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1. REINFORCE: achieving efficient failure resiliency for network function virtualization based services

   https://doi.org/10.1145/3281411.3281441  

   Kulkarni, Sameer G ; Liu, Guyue ; Ramakrishnan, K. K. ; Arumaithurai, Mayutan ; Wood, Timothy ; Fu, Xiaoming ( January 2018 , CoNEXT '18 Proceedings of the 14th International Conference on emerging Networking EXperiments and Technologies)

   Ensuring high availability (HA) for software-based networks is a critical design feature that will help the adoption of software-based network functions (NFs) in production networks. It is important for NFs to avoid outages and maintain mission-critical operations. However, HA support for NFs on the critical data path can result in unacceptable performance degradation. We present REINFORCE, an integrated framework to support efficient resiliency for NFs and NF service chains. REINFORCE includes timely failure detection and consistent failover mechanisms. REINFORCE replicates state to standby NFs (local and remote) while enforcing correctness. It minimizes the number of state transfers by exploiting the concept of external synchrony, and leverages opportunistic batching and multi-buffering to optimize performance. Experimental results show that, even at line-rate packet processing (10 Gbps), REINFORCE achieves chain-level failover across servers in a LAN (or within the same node) within 10ms (100/μs), incurring less than 10% (1%) performance overhead, and adds average latency of only ~400/μs (5/μs), with a worst-case latency of less than 1ms (10/μs).
2. Exploitation of network-segregated CPU resources in CMS

   https://doi.org/10.1051/epjconf/202125102020  

   Acosta-Silva, C. ; Delgado Peris, A. ; Flix, J. ; Frey, J. ; Hernández, J.M. ; Yzquierdo, A. Pérez-Calero ; Tannenbaum, T. ( January 2021 , EPJ Web of Conferences)

   Biscarat, C. ; Campana, S. ; Hegner, B. ; Roiser, S. ; Rovelli, C.I. ; Stewart, G.A. (Ed.)

   CMS is tackling the exploitation of CPU resources at HPC centers where compute nodes do not have network connectivity to the Internet. Pilot agents and payload jobs need to interact with external services from the compute nodes: access to the application software (CernVM-FS) and conditions data (Frontier), management of input and output data files (data management services), and job management (HTCondor). Finding an alternative route to these services is challenging. Seamless integration in the CMS production system without causing any operational overhead is a key goal. The case of the Barcelona Supercomputing Center (BSC), in Spain, is particularly challenging, due to its especially restrictive network setup. We describe in this paper the solutions developed within CMS to overcome these restrictions, and integrate this resource in production. Singularity containers with application software releases are built and pre-placed in the HPC facility shared file system, together with conditions data files. HTCondor has been extended to relay communications between running pilot jobs and HTCondor daemons through the HPC shared file system. This operation mode also allows piping input and output data files through the HPC file system. Results, issues encountered during the integration process, and remaining concerns are discussed.
3. Catfish: adaptive RDMA-enabled R-tree for low latency and high throughput

   Mengbai Xiao, Hao Wang ( January 2019 , Proceedings of 39th IEEE International Conference on Distributed Computing Systems (ICDCS 2019))

   R-tree is a foundational data structure used in spatial databases and scientific databases. With the advancement of Internet and computer architectures, in-memory data processing for R-tree in distributed systems has become a common platform. We have observed new performance challenges to process R-tree as the amount of multidimensional datasets become increasingly huge. Specifically, an R-tree server can be heavily overloaded while the network and client CPU are lightly loaded, and vice versa. In this paper, we present the design and implementation of Catfish, an RDMA enabled R-tree for low latency and high throughput by adaptively utilizing the available network bandwidth and computing resources to balance the workloads between clients and servers. We design and implement two basic mechanisms of using RDMA for the client-server R-tree. First, in the fast messaging design, we use RDMA writes to send R-tree requests to the server and let server threads process R-tree requests to achieve low query latency. Second, in the RDMA offloading design, we use RDMA reads to offload tree traversal from the server to the client, which rescues the server as it is overloaded. We further develop an adaptive scheme to effectively switch an R-tree search between fast messaging and RDMA offloading,more »maximizing the overall performance. Our experiments show that the adaptive solution of Catfish on InfiniBand significantly outperforms R-tree that uses only fast messaging or only RDMA offloading in both latency and throughput. Catfish can also deliver up to one order of magnitude performance over the traditional schemes using TCP/IP on 1 Gbps and 40 Gbps Ethernet. We make a strong case to use RDMA to effectively balance workloads in distributed systems for low latency and high throughput.« less
4. Alignment of dynamic networks

   https://doi.org/10.1093/bioinformatics/btx246  

   Vijayan, V. ; Critchlow, D. ; Milenković, T. ( July 2017 , Bioinformatics)

   Network alignment (NA) aims to find a node mapping that conserves similar regions between compared networks. NA is applicable to many fields, including computational biology, where NA can guide the transfer of biological knowledge from well- to poorly-studied species across aligned network regions. Existing NA methods can only align static networks. However, most complex real-world systems evolve over time and should thus be modeled as dynamic networks. We hypothesize that aligning dynamic network representations of evolving systems will produce superior alignments compared to aligning the systems’ static network representations, as is currently done.

   For this purpose, we introduce the first ever dynamic NA method, DynaMAGNA ++. This proof-of-concept dynamic NA method is an extension of a state-of-the-art static NA method, MAGNA++. Even though both MAGNA++ and DynaMAGNA++ optimize edge as well as node conservation across the aligned networks, MAGNA++ conserves static edges and similarity between static node neighborhoods, while DynaMAGNA++ conserves dynamic edges (events) and similarity between evolving node neighborhoods. For this purpose, we introduce the first ever measure of dynamic edge conservation and rely on our recent measure of dynamic node conservation. Importantly, the two dynamic conservation measures can be optimized with any state-of-the-art NA method and not just MAGNA++.more »We confirm our hypothesis that dynamic NA is superior to static NA, on synthetic and real-world networks, in computational biology and social domains. DynaMAGNA++ is parallelized and has a user-friendly graphical interface.

   http://nd.edu/∼cone/DynaMAGNA++/.

   Supplementary data are available at Bioinformatics online.

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5. X-GOAL: Multiplex Heterogeneous Graph Prototypical Contrastive Learning

   https://doi.org/10.1145/3511808.3557490  

   Jing, Baoyu ; Feng, Shengyu ; Xiang, Yuejia ; Chen, Xi ; Chen, Yu ; Tong, Hanghang ( October 2022 , CIKM)

   Graphs are powerful representations for relations among objects, which have attracted plenty of attention in both academia and industry. A fundamental challenge for graph learning is how to train an effective Graph Neural Network (GNN) encoder without labels, which are expensive and time consuming to obtain. Contrastive Learning (CL) is one of the most popular paradigms to address this challenge, which trains GNNs by discriminating positive and negative node pairs. Despite the success of recent CL methods, there are still two under-explored problems. Firstly, how to reduce the semantic error introduced by random topology based data augmentations. Traditional CL defines positive and negative node pairs via the node-level topological proximity, which is solely based on the graph topology regardless of the semantic information of node attributes, and thus some semantically similar nodes could be wrongly treated as negative pairs. Secondly, how to effectively model the multiplexity of the real-world graphs, where nodes are connected by various relations and each relation could form a homogeneous graph layer. To solve these problems, we propose a novel multiplex heterogeneous graph prototypical contrastive leaning (X-GOAL) framework to extract node embeddings. X-GOAL is comprised of two components: the GOAL framework, which learns node embeddings formore »each homogeneous graph layer, and an alignment regularization, which jointly models different layers by aligning layer-specific node embeddings. Specifically, the GOAL framework captures the node-level information by a succinct graph transformation technique, and captures the cluster-level information by pulling nodes within the same semantic cluster closer in the embedding space. The alignment regularization aligns embeddings across layers at both node level and cluster level. We evaluate the proposed X-GOAL on a variety of real-world datasets and downstream tasks to demonstrate the effectiveness of the X-GOAL framework.« less