A Software-defined SoC Memory Bus Bridge Architecture for Disaggregated Computing

Disaggregation and rack-scale systems have the potential of drastically decreasing TCO and increasing utilization of cloud datacenters, while maintaining performance. While the concept of organising resources in separate pools and interconnecting them together on demand is straightforward, its materialisation can be radically different in terms of performance and scale potential. In this paper, we present a memory bus bridge architecture which enables communication between 100s of masters and slaves in todays complex multiprocessor SoCs, that are physically intregrated in different chips and even different mainboards. The bridge tightly couples serial transceivers and a circuit network for chip-to-chip transfers. A key property of the proposed bridge architecture is that it is software-defined and thus can be configured at runtime, via a software control plane, to prepare and steer memory access transactions to remote slaves. This is particularly important because it enables datacenter orchestration tools to manage the disaggregated resource allocation. Moreover, we evaluate a bridge prototype we have build for ARM AXI4 memory bus interconnect and we discuss application-level observed performance.Comment: 3rd International Workshop on Advanced Interconnect Solutions and Technologies for Emerging Computing Systems (AISTECS 2018, part of HiPEAC 2018

Design and implementation of a belief-propagation scheduler for multicast traffic in input-queued switches

Scheduling multicast traffic in input-queued switches to maximize throughput requires solving a hard combinatorial optimization problem in a very short time. This task advocates the design of algorithms that are simple to implement and efficient in terms of performance. We propose a new scheduling algorithm, based on message passing and inspired by the belief propagation paradigm, meant to approximate the provably-optimal scheduling policy for multicast traffic. We design and implement both a software and a hardware version of the algorithm, the latter running on a NetFPGA. We compare the performance and the power consumption of the two versions when integrated in a software router. Our main findings are that our algorithm outperforms other centralized greedy scheduling policies, achieving a better tradeoff between complexity and performance, and it is amenable to practical high-performance implementations

A software framework for alleviating the effects of MAC-aware jamming attacks in wireless access networks

The IEEE 802.11 protocol inherently provides the same long-term throughput to all the clients associated with a given access point (AP). In this paper, we first identify a clever, low-power jamming attack that can take advantage of this behavioral trait: the placement of a lowpower jammer in a way that it affects a single legitimate client can cause starvation to all the other clients. In other words, the total throughput provided by the corresponding AP is drastically degraded. To fight against this attack, we design FIJI, a cross-layer anti-jamming system that detects such intelligent jammers and mitigates their impact on network performance. FIJI looks for anomalies in the AP load distribution to efficiently perform jammer detection. It then makes decisions with regards to optimally shaping the traffic such that: (a) the clients that are not explicitly jammed are shielded from experiencing starvation and, (b) the jammed clients receive the maximum possible throughput under the given conditions. We implement FIJI in real hardware; we evaluate its efficacy through experiments on two wireless testbeds, under different traffic scenarios, network densities and jammer locations. We perform experiments both indoors and outdoors, and we consider both WLAN and mesh deployments. Our measurements suggest that FIJI detects such jammers in realtime and alleviates their impact by allocating the available bandwidth in a fair and efficient way. © Springer Science+Business Media

Physics-inspired methods for networking and communications

Advances in statistical physics relating to our understanding of large-scale complex systems have recently been successfully applied in the context of communication networks. Statistical mechanics methods can be used to decompose global system behavior into simple local interactions. Thus, large-scale problems can be solved or approximated in a distributed manner with iterative lightweight local messaging. This survey discusses how statistical physics methodology can provide efficient solutions to hard network problems that are intractable by classical methods. We highlight three typical examples in the realm of networking and communications. In each case we show how a fundamental idea of statistical physics helps solve the problem in an efficient manner. In particular, we discuss how to perform multicast scheduling with message passing methods, how to improve coding using the crystallization process, and how to compute optimal routing by representing routes as interacting polymers

dReDBox: A Disaggregated Architectural Perspective for Data Centers

Data centers are currently constructed with fixed blocks (blades); the hard boundaries of this approach lead to suboptimal utilization of resources and increased energy requirements. The dReDBox (disaggregated Recursive Datacenter in a Box) project addresses the problem of fixed resource proportionality in next-generation, low-power data centers by proposing a paradigm shift toward finer resource allocation granularity, where the unit is the function block rather than the mainboard tray. This introduces various challenges at the system design level, requiring elastic hardware architectures, efficient software support and management, and programmable interconnect. Memory and hardware accelerators can be dynamically assigned to processing units to boost application performance, while high-speed, low-latency electrical and optical interconnect is a prerequisite for realizing the concept of data center disaggregation. This chapter presents the dReDBox hardware architecture and discusses design aspects of the software infrastructure for resource allocation and management. Furthermore, initial simulation and evaluation results for accessing remote, disaggregated memory are presented, employing benchmarks from the Splash-3 and the CloudSuite benchmark suites.This work was supported in part by EU H2020 ICT project dRedBox, contract #687632.Peer ReviewedPostprint (author's final draft

5G infrastructures supporting end-user and operational services:The 5G-XHaul architectural perspective

We propose an optical-wireless 5G infrastructure offering converged fronthauling/backhauling functions to support both operational and end-user cloud services. A layered architectural structure required to efficiently support these services is shown. The data plane performance of the proposed infrastructure is evaluated in terms of energy consumption and service delay through a novel modelling framework. Our modelling results show that the proposed architecture can offer significant energy savings but there is a clear trade-off between overall energy consumption and service delay.Peer ReviewedPostprint (author's final draft

5G-XHaul:a converged optical and wireless solution for 5G transport networks

This is the pre-peer reviewed version of the following article: Gutiérrez-Terán, J., Maletic, N., Camps, D., Garcia-Villegas, E., Berberana, I., Anastasopoulos, M., Tzanakaki, A., Kalokidou, V., Flegkas, P., Syrivelis, D., Korakis, T., Legg, P., Markovic, D., Limperopoulos, G., Bartelt, J., Chaudhary, J.K., Grieger, M., Vucic, N., Zou, J., Grass, E. 5G-XHaul: a converged optical and wireless solution for 5G transport networks. "Transactions on emerging telecommunications technologies", 8 Juliol 2016, vol. 27, núm. 9, p. 1187-1195, which has been published in final form at http://onlinelibrary.wiley.com.recursos.biblioteca.upc.edu/doi/10.1002/ett.3063/epdf. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.The common European Information and Communications Technology sector vision for 5G is that it should leverage on the strengths of both optical and wireless technologies. In the 5G context, a wide spectra of radio access technologies—such as millimetre wave transmission, massive multiple-input multiple-output and new waveforms—demand for high capacity, highly flexible and convergent transport networks. As the requirements imposed on future 5G networks rise, so do the challenges in the transport network. Hence, 5G-XHaul proposes a converged optical and wireless transport network solution with a unified control plane based on software defined networking. This solution is able to support the flexible backhaul and fronthaul—X-Haul—options required to tackle the future challenges imposed by 5G radio access technologies. 5G-XHaul studies the trade-offs involving fully or partially converged backhaul and fronthaul functions, with the aim of maximising the associated sharing benefits, improving efficiency in resource utilisation and providing measurable benefits in terms of overall cost, scalability and sustainabilityPeer ReviewedPostprint (published version

Wireless-optical network convergence: enabling the 5G architecture to support operational and end-user services

© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.This article presents a converged 5G network infrastructure and an overarching architecture to jointly support operational network and end-user services, proposed by the EU 5G PPP project 5G-XHaul. The 5G-XHaul infrastructure adopts a common fronthaul/backhaul network solution, deploying a wealth of wireless technologies and a hybrid active/passive optical transport, supporting flexible fronthaul split options. This infrastructure is evaluated through a novel modeling. Numerical results indicate significant energy savings at the expense of increased end-user service delay.Peer ReviewedPostprint (author's final draft