Host-Aware/Drive-Managed Shingled Magnetic Recording (SMR) drives can accept non-sequential writes using a buffer called media cache. Data in the media cache will be migrated to its designated location by a cleaning process if the buffer is full (blocking cleaning) or the drive is idle (idle cleaning). However, blocking cleanings can severely extend the I/O response time. Therefore, it is crucial to fully understand the cleaning process and find ways of mitigating the caused performance degradation. In this article we further evaluate the cleaning process and propose a potential remedy scheme called Idler on Host-Aware SMR drives. Idler adaptively induces idle cleanings based on dynamic workload characteristics and media cache usages to reduce the severity of blocking cleanings. Our evaluations show that in the workloads with a small non-sequential write ratio (about 10 percent), Idler can reduce the tail response time and the workload finish time by 56-88 and 10-23 percent, respectively, compared with those without such control. With the help of an external write buffer on an SSD, the tail response time of SMR drives with Idler can be closer to that of conventional disk drives.

Network Function Virtualization (NFV) on Software-Defined Networks (SDN) can effectively optimize the allocation of Virtual Network Functions (VNFs) and the routing of network flows simultaneously. Nevertheless, most previous studies on NFV focus on unicast service chains and thereby are not scalable to support a large number of destinations in multicast. On the other hand, the allocation of VNFs has not been supported in the current SDN multicast routing algorithms. In this paper, therefore, we make the first attempt to tackle a new challenging problem for finding a service forest with multiple service trees, where each tree contains multiple VNFs required by each destination. Specifically, we formulate a new optimization, named Service Overlay Forest (SOF), to minimize the total cost of all allocated VNFs and all multicast trees in the forest. We design a new 3ρST-approximation algorithm to solve the problem, where ρSTdenotes the best approximation ratio of the Steiner Tree problem, and the distributed implementation of the algorithm is also presented. Simulation results on real networks for data centers manifest that the proposed algorithm outperforms the existing ones by over 25%. Moreover, the implementation of an experimental SDN with HP OpenFlow switches indicates that SOF can significantly improve the QoE of the Youtube service.

Deep packet filtering (DPF) has been demonstrated as an essential technique for effective fine-grained access controls, but it is commonly recognized that the technique may invade the individual privacy of the users. Secure computation can address the tradeoff between privacy and DPF functionality, but the current solutions limit the scalability of the network due to the intensive computation overheads and large connection setup delay, especially for the latest network paradigm, network function virtualisation (NFV) and software-defined network (SDN). In this paper, therefore, we propose a privacy-preserving deep packet filtering protocol, named DPF-ET, that can efficiently perform filtering function over encrypted traffic while diminishing the communication overhead and setup delay for the controller in SDN. DPF-ET guarantees the data privacy for users and remains rule privacy for the network owner. The implementation results on an experimental HP SDN/NFV platform demonstrate that the proposed DPF-ET outperforms the current approaches by reducing 250 times in the communications overhead and 32 times in the setup delay.