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Large community of high-energy physicists share their data all around world making it necessary to ship a large number of files over wide- area networks. Regional disk caches such as the Southern California Petabyte Scale Cache have been deployed to reduce the data access latency. We observe that about 94% of the requested data volume were served from this cache, without remote transfers, between Sep. 2022 and July 2023. In this paper, we show the predictability of the resource utilization by exploring the trends of recent cache usage. The time series based prediction is made with a machine learning approach and the prediction errors are small relative to the variation in the input data. This work would help understanding the characteristics of the resource utilization and plan for additional deployments of caches in the future.

Data-intensive sciences are becoming increasingly important for modern sciences. The transport control plane (TC-Plane) of the networks supporting data-intensive sciences can be important to achieve efficient and controlled transport of data for data-intensive sciences. In this paper, we analyze FTS, which is the de facto TC-Plane of the largest data-intensive network, revealing both efficiency and resource control issues of the current design. We then present the design and initial evaluation of Transport Control Networking (TCN), a design that is based on FTS but introduces (1) network-application co-design/coordination, which uses ALTO to realize network-wide resource control, and (2) a general, efficient, flexible optimization framework for TC-Plane, which allows both zero-order and first-order (e.g., bottleneck structure) gradient-based algorithms. We also discuss future work to engage the broad networking and data-intensive sciences communities.

The Energy Sciences Network (ESnet) is the high-performance network user facility for the US Department of Energy (DOE) Office of Science (SC) and delivers highly reliable data transport capabilities optimized for the requirements of data-intensive science. In essence, ESnet is the circulatory system that enables the DOE science mission by connecting all of its laboratories and facilities in the US and abroad. ESnet is funded and stewarded by the Advanced Scientific Computing Research (ASCR) program and managed and operated by the Scientific Networking Division at Lawrence Berkeley National Laboratory (LBNL). ESnet is widely regarded as a global leader in the research and education networking community.Throughout 2021, ESnet and the Office of Fusion Energy Sciences (FES) of the DOE SC organized an ESnet requirements review of FES-supported activities. Preparation for these events included identification of key stakeholders: program and facility management, research groups, and technology providers. Each stakeholder group was asked to prepare formal case study documents about their relationship to the FES program to build a complete understanding of the current, near-term, and long-term status, expectations, and processes that will support the science going forward.

ESnet provides a platform for moving large data sets and accelerating worldwide scientific collaboration. It provides high-bandwidth, reliable connections that link scientists at national laboratories, universities and other research institutions, enabling them to collaborate on some of the world's most important scientific challenges including renewable energy sources, climate science, and the origins of the universe. ESnet has embarked on a major project to provide substantial visibility into the inner-workings of the network by aggregating diverse data sources, exposing them via web services, and visualizing them with user-centered interfaces. The portal’s strategy is driven by understanding the needs and requirements of ESnet’s user community and carefully providing interfaces to the data to meet those needs. The 'MyESnet Portal ' allows users to monitor, troubleshoot, and understand the real time operations of the network and its associated services. This paper will describe the MyESnet portal and the process of developing it. The data for the portal comes from a wide variety of sources: homegrown systems, commercial products, and even peer networks. Some visualizations from the portal are presented highlighting some interesting and unusual cases such as power consumption and flow data. Developing effective user interfaces is an iterative process. When a new feature is released, users are both interviewed and observed using the site. From this process valuable insights were found concerning what is important to the users and other features and services they may also want. Open source tools were used to build the portal and the pros and cons of these tools are discussed

This study explores how fallout from the changing public health policy around COVID-19 has changed how researchers access and process their science experiments. Using a combination of techniques from statistical analysis and machine learning, we conduct a retrospective analysis of historical network data for a period around the stay-At-home orders that took place in March 2020. Our analysis takes data from the entire ESnet infrastructure to explore DOE high-performance computing (HPC) resources at OLCF, ALCF, and NERSC, as well as User sites such as PNNL and JLAB. We look at detecting and quantifying changes in site activity using a combination of t-Distributed Stochastic Neighbor Embedding (t-SNE) and decision tree analysis. Our findings bring insights into the working patterns and impact on data volume movements, particularly during late-night hours and weekends.

The volume of data moving through a network increases with new scientific experiments and simulations. Network bandwidth requirements also increase proportionally to deliver data within a certain time frame. We observe that a significant portion of the popular dataset is transferred multiple times to different users as well as to the same user for various reasons. In-network data caching for the shared data has shown to reduce the redundant data transfers and consequently save network traffic volume. In addition, overall application performance is expected to improve with in-network caching because access to the locally cached data results in lower latency. This paper shows how much data was shared over the study period, how much network traffic volume was consequently saved, and how much the temporary in-network caching increased the scientific application performance. It also analyzes data access patterns in applications and the impacts of caching nodes on the regional data repository. From the results, we observed that the network bandwidth demand was reduced by nearly a factor of 3 over the study period.

Over the past 50 years, the development of information and communications technology has provided unprecedented support to the steady economic growth of developed countries. In recent years, some of the largest growth has been reported in emerging economies, which, however, often lack adequate telecommunications infrastructure to further sustain their development. Although a number of service providers and system vendors have started to address the issue, the challenges they encounter are substantially different from those in the developed world, including an unreliable electricity grid, poor fiber infrastructure, low revenue expectations, and often a harsh climate environment. This paper reports use cases and solutions pertinent to the development of the networking infrastructure in emerging economies, provided by organizations directly involved in such activities. After providing some background information on the current state of network infrastructure and the main challenges for Africa and rural China, the paper provides details on two proposed solutions. The first focuses on the provisioning of services and network infrastructure through the development of low-cost data centers, whereas the second proposes cost-effective adaptation of both fiber and hybrid copper-fiber technology to rural areas. The article is concluded with a brief discussion on the complementarity of the two approaches.

Large community of high-energy physicists share their data all around world making it necessary to ship a large number of files over wide- area networks. Regional disk caches such as the Southern California Petabyte Scale Cache have been deployed to reduce the data access latency. We observe that about 94% of the requested data volume were served from this cache, without remote transfers, between Sep. 2022 and July 2023. In this paper, we show the predictability of the resource utilization by exploring the trends of recent cache usage. The time series based prediction is made with a machine learning approach and the prediction errors are small relative to the variation in the input data. This work would help understanding the characteristics of the resource utilization and plan for additional deployments of caches in the future.

We present one of the first case studies demonstrating the use of distributed acoustic sensing deployed on regional unlit fiber-optic telecommunication infrastructure (dark fiber) for broadband seismic monitoring of both near-surface soil properties and earthquake seismology. We recorded 7 months of passive seismic data on a 27 km section of dark fiber stretching from West Sacramento, CA to Woodland, CA, densely sampled at 2 m spacing. This dataset was processed to extract surface wave velocity information using ambient noise interferometry techniques; the resulting VS profiles were used to map both shallow structural profiles and groundwater depth, thus demonstrating that basin-scale variations in hydrological state could be resolved using this technique. The same array was utilized for detection of regional and teleseismic earthquakes and evaluated for long period response using records from the M8.1 Chiapas, Mexico 2017, Sep 8th event. The combination of these two sets of observations conclusively demonstrates that regionally extensive fiber-optic networks can effectively be utilized for a host of geoscience observation tasks at a combination of scale and resolution previously inaccessible.

Globally distributed scientific experiments involve movement of massive data volumes and many collaborators performing distributed data analysis. With complex workloads and heterogeneous resources, each user may desire certain behavior characteristics for their network paths. In this paper, we present the iNDIRA tool, which interacts with SDN north-bound interfaces to enable intent-based networking. It provides reliable, simple, and technology-agnostic communication between users and networks. Focusing particularly on science applications, iNDIRA uses natural language processing to construct semantic RDF graphs to understand, interact, and create the required network services. The technical challenges addressed by iNDIRA are: (1) development of a high-level descriptive language to query network-application requirements, (2) provides keyword identification and condition checking based on user profiles and topology details, (3) allows user negotiation based on the current network state, and (4) integrates network provisioning and service tools used by the application. iNDIRA is implemented on the ESnet network, where it interacts with OpenNSA (aka the NSI client) and Globus data transfer tools, to build complex cross-domain network paths for heterogeneous science applications, and perform secure data transfer. We argue that iNDIRA's approach presents users with an alternative approach to interact and communicate their network demands, allowing seamless network service integration.

The Software-defined network for End-to-end Networked Science at Exascale (SENSE) research project is building smart network services to accelerate scientific discovery in the era of 'big data' driven by Exascale, cloud computing, machine learning and AI. The project's architecture, models, and demonstrated prototype define the mechanisms needed to dynamically build end-to-end virtual guaranteed networks across administrative domains, with no manual intervention. In addition, a highly intuitive 'intent' based interface, as defined by the project, allows applications to express their high-level service requirements, and an intelligent, scalable model-based software orchestrator converts that intent into appropriate network services, configured across multiple types of devices. The significance of these capabilities is the ability for science applications to manage the network as a first-class schedulable resource akin to instruments, compute, and storage, to enable well defined and highly tuned complex workflows that require close coupling of resources spread across a vast geographic footprint such as those used in science domains like high-energy physics and basic energy sciences.

In this paper, we discuss building blocks that enable the exploitation of optical capacities beyond 100 Gb?s. Optical networks will benefit from more flexibility and agility in their network elements, especially from coherent transceivers. To achieve capacities of 400 Gb?s and more, coherent transceivers will operate at higher symbol rates. This will be made possible with higher bandwidth components using new electro-optic technologies implemented with indium phosphide and silicon photonics. Digital signal processing will benefit from new algorithms. Multi-dimensional modulation, of which some formats are already in existence in current flexible coherent transceivers, will provide improved tolerance to noise and fiber nonlinearities. Constellation shaping will further improve these tolerances while allowing a finer granularity in the selection of capacity. Frequency-division multiplexing will also provide improved tolerance to the nonlinear characteristics of fibers. Algorithms with reduced computation complexity will allow the implementation, at speeds, of direct pre-compensation of nonlinear propagation effects. Advancement in forward error correction will shrink the performance gap with Shannon's limit. At the network control and management level, new tools are being developed to achieve a more efficient utilization of networks. This will also allow for network virtualization, orchestration, and management. Finally, FlexEthernet and FlexOTN will be put in place to allow network operators to optimize capacity in their optical transport networks without manual changes to the client hardware.

Service providers and vendors are moving toward a network virtualized core, whereby multiple applications would be treated on their own merit in programmable hardware. Such a network would have the advantage of being customized for user requirements and allow provisioning of next generation services that are built specifically to meet user needs. In this article, we articulate the impact of network virtualization on networks that provide customized services and how a provider's business can grow with network virtualization. We outline a decision map that allows mapping of applications with technology that is supported in network-virtualization - oriented equipment. Analogies to the world of virtual machines and generic virtualization show that hardware supporting network virtualization will facilitate new customer needs while optimizing the provider network from the cost and performance perspectives. A key conclusion of the article is that growth would yield sizable revenue when providers plan ahead in terms of supporting network-virtualization-oriented technology in their networks. To be precise, providers have to incorporate into their growth plans network elements capable of new service deployments while protecting network neutrality. A simulation study validates our NV-induced model.

The Computing Models of the LHC experiments continue to evolve from the simple hierarchical MONARC[2] model towards more agile models where data is exchanged among many Tier2 and Tier3 sites, relying on both large scale file transfers with strategic data placement, and an increased use of remote access to object collections with caching through CMS's AAA, ATLAS' FAX and ALICE's AliEn projects, for example. The challenges presented by expanding needs for CPU, storage and network capacity as well as rapid handling of large datasets of file and object collections have pointed the way towards future more agile pervasive models that make best use of highly distributed heterogeneous resources. In this paper, we explore the use of Named Data Networking (NDN), a new Internet architecture focusing on content rather than the location of the data collections. As NDN has shown considerable promise in another data intensive field, Climate Science, we discuss the similarities and differences between the Climate and HEP use cases, along with specific issues HEP faces and will face during LHC Run2 and beyond, which NDN could address.

Sensor and smart phone technologies present opportunities for data explosion, streaming and collecting from heterogeneous devices every second. Analyzing these large datasets can unlock multiple behaviors previously unknown, and help optimize approaches to city wide applications or societal use cases. However, collecting and handling of these massive datasets presents challenges in how to perform optimized online data analysis 'on-the-fly', as current approaches are often limited by capability, expense and resources. This presents a need for developing new methods for data management particularly using public clouds to minimize cost, network resources and on-demand availability. This paper presents an implementation of the lambda architecture design pattern to construct a data-handling backend on Amazon EC2, providing high throughput, dense and intense data demand delivered as services, minimizing the cost of the network maintenance. This paper combines ideas from database management, cost models, query management and cloud computing to present a general architecture that could be applied in any given scenario where affordable online data processing of Big Datasets is needed. The results are presented with a case study of processing router sensor data on the current ESnet network data as a working example of the approach. The results showcase a reduction in cost and argue benefits for performing online analysis and anomaly detection for sensor data.

ESnet provides a platform for moving large data sets and accelerating worldwide scientific collaboration. It provides high-bandwidth, reliable connections that link scientists at national laboratories, universities and other research institutions, enabling them to collaborate on some of the world's most important scientific challenges including renewable energy sources, climate science, and the origins of the universe. ESnet has embarked on a major project to provide substantial visibility into the inner-workings of the network by aggregating diverse data sources, exposing them via web services, and visualizing them with user-centered interfaces. The portal’s strategy is driven by understanding the needs and requirements of ESnet’s user community and carefully providing interfaces to the data to meet those needs. The 'MyESnet Portal ' allows users to monitor, troubleshoot, and understand the real time operations of the network and its associated services. This paper will describe the MyESnet portal and the process of developing it. The data for the portal comes from a wide variety of sources: homegrown systems, commercial products, and even peer networks. Some visualizations from the portal are presented highlighting some interesting and unusual cases such as power consumption and flow data. Developing effective user interfaces is an iterative process. When a new feature is released, users are both interviewed and observed using the site. From this process valuable insights were found concerning what is important to the users and other features and services they may also want. Open source tools were used to build the portal and the pros and cons of these tools are discussed