Fabric-on-a-Chip: Toward Consolidating Packet Switching Functions on Silicon

The switching capacity of an Internet router is often dictated by the memory bandwidth required to bu¤er arriving packets. With the demand for greater capacity and improved service provisioning, inherent memory bandwidth limitations are encountered rendering input queued (IQ) switches and combined input and output queued (CIOQ) architectures more practical. Output-queued (OQ) switches, on the other hand, offer several highly desirable performance characteristics, including minimal average packet delay, controllable Quality of Service (QoS) provisioning and work-conservation under any admissible traffic conditions. However, the memory bandwidth requirements of such systems is O(NR), where N denotes the number of ports and R the data rate of each port. Clearly, for high port densities and data rates, this constraint dramatically limits the scalability of the switch. In an effort to retain the desirable attributes of output-queued switches, while significantly reducing the memory bandwidth requirements, distributed shared memory architectures, such as the parallel shared memory (PSM) switch/router, have recently received much attention. The principle advantage of the PSM architecture is derived from the use of slow-running memory units operating in parallel to distribute the memory bandwidth requirement. At the core of the PSM architecture is a memory management algorithm that determines, for each arriving packet, the memory unit in which it will be placed. However, to date, the computational complexity of this algorithm is O(N), thereby limiting the scalability of PSM switches. In an effort to overcome the scalability limitations, it is the goal of this dissertation to extend existing shared-memory architecture results while introducing the notion of Fabric on a Chip (FoC). In taking advantage of recent advancements in integrated circuit technologies, FoC aims to facilitate the consolidation of as many packet switching functions as possible on a single chip. Accordingly, this dissertation introduces a novel pipelined memory management algorithm, which plays a key role in the context of on-chip output- queued switch emulation. We discuss in detail the fundamental properties of the proposed scheme, along with hardware-based implementation results that illustrate its scalability and performance attributes. To complement the main effort and further support the notion of FoC, we provide performance analysis of output queued cell switches with heterogeneous traffic. The result is a flexible tool for obtaining bounds on the memory requirements in output queued switches under a wide range of tra¢ c scenarios. Additionally, we present a reconfigurable high-speed hardware architecture for real-time generation of packets for the various traffic scenarios. The work presented in this thesis aims at providing pragmatic foundations for designing next-generation, high-performance Internet switches and routers

Providing flow based performance guarantees for buffered crossbar switches

Buffered crossbar switches are a special type of com-bined input-output queued switches with each crosspoint of the crossbar having small on-chip buffers. The introduc-tion of crosspoint buffers greatly simplifies the scheduling process of buffered crossbar switches, and furthermore en-ables buffered crossbar switches with speedup of two to eas-ily provide port based performance guarantees. However, recent research results have indicated that, in order to pro-vide flow based performance guarantees, buffered crossbar switches have to either increase the speedup of the cross-bar to three or greatly increase the total number of cross-point buffers, both adding significant hardware complexity. In this paper, we present scheduling algorithms for buffered crossbar switches to achieve flow based performance guar-antees with speedup of two and with only one or two buffers at each crosspoint. When there is no crosspoint blocking in a specific time slot, only the simple and distributed in-put scheduling and output scheduling are necessary. Other-wise, the special urgent matching is introduced to guarantee the on-time delivery of crosspoint blocked cells. With the proposed algorithms, buffered crossbar switches can pro-vide flow based performance guarantees by emulating push-in-first-out output queued switches, and we use the counting method to formally prove the perfect emulation. For the special urgent matching, we present sequential and paral-lel matching algorithms. Both algorithms converge with N iterations in the worst case, and the latter needs less itera-tions in the average case. Finally, we discuss an alternative backup-buffer implementation scheme to the bypass path, and compare our algorithms with existing algorithms in the literature

Host and Network Optimizations for Performance Enhancement and Energy Efficiency in Data Center Networks

Modern data centers host hundreds of thousands of servers to achieve economies of scale. Such a huge number of servers create challenges for the data center network (DCN) to provide proportionally large bandwidth. In addition, the deployment of virtual machines (VMs) in data centers raises the requirements for efficient resource allocation and find-grained resource sharing. Further, the large number of servers and switches in the data center consume significant amounts of energy. Even though servers become more energy efficient with various energy saving techniques, DCN still accounts for 20% to 50% of the energy consumed by the entire data center. The objective of this dissertation is to enhance DCN performance as well as its energy efficiency by conducting optimizations on both host and network sides. First, as the DCN demands huge bisection bandwidth to interconnect all the servers, we propose a parallel packet switch (PPS) architecture that directly processes variable length packets without segmentation-and-reassembly (SAR). The proposed PPS achieves large bandwidth by combining switching capacities of multiple fabrics, and it further improves the switch throughput by avoiding padding bits in SAR. Second, since certain resource demands of the VM are bursty and demonstrate stochastic nature, to satisfy both deterministic and stochastic demands in VM placement, we propose the Max-Min Multidimensional Stochastic Bin Packing (M3SBP) algorithm. M3SBP calculates an equivalent deterministic value for the stochastic demands, and maximizes the minimum resource utilization ratio of each server. Third, to provide necessary traffic isolation for VMs that share the same physical network adapter, we propose the Flow-level Bandwidth Provisioning (FBP) algorithm. By reducing the flow scheduling problem to multiple stages of packet queuing problems, FBP guarantees the provisioned bandwidth and delay performance for each flow. Finally, while DCNs are typically provisioned with full bisection bandwidth, DCN traffic demonstrates fluctuating patterns, we propose a joint host-network optimization scheme to enhance the energy efficiency of DCNs during off-peak traffic hours. The proposed scheme utilizes a unified representation method that converts the VM placement problem to a routing problem and employs depth-first and best-fit search to find efficient paths for flows

Scheduling in Networks with Limited Buffers

In networks with limited buffer capacity, packet loss can occur at a link even when the average packet arrival rate is low compared to the link's speed. To offer strong loss-rateguarantees, ISPs may need to adopt stringent routing constraints to limit the load at the network links and the routing path length. However, to simultaneously maximize revenue, ISPs should be interested in scheduling algorithms that lead to the least stringent routing constraints. This work attempts to address the ISPs needs as follows. First, by proposing an algorithm that performs well (in terms of routing constraints) on networks of output queued (OQ) routers (that is, ideal routers), and second, by bounding the extra switch fabric speed and buffer capacity required for the emulationof these algorithms in combined input-output queued (CIOQ) routers.The first part of the thesis studies the problem of minimizing the maximum session loss rate in networks of OQ routers. It introduces the Rolling Priority algorithm, a local online scheduling algorithm that offers superior loss guarantees compared to FCFS/Drop Tail and FCFS/Random Drop. Rolling Priority has the following properties: (1) it does not favor any sessions over others at any link, (2) it ensures a proportion of packets from each session are subject to a negligibly small loss probability at every link along the session's path, and (3) maximizes the proportion of packets subject to negligible loss probability. The second part of the thesis studies the emulation of OQ routers using CIOQ. The OQ routers are equipped with a buffer of capacity B packets at every output. For the family of work-conserving scheduling algorithms, we find that whereas every greedy CIOQ policy is valid for the emulation of every OQ algorithm at speedup B, no CIOQ policy is valid at speedup less than the cubic root of B-2 when preemption is allowed. We also find that CCF, a well-studied CIOQ policy, is not valid at any speedup less than B. We then introduce a CIOQ policy CEH, that is valid at speedup greater than the square root of 2(B-1)

Configurable data center switch architectures

In this thesis, we explore alternative architectures for implementing con_gurable Data Center Switches along with the advantages that can be provided by such switches. Our first contribution centers around determining switch architectures that can be implemented on Field Programmable Gate Array (FPGA) to provide configurable switching protocols. In the process, we identify a gap in the availability of frameworks to realistically evaluate the performance of switch architectures in data centers and contribute a simulation framework that relies on realistic data center traffic patterns. Our framework is then used to evaluate the performance of currently existing as well as newly proposed FPGA-amenable switch designs. Through collaborative work with Meng and Papaphilippou, we establish that only small-medium range switches can be implemented on today's FPGAs. Our second contribution is a novel switch architecture that integrates a custom in-network hardware accelerator with a generic switch to accelerate Deep Neural Network training applications in data centers. Our proposed accelerator architecture is prototyped on an FPGA, and a scalability study is conducted to demonstrate the trade-offs of an FPGA implementation when compared to an ASIC implementation. In addition to the hardware prototype, we contribute a light weight load-balancing and congestion control protocol that leverages the unique communication patterns of ML data-parallel jobs to enable fair sharing of network resources across different jobs. Our large-scale simulations demonstrate the ability of our novel switch architecture and light weight congestion control protocol to both accelerate the training time of machine learning jobs by up to 1.34x and benefit other latency-sensitive applications by reducing their 99%-tile completion time by up to 4.5x. As for our final contribution, we identify the main requirements of in-network applications and propose a Network-on-Chip (NoC)-based architecture for supporting a heterogeneous set of applications. Observing the lack of tools to support such research, we provide a tool that can be used to evaluate NoC-based switch architectures.Open Acces