The increase in computation and storage has led to a significant growth in the scale of systems powering applications and services, raising concerns about sustainability and operational costs. In this paper, we explore power-saving techniques in high-performance computing (HPC) and data center networks, and their relation with performance degradation. From this premise, we propose leveraging the Energy Efficient Ethernet (EEE) protocol, with the flexibility to extend to conventional Ethernet or upcoming Ethernet-derived interconnect versions of BXI and Omnipath. We analyze the PerfBound power-saving mechanism, identifying possible improvements and modeling it into a simulation framework. Through different experiments, we examine its impact on performance and determine the most appropriate interconnect. We also study traffic patterns generated by selected HPC and machine learning applications to evaluate the behavior of power-saving techniques. From these experiments, we provide an analysis of how applications affect system and network energy consumption. Based on this, we disclose the weakness of dynamic power-down mechanisms and propose an approach that improves energy reduction with minimal or no performance penalty. This work presents a thorough analysis of PerfBound and an enhancement to the technique, while also targeting emerging post-exascale networks.

Abstract Supercomputers (SCs) enable advanced research for a variety of scientific fields, and data centers (DCs) power our day-to-day services. These two massive systems work at scales, in terms of storage and computing power, which are not comparable to our everyday devices. As such, they require state-of-the-art technology to constantly evolve and meet our increasing demand. The interconnection network is the backbone of these systems, since it must provide efficient communication among the nodes that compose the whole system, otherwise becoming the entire system bottleneck. As multiple applications and services may use subsets of the system at the same time, interconnection networks must prevent excessive degradation for latency-sensitive applications. To this end, differentiated services are used to provide fair network access that considers bandwidth and latency requirements for each application. In this paper, we extend the switch architecture of next-generation BXI networks (hereafter called BXIv3) to incorporate arbitration tables so these networks can provide quality of service (QoS) to applications and services running on both SCs and DCs. Our proposal has been implemented in a network simulator, which models the behavior of a BXIv3 network. We have used several traffic patterns and arbitration table configurations to conduct a set of simulation experiments for the evaluation of our solution. The obtained results show that our proposal achieves accurate bandwidth allocation with differentiated latencies. Moreover, a study of memory requirements shows that our solution is quite feasible for hardware implementation.

The ever-increasing demand for computational power and storage capacity has led to massive Supercomputers and Data Centers running highly parallel applications and services commonly utilized in fields such as Physics, Biology, Robotics, Medicine, or generative AI. The interconnection network is the backbone of these systems since it allows processing and storage nodes to communicate with high bandwidth and low latency, otherwise becoming the entire system bottleneck. These systems commonly run several applications simultaneously, which may have specific network requirements due to technical or contractual reasons. Indeed, the traffic flows from different applications are expected to need different bandwidth and latency levels predetermined before execution. Therefore, Quality of Service (QoS) has been a recurrent design aspect for high-performance interconnection networks, as it happens for different technologies, such as Slingshot (Cray) or InfiniBand (NVIDIA). In this regard, as far as we know, no proposals have been made yet to provide applications with QoS for the upcoming generation of BXI (BXI3). We propose using arbitration tables to assign different priorities to packets when they are injected by NICs or forwarded by switches. We have conducted simulation experiments to evaluate our proposal, comparing several QoS configurations using synthetic workloads. The obtained results show that the proposed QoS approach is efficient and feasible so that it can be applied to the upcoming BXI3.

Summary Fog computing has become a complementary technology to cloud computing and addresses some of the cloud computing threats such as the response time and network bandwidth demand. Fog computing successes processing data and storing data near to the edge, and usually is combined with container virtualization to provide hardware isolation. Empowered by these capabilities, numerous Internet of Things (IoT) applications are developed as virtualized instances on resource‐constrained fog nodes such as single‐board computers (SBC). In addition, blockchain has emerged as a key technology that is transforming the way we share information. Blockchain technology represents a decentralised, distributed, and immutable database ledger and is a potential solution for the distributed ecosystem of IoT applications. The distributed structure of blockchain is naturally suitable for IoT applications. However, it introduces new challenges related to CPU overhead or response time. This paper proposes a layered architecture that integrates blockchain technology and OS‐level virtualization technology to develop fog‐based IoT applications. It also provides insights for future deployments through a proof‐of‐concept use case harnessing SBCs, in this case Raspberry Pi, as blockchain‐enabled fog nodes to drive virtualized IoT applications. The study shows that the maximum CPU overhead added by a permissioned blockchain based on Ethereum on the Raspberry Pi is around a 25% under stress situations while the overhead introduced by the sealer process is negligible. These results support the feasibility of using blockchain on resource‐constrained fog nodes for supporting IoT applications.

Abstract Data centers are a fundamental infrastructure in the Big-Data era, where applications and services demand a high amount of data and minimum response times. The interconnection network is an essential subsystem in the data center, as it must guarantee high communication bandwidth and low latency to the communication operations of applications, otherwise becoming the system bottleneck. Simulation is widely used to model the network functionality and to evaluate its performance under specific workloads. Apart from the network modeling, it is essential to characterize the end-nodes communication pattern, which will help identify bottlenecks and flaws in the network architecture. In previous works, we proposed the VEF traces framework: a set of tools to capture communication traffic of MPI-based applications and generate traffic traces used to feed network simulator tools. In this paper, we extend the VEF traces framework with new communication workloads such as deep-learning training applications and online data-intensive workloads.