In this work, we introduce PSMOA, a novel Policy Support Multi-objective Optimization Algorithm designed for decentralized data replication. PSMOA combines the Non-dominated Sorting Genetic Algorithm III (NSGA-III) with Entropy Weighted TOPSIS to dynamically assign weights to objectives based on system policies. The algorithm focuses on optimizing replication time, cost, popularity, and load balancing. Our simulations demonstrate that PSMOA outperforms NSGA-II and NSGA-III by achieving solutions with both lower replication costs and faster replication times, all while adhering to varying policy requirements. The results illustrate PSMOA's effectiveness in optimizing data replication within complex, multi-organizational environments.

Efficient data replication in decentralized storage systems must account for diverse policies, especially in multiorganizational, data-intensive environments. This work proposes PSMOA, a novel Policy Support Multi-objective Optimization Algorithm for decentralized data replication that dynamically adapts to varying organizational requirements such as minimization or maximization of replication time, storage cost, replication based on content popularity, and load balancing while respecting policy constraints. PSMOA outperforms NSGA-II and NSGAIII in both Generational Distance (20.29 vs 148.74 and 67.74) and Inverted Generational Distance (0.78 vs 3.76 and 5.61), indicating better convergence and solution distribution. These results validate PSMOA's novelty in optimizing data replication in multi-organizational environments.

Scientific data volume is growing in size, and as a direct result, the need for faster transfers is also increasing. The scientific community has sought to leverage parallel transfer methods using multi-threaded and multi-source download models to reduce download times. In multi-source transfers, a client downloads data from multiple replicated servers in parallel. Tools such as Aria2 and BitTorrent support such multi-source transfers and have shown improved transfer times.In this work, we introduce Multi-Source Data Transfer Protocol, MDTP, which further improves multi-source transfer performance. MDTP logically divides a file request into smaller chunk requests and distributes the chunk requests across multiple servers. Chunk sizes are adapted based on each server’s performance but selected in a way that ensures each round of requests completes around the same time. We formulate this chunk-size allocation problems as a variant of the bin-packing problem, where adaptive chunking efficiently fills the available capacity "bins" corresponding to each server.Our evaluation shows that MDTP reduces transfer times by 10–22% compared to Aria2, the fastest alternative. Comparisons with other protocols, such as static chunking and BitTorrent, demonstrate even greater improvements. Additionally, we show that MDTP distributes load proportionally across all available replicas, not just the fastest ones, which improves throughput. Finally, we show MDTP maintains high throughput even when latency increases or bandwidth to the fastest server decreases.

This paper introduces an alternate approach for encrypting video streams using attribute-based encryption (ABE), focusing on securing the data rather than the connection between streaming endpoints. This fundamental shift in the security model means we can now encrypt video segments once at the source and cache them anywhere reducing the computational load on intermediate caches by removing the need for encryption and decryption on a per-client basis. Additionally, restricting or revoking access can be done by revoking users' private keys rather than re-encrypting the entire video stream. Finally, our approach eliminates the need for decryption and encryption at the intermediate caches (currently required for TLS terminations) since an encrypted version of the content can be stored anywhere, reducing the load on the cache.

This work aims to enable efficient digital rights management (DRM) for volumetric video by introducing attribute-based selective coordinate encryption for point clouds. By encrypting only a subset of coordinates, our approach reduces computational overhead and latency while maintaining necessary security. Selective encryption ensures that point cloud frames—and, by extension, entire volumetric videos—are sufficiently obfuscated so that, while the content remains viewable, it appears highly distorted and visually unpleasant, preventing meaningful unauthorized viewing. We propose a flexible framework that allows varying the amount and type of co-ordinate encryption (e.g., X, Y, Z, or combinations), and we assess visual degradation using established point cloud quality metrics. Our results show that encrypting only X coordinates cuts encryption and decryption times by 37% and 46%, respectively, compared to full-frame encryption, while X and Y encryption achieves 20% and 36% reductions, both still significantly degrading visual quality. Leveraging Attribute-Based Encryption (ABE) further enables content to be securely cached and efficiently distributed in its protected form, eliminating the need for re-encryption, thereby reducing computational load and latency. While our current evaluation is limited to individual point cloud frames, future work will extend to entire volumetric video streams, including analysis of caching gains during streaming with ABE.

Inspired by prior work suggesting undetected errors were becoming a problem on the Internet, we set out to create a measurement system to detect errors that the TCP checksum missed. We designed a client-server framework in which the servers sent known files to clients. We then compared the received data with the original file to identify undetected errors introduced by the network. We deployed this measurement framework on various public testbeds. Over the course of 9 months, we transferred a total of 26 petabytes of data. Scaling the measurement framework to capture a large number of errors proved to be a challenge. This paper focuses on the challenges encountered during the deployment of the measurement system. We also present the interim results, which suggest that the error problems seen in prior works may be caused by two distinct processes: (1) errors that slip past TCP and (2) file system failures. The interim results also suggest that the measurement system needs to be adjusted to collect exabytes of measurement data, rather than the petabytes that prior studies predicted.

The Science Demilitarized Zone (Science DMZ) is a network environment optimized for scientific applications. he Science DMZ model provides a reference set of network design patterns, tuned hosts and protocol stacks dedicated to large data transfers and streamlined security postures that significantly improve data transfer performance, accelerating scientific collaboration and discovery. Over the past decade, many universities and organizations have adopted this model for their research computing. Despite becoming increasingly popular, there is a lack of quantitative studies comparing such a specialized network to conventional production networks regarding network characteristics and data transfer performance. But does a Science DMZ exhibit significantly different behavior than a general-purpose campus network? Does it improve application performance compared a to general-purpose network? Through a two-year-long quantitative network measurement study, we find that a Science DMZ exhibits lower latency, higher throughput, and lower jitter behaviors. We also see several non-intuitive results. For example, a DMZ may take a longer route to external destinations and experience higher latency than the campus network. While the DMZ model benefits researchers, the benefits are not automatic, careful network tuning based on specific use cases is required to realize the full potential of Science DMZs.

The recent COVID-19 crisis has demonstrated the potential of cutting-edge genomics research. However, privacy of these sensitive pieces of information is an area of significant concern for health professionals and genomics researchers. The current security model depends on securing location and data transmission channels but lacks the needed flexibility. Even when data is encrypted, key distribution and access control to those keys can be complex, leading to complex systems and reduced research productivity. In this work, we investigate an attribute-based access control model for genomics data. This work builds on top of a Named Data Networking (NDN) Name-based Access Control (NAC) model and explores how attribute-based encryption can be used to provide access control.

Cities around the world are increasingly promoting electric vehicles (EV) to reduce and ultimately eliminate greenhouse gas emissions. A huge number of EVs will put unprecedented stress on the power grid. To efficiently serve the increased charging load, these EVs need to be charged in a coordinated fashion. One promising coordination strategy is vehicle-to-vehicle (V2V) charging coordination, enabling EVs to sell their surplus energy in an ad-hoc, peer to peer manner. This paper introduces an Information Centric Networking (ICN)-based protocol to support ad-hoc V2V charging coordination (V2V-CC). Our evaluations demonstrate that V2V-CC can provide added flexibility, fault tolerance, and reduced communication latency than a conventional centralized cloud based approach. We show that V2V-CC can achieve a 93% reduction in protocol completion time compared to a conventional approach. We also show that V2V-CC also works well under extreme packet loss, making it ideal for V2V charging coordination.

The COVID-19 crisis and the subsequent vaccine development have demonstrated the potential of cutting-edge genomics research. However, the privacy of these sensitive pieces of information is an area of significant concern for genomics researchers. The current security models make it difficult to create flexible, automated, and secure data-sharing frameworks. These models also increase the complexity of adding or revoking access.

With live video streaming becoming accessible in various applications on all client platforms, it is imperative to create a seamless and efficient distribution system that is flexible enough to choose from multiple Internet architectures best suited for video streaming (live, on-demand, AR). In this paper, we highlight the benefits of such a hybrid system for live video streaming as well as present a detailed analysis with the goal to provide a high quality of experience (QoE) for the viewer. For our hybrid architecture, video streaming is supported simultaneously over TCP/IP and Named Data Networking (NDN)-based architecture via operating system and networking virtualization techniques to design a flexible system that utilizes the benefits of these varying internet architectures. Also, to relieve users from the burden of installing a new protocol stack (in the case of NDN) on their devices, we developed a lightweight solution in the form of a container that includes the network stack as well as the streaming application. At the client, the required Internet architecture (TCP/IP versus NDN) can be selected in a transparent and adaptive manner.Based on a prototype we have designed and implemented maintaining efficient use of network resources, we demonstrate that in the case of live streaming, NDN achieves better QoE per client than IP and can also utilize higher than allocated bandwidth through in-network caching. Even without caching, our hybrid setup achieves better average bitrate over live video streaming services than its IP-only alternative. Furthermore, we present detailed analysis on ways adaptive video streaming with NDN can be further improved with respect to QoE.

In today's era of Internet of Things (IoT), where massive amounts of data are produced by IoT and other devices, edge computing has emerged as a prominent paradigm for low-latency data processing. However, applications may have diverse latency requirements: certain latency-sensitive processing operations may need to be performed at the edge, while delay-tolerant operations can be performed on the cloud, without occupying the potentially limited edge computing resources. To achieve that, we envision an environment where computing resources are distributed across edge and cloud offerings. In this paper, we present the design of CLEDGE (CLoud + EDGE), an information-centric hybrid cloud-edge framework, aiming to maximize the on-time completion of computational tasks offloaded by applications with diverse latency requirements. The design of CLEDGE is motivated by the networking challenges that mixed reality researchers face. Our evaluation demonstrates that CLEDGE can complete on-time more than 90% of offloaded tasks with modest overheads.

Climate and other big data applications face substantial problems in terms of data storage, retrieval, sharing and management. While several community repositories and tools are available to help with climate data, these problems still persist and the community is actively looking for better solutions. In this project we apply NDN to support climate modeling applications. The information-centric nature of NDN, where content becomes a first class entity, simplifies many of the problems in this domain. NDN offers lightweight data publication, discovery and retrieval compared to IP-based solutions. However, introducing a new network architecture to a mature domain that routinely produces petabytes of datasets and a plethora of assorted tools to manipulate them, is a risky proposition. The advantages of NDN alone may not be sufficient to overcome the natural inertia. Our approach is to introduce NDN while carefully avoiding undue disruption to existing workflows. To that extent we employ a user interface that employs familiar filesystem operations to publish, discover and retrieve data, integrated with domain-specific translators that automatically convert and publish datasets as NDN objects. We outline the advantages of NDN in this application domain and the challenges we faced during the adaptation. We believe this is the first exercise in applying NDN in an existing, large, mature application domain.