Containerization has been used in many applications for isolation purposes due to its lightweight, scalable and highly portable properties. However, to apply containerization in largescale Internet data centers faces a big challenge. Services in data centers are always instantiated as a group of containers, which often generate heavy communication workloads and therefore resulting in inefficient communications and downgraded service performance. Although assigning the containers of the same service to the same server can reduce the communication overhead, this may cause heavily imbalanced resource utilization since containers of the same service are usually intensive to the same resource. To reduce communication cost as well as balance the resource utilization in large-scale data centers, we further explore the container distribution issues in a real industrial environment and find that such conflict lies in two phases – Container Placement and Container Reassignment. The objective of this paper is to address the container distribution problem in these two phases. For the container placement problem, we propose an efficient Communication Aware Worst Fit Decreasing (CAWFD) algorithm to place a set of new containers into data centers. For the container reassignment problem, we propose a two-stage algorithm called Sweep&Search to optimize a given initial distribution of containers by migrating containers among servers. We implement the proposed algorithms in Baidu’s data centers and conduct extensive evaluations. Compared with the state-of-the-art strategies, the evaluation results show that our algorithms perform better up to 70% and increase the overall service throughput up to 90% simultaneously.

Federated learning (FL) strikes a balance between privacy preservation and collaborative model training. However, the periodic transmission of model updates or parameters from each client to the federated server incurs substantial communication overhead, especially for participants with limited network bandwidth. This overhead significantly hampers the practical applicability of FL in real-world scenarios. To address this challenge, we propose FedSparse, an innovative sparse communication framework designed to enhance communication efficiency. The core idea behind FedSparse is to introduce a communication overhead regularization term into the client’s objective function, thereby reducing the number of parameters that need to be transmitted. FedSparse incorporates a Resource Optimization Proximal (ROP) term and an Importance-based Regularization Weighting (IRW) mechanism into the client update objective function. The local update process optimizes both the empirical risk and communication overhead by applying a sparse regularization weighted by update importance. By making minimal modifications to traditional FL approaches, FedSparse effectively reduces the number of parameters transmitted, thereby decreasing the communication overhead. We evaluate the effectiveness of FedSparse through experiments on various datasets under non-independent and identically distributed (non-IID) conditions, demonstrating its flexibility in resource-constrained environments. On the MNIST, Fashion-MNIST, and CIFAR datasets, FedSparse reduces the communication overhead by 24%, 17%, and 5%, respectively, compared to the baseline algorithm, while maintaining similar model performance. Additionally, on simulated non-IID datasets, FedSparse achieves a 6% to 8% reduction in communication resource consumption. By adjusting the sparsity intensity hyperparameter, we demonstrate that FedSparse can be tailored to different FL applications with varying communication resource constraints. Finally, ablation studies highlight the individual contributions of the ROP and IRW modules to the overall improvement in communication efficiency.

Many important cloud services require replicating massive data from one datacenter (DC) to multiple DCs. While the performance of pair-wise inter-DC data transfers has been much improved, prior solutions are insufficient to optimize bulk-data multicast, as they fail to explore the rich inter-DC overlay paths that exist in geo-distributed DCs, as well as the remaining bandwidth reserved for online traffic under fixed bandwidth separation scheme. To take advantage of these opportunities, we present BDS+ , a near-optimal network system for large-scale inter-DC data replication. BDS+ is an application-level multicast overlay network with a fully centralized architecture, allowing a central controller to maintain an up-to-date global view of data delivery status of intermediate servers, in order to fully utilize the available overlay paths. Furthermore, in each overlay path, it leverages dynamic bandwidth separation to make use of the remaining available bandwidth reserved for online traffic. By constantly estimating online traffic demand and rescheduling bulk-data transfers accordingly, BDS+ can further speed up the massive data multicast. Through a pilot deployment in one of the largest online service providers and large-scale real-trace simulations, we show that BDS+ can achieve 3- $5\times $ speedup over the provider’s existing system and several well-known overlay routing baselines of static bandwidth separation. Moreover, dynamic bandwidth separation can further reduce the completion time of bulk data transfers by 1.2 to 1.3 times.

Containers have been used in many applications for isolation purposes due to the lightweight, scalable and highly portable properties. However, to apply containers in virtual network functions (VNFs) faces a big challenge because high-performance VNFs often generate frequent communication workloads among containers while the container communications are generally not efficient. Compared with hardware modification solutions, properly distributing containers among hosts is an efficient and low-cost way to reduce communication overhead. However, we observe that this approach yields a trade-off between the communication overhead and the overall throughput of the cluster. In this paper, we focus on the communication-aware container redistribution problem to optimize the communication overhead and the overall throughput jointly for VNF clusters. We propose a solution called FreeContainer which utilizes a novel two-stage algorithm to re-distribute containers among hosts. We implement FreeContainer in Baidu clusters with 6000 servers and 35 services deployed. Extensive experiments on real networks are conducted to evaluate the performance of the proposed approach. The results show that FreeContainer can increase the overall throughput up to 90% with significant reduction on communication overhead.

With the explosive growth of mobile devices at the edge of mobile social networks (MSNs), the amount of the content that needs to be transmitted is exploded. Traditional content delivery mechanisms leverage only local information to make routing decisions, which results in both high latency and low delivery rate. Software-defined networking (SDN) is a novel network paradigm, the design philosophy of which could be applied to MSN for improving the content delivery performance. In this article, the centralized control thought of SDN is introduced into MSN to efficiently process social information. The classical routing algorithm of BubbleRap is improved from the perspective of network density, which is the basis of designing the sparse and dense routing mechanisms for MSNs. In addition, flexibly switching between these two routing mechanisms is implemented by a discriminating scheme, achieving efficient yet adaptive routing. The experimental results show that the delivery ratio of sparse routing is up to 83%, and the dense routing could reach up to 93%.