Mobile Edge Computing (MEC) provides different storage and computing capabilities within the access range of mobile devices. This moderates the burden of offloading compute/storage-intensive processes of the mobile devices to the centralized cloud data centers. As a result, the network latency is reduced and the quality of service provided for the mobile end users is improved. Different applications benefit from the large-scale deployments of MEC servers. However, the considerable complexity of managing large scale deployments of the sheer number of applications for the millions of mobile devices is a challenge. Recently, Software Defined Networking (SDN) is leveraged to resolve the problem by providing unified and programmable interfaces for managing network devices. Most of the current SDN packet processing services are tightly dependent on the packet classification service. This primary service classifies any incoming packet based on matching a set of specific fields of its header against a flow table. Acceleration of this basic process considerably increases the performance of the SDN-based MEC. In this paper, the hierarchical tree algorithm, which is a packet classification method, is parallelized using popular platforms on a cluster of Graphics Processing Units (GPUs), a cluster of Central Processing Units (CPUs), and a hybrid cluster. The best scenario for the parallel implementation of this algorithm on the CPU cluster is that which combines OpenMP and MPI.

In this case, the throughput of the classifier is 4.2 million packets per second (MPPS). On the GPU cluster, two different scenarios have been used. In the first scenario, the global memory is used to store the rules and the Hierarchical-trie of the classifier while in the second scenario we break the filter set in a way that the resulting Hierarchical-trie of each subset could be stored in the shared memory of GPU. According to the results, although the first GPU cluster scenario achieves a throughput of 29.19 MPPS and a speedup 58 times as great as the serial mode, the second scenario is 12 times faster due to using the shared memory. The best performance, however, belongs to the hybrid cluster mode. The hybrid cluster achieves a throughput of 30.59 which is 1.4 MPPS more than the GPU cluster.

移动边缘计算（MEC）在移动设备的访问范围内提供了不同的存储和计算功能。这减轻了将移动设备的计算/存储密集型过程卸载到集中式云数据中心的负担。结果，减少了网络等待时间并且改善了为移动终端用户提供的服务质量。不同的应用程序受益于MEC服务器的大规模部署。然而，管理数百万个移动设备的大量应用程序的大规模部署的相当大的复杂性是一个挑战。最近，软件定义网络（SDN）通过提供用于管理网络设备的统一和可编程接口来解决该问题。当前大多数SDN数据包处理服务都紧密依赖于数据包分类服务。该主要服务基于将其报头的一组特定字段与流表进行匹配的基础，对任何传入数据包进行分类。加速此基本过程可大大提高基于SDN的MEC的性能。在本文中，分层树算法（一种数据包分类方法）在图形处理单元（GPU）群集，中央处理单元（CPU）群集和混合群集上使用流行的平台进行了并行处理。在CPU群集上并行实现此算法的最佳方案是结合OpenMP和MPI的方案。该主要服务基于将其报头的一组特定字段与流表进行匹配的基础，对任何传入的数据包进行分类。加速此基本过程可大大提高基于SDN的MEC的性能。在本文中，分层树算法（一种数据包分类方法）在图形处理单元（GPU）群集，中央处理单元（CPU）群集和混合群集上使用流行的平台进行了并行处理。在CPU群集上并行实现此算法的最佳方案是结合OpenMP和MPI的方案。该主要服务基于将其报头的一组特定字段与流表进行匹配的基础，对任何传入的数据包进行分类。加速此基本过程可大大提高基于SDN的MEC的性能。在本文中，分层树算法（一种数据包分类方法）在图形处理单元（GPU）群集，中央处理单元（CPU）群集和混合群集上使用流行的平台进行了并行处理。在CPU群集上并行实现此算法的最佳方案是结合OpenMP和MPI的方案。在图形处理单元（GPU）群集，中央处理单元（CPU）群集和混合群集上使用流行的平台进行并行化。在CPU群集上并行实现此算法的最佳方案是结合OpenMP和MPI的方案。在图形处理单元（GPU）群集，中央处理单元（CPU）群集和混合群集上使用流行的平台进行并行化。在CPU群集上并行实现此算法的最佳方案是结合OpenMP和MPI的方案。

在这种情况下，分类器的吞吐量为每秒420万个数据包（MPPS）。在GPU群集上，使用了两种不同的方案。在第一种情况下，全局内存用于存储规则和分类器的Hierarchical-trie，而在第二种情况下，我们以可以将每个子集的结果Hierarchical-trie可以存储在共享库中的方式破坏过滤器集GPU的内存。根据结果​​，尽管第一个GPU群集方案的吞吐量为29.19 MPPS，并且加速比串行模式高58倍，但第二个方案由于使用共享内存，因此要快12倍。但是，最佳性能属于混合群集模式。混合群集实现的吞吐量为30.59，比GPU群集高1.4 MPPS。