L7-filter is a significant deep packet inspection (DPI) extension to Netfilter in Linux's QoS framework. It classifies network traffic based on information hidden in the packet payload. Although the computationally intensive payload classification can be accelerated with multiple processors, the default OS scheduler is oblivious to both the software characteristics and the underlying multicore architecture. In this paper, we present a parallelized L7-filter algorithm and an efficient scheduler technique for multicore servers. Our multithreaded L7-filter algorithm can process the incoming packets on multiple servers boosting the throughput tremendously. Our scheduling algorithm is based on Highest Random Weight (HRW), which maintains the connection locality for the incoming traffic, but only guarantees load balance at the connection level. We present an Adapted Highest Random Weight (AHRW) algorithm that enhances HRW by applying packet-level load balancing with an additional feedback vector corresponding to the queue length at each processor. We further introduce a Hierarchical AHRW (AHRW-tree) algorithm that considers characteristics of the multicore architecture such as cache and hardware topology by developing a hash tree architecture. The algorithm reduces the scheduling overhead toO(logN) instead ofO(N) and produces a better balance between locality and load balancing. Results show that the AHRW-tree scheduler can improve the L7-filter throughput by about 50% on a Sun-Niagara-2-based server compared to a connection locality-based scheduler. Although extensively tested for L7-filter traces, our technique is applicable to many other packet processing applications, where connection locality and load balancing are important while executing on multiple processors. With these speedups and inherent software flexibility, our design and implementation provide a cost-effective alternative to the traffic monitoring and filtering ASICs.

We study a streaming network application-video transcoding to be executed on a multi-core server. It is important for the scheduler to minimize the total processing time and preserve good video quality in an energy-efficient manner. However, the performance of existing scheduling schemes is largely limited by ineffective use of the multi-core architecture characteristic and undifferentiated transcoding cost in terms of energy consumption. In this paper, we identify three key factors that collectively play important roles in affecting transcoding performance: memory access (M), core/cache topology (C) and transcoding format cost (C), or MC2for short. Based on MC2, we propose E-AHRW, an Energy-efficient Adaptive Highest Random Weight hash scheduler by extending the HRW scheduler proposed for packet scheduling on a homogeneous multiprocessor. E-AHRW achieves stream locality and load balancing at both stream and packet (frame) level by adaptively adjusting the hashing decision according to real-time weighted queue length of each processing unit (PU). Based on E-AHRW, we also design, implement and evaluate a hash-tree scheduler to further reduce the computation cost and achieve more effective load balancing on multi-core architectures. Through implementation on an Intel Xeon server and evaluations on realistic workload, we demonstrate that E-AHRW improves throughput, energy efficiency and video quality due to better load balancing, lower L2 cache miss rate and negligible scheduling overhead.

As the line speed of the network evolves at an unprecedented rate, a wide spectrum of network applications call for increasing processing density on network devices. The prevalence of multicore chips ameliorates the stress on processing power, but the QoS guarantee is often ignored. In addition, results of legacy QoS studies are difficult to apply to multicore web servers. Therefore, a multicore scheduler that incorporates QoS concerns is missing. As the network development moves towards cloud computing, we see an increasing importance of QoS guarantees on high performance multicore network appliances. In this paper, we propose a proportional share hash based scheduler, PS-HRW, which extends existing optimizations in multicore scheduling with QoS concerns. We address the network QoS requirement by assigning weights to each connection following the classic General Processor Sharing (GPS) theory. Based on our previous multicore scheduling studies, PS-HRW allocates computing resources based on the QoS requirement, such that the workload is balanced at the packet level, and the connection locality is maintained. To provide accurate QoS guarantee, PS-HRW allocates an integral number of cores first and then allocates the residuals using a partitioning theory. However, different from traditional simulation based approach, we target at two popular applications on modern network appliances: Deep Packet Inspection (DPI) and multimedia transcoding. In addition, we generalize the topology of different multicore architectures into a communication matrix and optimize PS-HRW to incorporate cache awareness. Essentially, PS-HRW schedules incoming traffic efficiently by balancing between connection locality, load balancing, core/cache topology and QoS guarantees.

We design, implement and evaluate a power-efficient and traffic-aware transcoding system on multicore servers that appropriately adjusts the processor operating level. The system is capable of configuring the number of active cores and core frequency "on-the-fly" according to the varying traffic rate. Results on an AMD machine show that our system saves 51.0% power consumption compared to a native system without power-saving schemes. It also outperforms three other power-aware systems, CG [2] (clock gating), C-DVFS [3] (chip-wide DVFS) and Hybrid [1] (chip-wide DVFS + power-gating), by 19.5%, 10.5% and 5.5% reduction of power consumption, respectively.

Ubiquitous multi-core-based web servers and edge routers are increasingly popular in deploying computationally intensive Deep Packet Inspection (DPI) programs. Previous work has shown the benefits of connection locality-based scheduling on multi-core servers to improve L7-filter performance. However, we show that highly threaded hierarchical multi-core processors, such as the Sun Niagara 2 processor, accumulate imbalanced workload at each resource layer. This workload imbalance potentially offsets the benefits from connection locality. In addition, connection-locality-based load balance fails to work when network traffic is unevenly distributed.

This paper proposes a new server architecture EINIC (Enhanced Integrated NIC) for multi-core processors to tackle the mismatch between network speed and host computational capacity. Similar to prior work, EINIC integrates a redesigned NIC onto a CPU. However, we extend the integrated NIC (INIC) to multicore platforms and examine its behaviors with the network receiving optimization. Additionally, by exploiting NICs proximity to CPUs, we also design an I/O-aware last level shared cache (LLC). Our I/O-aware design allows us to split the cache into an I/O cache and a general cache in a flexible way. It ameliorates cache interferences between network and non-network data. Our simulation results show that EINIC not only attacks the mismatch, but also ameliorates the cache interference.

L7-filter is a significant component in Linux's QoS framework that classifies network traffic based on application layer data. It enables subsequent distribution of network resources in respect to the priority of applications. Considerable research has been reported to deploy multi-core architectures for computationally intensive applications. Unfortunately, the proliferation of multi-core architectures has not helped fast packet processing due to: 1) the lack of efficient parallelism in legacy network programs, and 2) the non-trivial configuration for scalable utilization on multi-core servers.In this paper, we propose a highly scalable parallelized L7-filter system architecture with affinity-based scheduling on a multi-core server. We start with an analytical study of the system architecture based on an offline design. Similar to Receive Side Scaling (RSS) in the NIC, we develop a model to explore the connection level parallelism in L7-filter and propose an affinity-based scheduler to optimize system scalability. Performance results show that our optimized L7-filter has superior scalability over the naive multithreaded version. It improves system performance by about 50% when all the cores are deployed.

Virtualization technology is now widely deployed on high performance networks such as 10-Gigabit Ethernet (10GE). It offers useful features like functional isolation, manageability and live migration. Unfortunately, the overhead of network I/O virtualization significantly degrades the performance of network-intensive applications. Two major factors of loss in I/O performance result from the extra driver domain to process I/O requests and the extra scheduler inside the virtual machine monitor (VMM) for scheduling domains.