With the advent of high-performance nonvolatile memory express (NVMe) SSD, the overhead caused by the storage software stack becomes a significant bottleneck for exploiting the potential of NVMe SSD. Recent I/O isolation approaches eliminate CPU switching, I/O interference, and lock contention for I/O queues by pinning I/O threads in isolated and dedicated I/O paths. However, they degrade the overall performance of mixed workloads with heterogeneous I/O demands. The I/O-intensive workloads issue multiple I/O requests and quickly fill up their dedicated I/O queues, resulting in I/O wait. On the contrary, the non-I/O-intensive workloads cannot deliver enough I/O requests to saturate their associated I/O queues. Moreover, frequent allocations and deallocations of I/O request objects expose a significant overhead for I/O-intensive workloads. In this article, we propose EBIO, an efficient block I/O stack for NVMe SSD, to improve the overall performance of mixed workloads. EBIO contains an on-demand queue management strategy (ODQM) and a reusable I/O management strategy (RERM). Specifically, ODQM eliminates I/O wait by dynamically adding queues for I/O-intensive workloads and leverages I/O generation time to guarantee strong sequential consistency and fairness in scheduling I/O requests. RERM reduces the overhead caused by repeatedly allocating I/O request objects for I/O-intensive workloads by reusing the allocated objects. Experimental results show that, compared to the state-of-the-art approaches, EBIO improves input/output operations per second by up to 16.44% and reduces I/O latency by up to 32.76%.

In the wide-area high-performance computing environment, heterogeneous storage resources are geographically distributed in different supercomputing centers, which leads to the barriers between applications and data. This paper proposes a global virtual data space, named GVDS, to meet the needs of unified data access across supercomputing centers. GVDS integrates the parallel/distributed file systems of supercomputing centers to present a virtual space with tremendous storage capability for users. GVDS organizes users into groups for easy management, which allows users to share, collaborate, and perform computations on the stored data. For failure tolerance, global metadata is replicated and distributed on multiple supercomputing centers, redundant I/O service components are deployed in each supercomputing center. GVDS uses adaptive prefetching, caching, and request merging to improve access performance. Experimental results running on real-world supercomputing centers show that, GVDS can deliver excellent I/O performance running micro-benchmark, real-world traces and applications.

Interconnection networks are a significant consideration for high-performance computing and the datacenter. However, interflow interference seriously impacts the communication performance and even causes disastrous congestion. The paper reports a virtual channel (VC)-sharing scheme that is aimed to separate VCs into many groups, and assign them to data flows based on the destination address. The technique can effectively isolate various traffics into separate VCs groups such that heavy loads have a lesser influence on the other normal traffics. As a consequence, we achieve a slimming congestion tree. In the proposal, the routing algorithm is a two-stage selection that includes the port selection and the VC group selection, respectively. Each of them has an independently selecting algorithm so that routing algorithm is a combined tactic by Cartesian product. The experiment represents that our scheme has excellent performance on adversarial traffics. Using our scheme, the growth curve is linear and slow after crossing the saturation point. For benign traffic patterns, our scheme does not effect any oblivious change on the communication performance when the system receives a lower injection rate.

Recently, big data becomes more and more widespread on the Internet, as various P2P protocols, especially BitTorrent, make great contribution. Accompanied with BitTorrent spreading, however, malicious activities, divulging sensitive data and other security problems arise. Relative researches and analyses indicate that existing means of protecting P2P network are sophisticated but intricate when distributing big data, leading inefficiency of implementation. In this paper, a scheme to distribute big data securely and efficiently on BitTorrent network is proposed, which can be implemented in server, authorizing peers' admittances and actions, hence protecting sensitive data in the network. To achieve this goal, identity verification and cipher system are embedded into BitTorrent protocol, enabling the server to regulate and keep trace of peers' behaviors and sensitive data. The experimental results show the functional effectiveness of this scheme, as well as acceptable overhead on server.