Virtualization is a building block technology of cloud computing. Software virtualization solutions, such as paravirtualized Memory Management Unit and paravirtualized I/O, provide flexible manageability, such as virtual machine migration and IP-based network packet filtering. However, it suffers from performance and scalability issues. Hardware virtualization and its advanced accelerations, such as Extended Page Table and Single Root I/O Virtualization, were introduced later to simplify virtualization implementation and improve performance. However, this solution may suffer from a manageability issue. In this paper, we describe our work on the implementation of hardware virtualization and optimizations of the advanced hardware acceleration support in Xen to improve the virtualization performance and scalability. We further propose HYVI, a hybrid virtualization solution combining the advantages of software virtualization (manageability) and hardware virtualization (performance and scalability), to address performance and manageability issues. Finally, we present performance evaluations and characterizations of these hardware accelerations and hybrid virtualization, using both OS benchmarks and a server consolidation benchmark (vConsolidate). The results show that optimized hardware acceleration can achieve an up to 77 percent and 33 percent performance improvement over software full virtualization and paravirtualization in the server consolidation benchmark, respectively. Hybrid virtualization achieves an up to 1.56 × performance, with 0.69 lower CPU core usage of paravirtualization, and maintains flexible manageability.

Virtualization is gaining popularity in cloud computing and has become the key enabling technology in cloud infrastructure. By replicating the virtual server state to multiple independent platforms, virtualization improves the reliability and availability of cloud systems. Unfortunately, existing Virtual Machine (VM) replication solutions were designed only for software virtualized I/O, which suffers from large performance and scalability overheads. Although hardware-assisted I/O virtualization (such as SR-IOV) can achieve close to native performance and very good scalability, they cannot be properly replicated across different physical machines due to architectural limitations (such as lack of efficient device state read/write, buffering outbound packets, etc.). In this paper, we address those architectural limitations, by proposing ReNIC, an architectural extension to SR-IOV I/O virtualization for efficient I/O replications. We have extended Xen hypervisor and the Remus rapid checkpoint solution to support this new architectural extension. We developed a system simulator on multi-core systems to extensively evaluate ReNIC. The experimental results demonstrate that ReNIC achieves up to 54% CPU usage reduction, compared to software based I/O virtualization at runtime, and up to 16.2% performance advantage over software based I/O virtualization in rapid checkpoint. During migration, ReNIC reduces service shutdown time by about 50%, compared to device emulation and paravirtualized I/O, and over 71% compared to teaming driver.

Internet-scale services need new design patterns and programming models for the partitioned data set with many copies that are changed independently. This is a huge software challenge. Big Web sites spend 70% of their efforts on undifferentiated heavy lifting (e.g., partitioning, replication and scaling) versus 30% on differentiated value (feature) creation. This talk will review the challenges for Internet-scale services and some of the emerging solutions to address those challenges, based on our experience in building Internet-scale service platforms as well as the industry best practices.

High-quality I/O virtualization (that is, complete device semantics, full-feature set, close-to-native performance and real-time response) is critical to both server and client virtualizations. Existing solutions for I/O virtualization (e.g., full device emulation, paravirtualization and direct I/O) cannot meet the requirements of high-quality I/O virtualization due to high overheads, lack of complete semantic or full-feature set support.

To keep up with the explosive Internet packet processing demands, modern network processors (NPs) employ a highly parallel, multi-threaded and multi-core architecture. In such a parallel paradigm, accesses to the shared variables in the external memory (and the associated memory latency) are contained in the critical sections, so that they can be executed atomically and sequentially by different threads in the network processor. In this paper, we present a novel program transformation that is used in the Intelreg Auto-partitioning C Compiler for IXP to exploit the inherent finer-grained parallelism of those critical sections, using the software-controlled caching mechanism available in the NPs. Consequently, those critical sections can be executed in a pipelined fashion by different threads, thereby effectively hiding the memory latency and improving the performance of network applications. Experimental results show that the proposed transformation provides impressive speedup (up-to 9.9times) and scalability (up-to 80 threads) of the performance for the real-world network application (a 10Gbps Ethernet Core/Metro Router)

No abstract available.

Modern network processors employs parallel processing engines (PEs) to keep up with explosive internet packet processing demands. Most network processors further allow processing engines to be organized in a pipelined fashion to enable higher processing throughput and flexibility. In this paper, we present a novel program transformation technique to exploit parallel and pipelined computing power of modern network processors. Our proposed method automatically partitions a sequential packet processing application into coordinated pipelined parallel subtasks which can be naturally mapped to contemporary high-performance network processors. Our transformation technique ensures that packet processing tasks are balanced among pipeline stages and that data transmission between pipeline stages is minimized. We have implemented the proposed transformation method in an auto-partitioning C compiler product for Intel Network Processors. Experimental results show that our method provides impressive speed up for the commonly used NPF IPv4 forwarding and IP forwarding benchmarks. For a 9-stage pipeline, our auto-partitioning C compiler obtained more than 4X speedup for the IPv4 forwarding PPS and the IP forwarding PPS (for both the IPv4 traffic and IPv6 traffic).

Effective compilation of packet processing applications onto the Intel IXP network processors requires, among other things, the automatic use of multiple threads on one or more processing elements, and the automatic introduction of synchronization as required to correctly enforce dependences between such threads. We describe the program transformation that is used in the Intel Auto-partitioning C Compiler for IXP to automatically multithread/multi-process a program for the IXP. This transformation consists of steps that introduce inter-thread signaling to enforce dependences, optimize the placement of such signaling, reduce the number of signals in use to the number available in hardware, and transform the initialization code for correct execution in the multithreaded version. Experimental results show that our method provides impressive speedup for six PPSes (Packet Processing Stages) in the widely used NPF IP forwarding benchmarks. For most packet processing stages, our algorithms can achieve almost linear performance improvement after automatic multi-threading transformation. The automatic multi-processing transformation help further boost the speedup of two PPSes.

We propose QROUTE, an integrated framework for QoS-guaranteed multicast, in which a feasible multicast tree meeting multiple QoS constraints can be constructed in a distributed fashion using local states at routers. The simplicity in the design and implementation of QROUTE has been demonstrated by prototyping a router testbed, whose measured performance is presented. Compared to other QoS-based multicast routing protocols, QROUTE is more effective because it searches all the available feasible paths in parallel so as to ensure a better chance of successful routing and a more efficient use of network resources. We also evaluate the performance of QROUTE by comparing it with other related protocols through simulations, and find that it has lower blocking probability, lower routing overhead and lower set-up latency; the resulting multicast trees of QROUTE also generally require fewer links than those of the related protocols.