise a multi-agent actor-critic framework, where a communication module is introduced to extract and share the variability of the actions and observations of all BSs. To beneficially exploit the spatio-temporal differences of the content popularity, we harness a variational recurrent neural network (VRNN) for estimating the time-variant popularity distribution in each BS. Based on multi-agent deep reinforcement learning, we conceive a cooperative edge caching algorithm where the BSs operate cooperatively, since the distributed decision making of each agent depends on both the local and the global states. Our experiments conducted within a large scale cellular network having numerous BSs reveal that the proposed algorithm relying on the collaboration of BSs substantially improves the benefits of edge caches.

Explosive growth in video traffic volumes incurs a high percentage of redundancy in today's Internet, following the 80–20 rule. Fortunately, the advanced in-network cache is considered as an effective scheme for eliminating the repetitive traffic by caching the popular content in network nodes. Besides, the emerging software-defined networking (SDN) enables centralized control and management, as well as the collaboration between network devices and upper applications. Moreover, the Network Functions Virtualization is also developed to support for customized network functions, including caching and streaming. This inspires us to design an SDN-assisted multimedia streaming Video-on-Demand system, integrating in-network cache, to improve the quality of service. The designed architecture is capable of reducing the redundant traffic via the reusable duplications. In particular, it can achieve greater performance gains by deploying specific scheduling policy. We further propose a variable-length interval cache strategy for RTP streaming, which can realize the self-adaptive adjustment of the size of cached video segments based on their access patterns. Our goal is to efficiently utilize the limited storage resources and increase the cache hit ratio. We present the theoretical analysis to demonstrate the attainable performance of the proposed algorithm; furthermore, the integrated system design is implemented as a prototype to show its feasibility and applicability. Ultimately, emulation experiments are conducted to evaluate the achievable performance improvement more comprehensively.

Current typical video conferencing connection is bridged by a multipoint control unit (MCU), which may cause large delay and communication bottleneck for the whole system. With the development of network technology, a video conferencing system can be implemented based on software-defined networking (SDN), which makes the service controllable and improves the scalability and flexibility. Additionally, a video encoding method called scalable video coding (SVC) can also help. In this paper, we propose a video conferencing architecture based on SDN-enabled SVC multicasting, which discards the traditional Internet group management protocol (IGMP) and MCU. The system implements SVC multicast streaming to satisfy different device capabilities of various conference terminals. The SDN controller is responsible for dynamically managing and controlling the layers of a video stream when a conference member faces network congestion. Also, a conference manager is designed to facilitate the management of the conference members. Experimental results show that our system can not only provide a flexible and controllable video delivery, but also reduce the network usage while guaranteeing the quality of service (QoS) of video conferencing.