Ding Tang

Bloom filter-based workflow management to enable QoS guarantee in wireless sensor networks

As a popular service composition technology, workflow has been successfully used in wireless sensor networks (WSNs) to compose a set of atomic services for service-oriented WSN applications. However, in a resource-constrained WSN, the sensed data is usually inaccurate or even missing, and this affects the normal execution of atomic services and may result in the non-guaranteed workflow QoS. Because the implementation of workflows in WSNs is usually hierarchical, effective workflow management in a WSN should consider both aspects of atomic services and sensor nodes, though this has largely been overlooked in existing research. Hence, a dynamic QoS-oriented, effective and efficient hierarchical workflow management mechanism is necessary. In this paper, we propose a Bloom filter-based hierarchical workflow management model, which coordinates both the atomic service level and the node level for guaranteed workflow QoS. Through constructing the service-level counting Bloom filter (CBF) to maintain the set of normal atomic services, and constructing the node-level Bloom filter (BF) to maintain the set of attribute strings of the current working nodes, an effective and efficient QoS degradation locating can be realized. Furthermore, the corresponding adaptation mechanism for guaranteed QoS is also developed. The case study and experimental evaluations demonstrate the capability of the proposed approach in WSNs.

Vulnerability-constrained multiple minimum cost paths for multi-source wireless sensor networks

In wireless sensor networks, one of the primary requirements is that sensor data acquired from the physical world can be interchanged with all interested collaborative entities in a secure, reliable manner. Because of highly unpredictable nature of the environments caused by malicious attacks or potential threats, minimizing transmission cost between source and sink nodes with jointly considering the security of the whole network is a critical issue. This paper considers two optimization problems of deriving the minimum cost paths from multiple source nodes to the sink node under the guaranteed level of the vulnerability. The link or node vulnerability is defined as a metric, which characterizes the degree of link or node sharing among paths. With the defined link vulnerability, the link vulnerability-constrained minimum cost paths problem is first formulated, and two polynomial-time algorithms are developed for deriving the optimal paths. For the node-vulnerability-constrained minimum cost paths problem, we adopt the network conversion and then achieve the optimal solution with previous proposed algorithms. The necessary condition for solution existence, the optimality of the proposed algorithms, and the related properties of tree network are further theoretically analyzed. Extensive simulations show the significant performance improvements achieved by our proposed algorithms.Copyright

Hierarchical workflow management in wireless sensor network

To build the service-oriented applications in a wireless sensor network (WSN), the workflow can be utilized to compose a set of atomic services and execute the corresponding pre-designed processes. In general, WSN applications rely closely on the sensor data which are usually inaccurate or even incomplete in the resource-constrained WSN. Then, the erroneous sensor data will affect the execution of atomic services and furthermore the workflows, which form an important part in the bottom-to-up dynamics of WSN applications. In order to alleviate this issue, it is necessary to manage the workflow hierarchically. However, the hierarchical workflow management remains an open and challenging problem. In this paper, by adopting the Bloom filter as an effective connection between the sensor node layer and the upper application layer, a hierarchical workflow management approach is proposed to ensure the QoS of workflow-based WSN application. The case study and experimental evaluations demonstrate the capability of the proposed approach.

Improving TCP performance in data center networks with Adaptive Complementary Coding

TCP suffers from low throughput and high latency because of its expensive timeout based loss recovery mechanism in data center networks (DCNs). In this paper, we propose TCP with Adaptive Complementary Coding (TCP-ACC) to effectively address these problems. Without revising existing TCP congestion control, we first design a light-weight complementary coding scheme to avoid TCP timeout which will result in higher throughput and lower latency. In our scheme, the redundancy setting is adaptive to the real-time packet loss rate. Then we introduce Lyapunov optimization framework to find the optimal number of redundant coding packets for TCP-ACC, and we also prove that TCP-ACC can reduce the flow timeout probability close to that of the optimal complementary coding solution. Extensive NS2 simulations show that, compared with other three solutions for TCPs problems in DCNs, TCP-ACC can reduce the flow completion time by 45% and improve the flow throughput by 40% on average.

TCP-FNC: A novel TCP with network coding for wireless networks

In this paper, we propose TCP-FNC (TCP with fast network coding), which is designed to reduce the decoding delay for TCP with network coding. TCP-FNC retains the framework of TCP/NC and includes two schemes to meet the demand of online network coding. First, we develop a feedback based scheme named FCWL, which can efficiently reduce the waiting delay and work well with both RTT-based and loss-based TCP variants. Second, we propose an optimized progressive decoding algorithm named EFU for Gaussian-Jordan elimination, which can further reduce the computation delay from O(n2) to O(n). Evaluation results indicate that our TCP-FNC can achieve shorter decoding delay than TCP/NC and VON without trading off its goodput performance. We verify that TCP-FNC can reduce the decoding delay by 33% on average.

A Multi-queue Aggregation Framework for M2M Traffic in LTE-A and Beyond Networks.

Traffic aggregation has been considered as an effective approach to improve the radio resource utilization for M2M communication in LTE-A and beyond networks. In the LTE-A specification, the Relay Node (RN) is recommended to aggregate uplink M2M small-sized packets. However, the delay brought by the packets aggregation is inevitably increased, which is a vital metric for M2M packets with low delay requirement, such as emergency alerting. In this paper, we propose a new framework for optimal aggregation implemented in the PDCP of RN, which features balancing a tradeoff between QoS requirements of packets and the utilization efficiency of Physical Radio Blocks (PRBs). Specifically, (1) the RN dispatches the new arrival M2M packets into corresponding virtual queues according to their priorities set by M2M devices. Then, an Optimal Aggregating Scheme (OAS) is designed to minimize the PRB usage in condition satisfying the specific restriction of waiting time of packets in virtual queues. (2) The optimal aggregating problem is proved to be a NP-hard problem, which is solved by the Priority Branch and Bound Algorithm (PBBA) and the Priority Aggregating Heuristic. Numerical results illustrate that OAS achieves a tradeoff of QoS and PRB utilization efficiency in comparison with four existing schemes.

A partial-decentralized coflow scheduling scheme in data center networks

In this paper, we propose CGM-PS, a partial-decentralized, un-starving, work-conservative, and preemptive coflow scheduling scheme to shorten the Coflow Completion Time (CCT) for TCP flows in data center networks (DCNs). In CGMPS, we propose both inter- and intra- coflow scheduling policies. In inter-coflow scheduling, we present P-SEBF, which adopts a connected-graph model based novel concept Partialcoflow, to achieve approximate SEBF scheduling in a partial-decentralized manner. In intra-coflow scheduling, we present FP-MDFS to give flow-level priorities and appropriate rates to TCP flows for finishing the coflows as quick as possible without wasting network capacities in a decentralized manner. Trace-based simulation results show that, among existing coflow scheduling schemes, CGM-PS can achieve the minimal CCTs both on average and in the 90th percentile. In brief, CGM-PS only brings about similar scheduling overhead with the decentralized schemes, while it can achieve the CCT performance even better than the near optimal centralized scheme.

Context-Aware Distributed Service Provisioning based on Anycast for Information-Centric Network

Information-Centric Networking (ICN) is a new emerging concept, in which the principal paradigm shifts from the traditional end-to-end connection to the information-centric communication model. In ICN, information unit, such as content or service, is distributed in different sites or data centers to provide large-scale services. A common supporting approach for scalable service provisioning is deploying multiple replica servers throughout the network. Accordingly, an efficient and flexible scheme is needed to direct distributed requests to an appropriate replica server. In this paper, we first propose an incrementally deployable ICN architecture based on edge/core separation. And then, a practical anycast-based service provisioning scheme is presented with joint considerations of both servers contexts and the underlying network conditions. Extensive experiments have been performed to evaluate the proposed scheme. Experiment results show that efficient context-aware distributed service provisioning can be achieved.