Xiaoling Li

Topology awareness algorithm for virtual network mapping

Network virtualization is recognized as an effective way to overcome the ossification of the Internet. However, the virtual network mapping problem (VNMP) is a critical challenge, focusing on how to map the virtual networks to the substrate network with efficient utilization of infrastructure resources. The problem can be divided into two phases: node mapping phase and link mapping phase. In the node mapping phase, the existing algorithms usually map those virtual nodes with a complete greedy strategy, without considering the topology among these virtual nodes, resulting in too long substrate paths (with multiple hops). Addressing this problem, we propose a topology awareness mapping algorithm, which considers the topology among these virtual nodes. In the link mapping phase, the new algorithm adopts the k-shortest path algorithm. Simulation results show that the new algorithm greatly increases the long-term average revenue, the acceptance ratio, and the long-term revenue-to-cost ratio (R/C).

GDPS: An Efficient Approach for Skyline Queries over Distributed Uncertain Data

Abstract The skyline query as an important aspect of big data management, has received considerable attention from the database community, due to its importance in many applications including multi-criteria decision making, preference answering, and so forth. Moreover, the uncertain data from many applications have become increasing distributed, which makes the central assembly of data at one location for storage and query infeasible and inefficient. The lack of global knowledge and the computational complexity derived from the introduction of the data uncertainty make the skyline query over distributed uncertain data extremely challenging. Although many efforts have addressed the skyline query problem over various distributed scenarios, existing studies still lack the approaches to efficiently process the query. In this paper, we extensively study the distributed probabilistic skyline query problem and propose an efficient approach GDPS to address the problem with an optimized iterative feedback mechanism based on the grid summary. Furthermore, many strategies for further optimizing the query are also proposed, including the optimization strategies for the local pruning, tuple selecting and the server pruning. Extensive experiments on real and synthetic data sets have been conducted to verify the effectiveness and efficiency of our approach by comparing with the state-of-the-art approaches.

Parallelizing skyline queries over uncertain data streams with sliding window partitioning and grid index

Skyline query processing over uncertain data streams has attracted considerable attention in database community recently, due to its importance in helping users make intelligent decisions over complex data in many real applications. Although lots of recent efforts have been conducted to the skyline computation over data streams in a centralized environment typically with one processor, they cannot be well adapted to the skyline queries over complex uncertain streaming data, due to the computational complexity of the query and the limited processing capability. Furthermore, none of the existing studies on parallel skyline computation can effectively address the skyline query problem over uncertain data streams, as they are all developed to address the problem of parallel skyline queries over static certain data sets. In this paper, we formally define the parallel query problem over uncertain data streams with the sliding window streaming model. Particularly, for the first time, we propose an effective framework, named distributed parallel framework to address the problem based on the sliding window partitioning. Furthermore, we propose an efficient approach (parallel streaming skyline) to further optimize the parallel skyline computation with an optimized streaming item mapping strategy and the grid index. Extensive experiments with real deployment over synthetic and real data are conducted to demonstrate the effectiveness and efficiency of the proposed techniques.

Parallel skyline queries over uncertain data streams in cloud computing environments

Skyline query processing over uncertain data streams has attracted considerable attention recently, due to its importance in helping users make intelligent decisions on complex data. Nevertheless, existing studies only focus on retrieving the skylines over data streams in a centralised environment typically with one processor, which limits the scalability and cannot meet the requirement for massive data analysis. Cloud computing provides unprecedentedly opportunities for supporting massive data management, which can be well adapted to the parallel skyline queries. In this paper, we extensively study the parallel skyline query problem over uncertain data streams in cloud computing environments. Particularly, three parallel models SPM, APM, and DPM are proposed to address the problem based on the sliding window partitioning. Additionally, an adaptive sliding granularity adjustment strategy and a load balance strategy are proposed to further optimise the queries. Extensive experiments are conducted to demonstrate the effectiveness and efficiency of the proposals.

MABP: an optimal resource allocation approach in data center networks

In data center networks, resource allocation based on workload is an effective way to allocate the infrastructure resources to diverse cloud applications and satisfy the quality of service for the users, which refers to mapping a large number of workloads provided by cloud users/tenants to substrate network provided by cloud providers. Although the existing heuristic approaches are able to find a feasible solution, the quality of the solution is not guaranteed. Concerning this issue, based on the minimum mapping cost, this paper solves the resource allocation problem by modeling it as a distributed constraint optimization problem. Then an efficient approach is proposed to solve the resource allocation problem, aiming to find a feasible solution and ensuring the optimality of the solution. Finally, theoretical analysis and extensive experiments have demonstrated the effectiveness and efficiency of our proposed approach.

SPGM: an efficient algorithm for mapping MapReduce-like data-intensive applications in data centre network

In traditional data centre network, how to efficiently allocate the virtual data centres VDCs on the physical data centre network PDCN is a challenging problem, which is denoted as GraphMap. GraphMap refers to map the virtual nodes to the substrate nodes and the virtual links to the substrate paths, respectively. The existing heuristic approaches attempt a two stage solution by solving the node mapping in a first stage and doing the link mapping in a second stage, which results in the mapping time being very large. In this paper, we propose an efficient mapping algorithm based on shortest path graph matching SPGM for online MapReduce-like data-intensive applications; the simulations show that SPGM can efficiently allocate the MapReduce-like data intensive applications on the PDCN in a much shorter time compared to the existing heuristic algorithms and maintain good performance.

BLOR: An efficient bandwidth and latency sensitive overlay routing approach for flash data dissemination

The problem of flash data dissemination refers to transmitting time‐critical data to a large group of distributed receivers in a timely manner, which widely exists in many mission‐critical applications and Web services. However, existing approaches for flash data dissemination fail to ensure the timely and efficient transmission, because of the unpredictability of the dissemination process. Overlay routing has been widely used as an efficient routing primitive for providing better end‐to‐end routing quality by detouring inefficient routing paths in the real networks. To improve the predictability of the flash data dissemination process, we propose a bandwidth and latency sensitive overlay routing approach named BLOR, by optimizing the overlay routing and avoiding inefficient paths in flash data dissemination. BLOR tries to select optimal routing paths in terms of network latency, bandwidth capacity, and available bandwidth in nature, which has never been studied before. Additionally, a location‐aware unstructured overlay topology construction algorithm, an unbiased top‐k dominance model, and an efficient semi‐distributed information management strategy are proposed to assist the routing optimization of BLOR. Extensive experiments have been conducted to verify the effectiveness and efficiency of the proposals with real‐world data sets. Copyright

A runtime software monitoring cost analysis method

Pointing to the problem of extra monitoring cost of injected runtime software monitoring technologies, this work proposes a method MCAM, which is used to analyze the extra software monitoring cost. Analyzing and disposing obtained monitoring information, reducing to inject the unnecessary monitoring probes; we propose an optimization analysis method OCAM, which can reduce the software monitoring cost at a certain extent. MCAM and OCAM are mainly based on three factors, software complexity, user monitoring requirement, and execution traces. Based on these factors, this paper gives out the formal definitions and analysis processes of the two methods. Experiment result shows that software monitoring cost computed by OCAM is smaller than computed by MCAM.

PSMM: A Plug-In Based Software Monitoring Method

Pointing to the problem of security and reliability in modern software system, we put forward a plug-in based software monitoring method, PSMM. Firstly, we propose the model of the software monitoring method. Secondly, we deeply study two main parts of PSMM, monitoring method construction platform and monitoring information collection platform. Lastly, we validate the monitoring method by constructing Nuclear Power Control Simulation System. Results indicate that the monitoring method could obtain internal information of the software which used to judging whether the system is under an acceptable status; the method can improve the running quality of software, reduce the probability of failure, and improve the reliability of software.

Efficient skyline computation over distributed interval data

The increasing volume of uncertain data has resulted in a dire need for supporting efficient uncertain data management. The skyline query as an important aspect of data management has received considerable attention in recent years, because of its importance in making intelligent decisions over complex data. Moreover, data collection and storage have become increasingly distributed, which makes the central assembly of data for storage and query infeasible and inefficient. Although many research efforts have been conducted to address the skyline query problem in various distributed scenarios, we still lack algorithms to address the queries over interval data, which is a special kind of attribute‐level uncertain data that widely exists in many applications. In this paper, we extensively study the skyline query over distributed interval data. We model the skyline query problem and define the distributed skyline query over interval data. Particularly, 2 efficient algorithms are proposed to retrieve the skylines progressively from distributed local sites with a highly optimized feedback framework. Moreover, we exploit 2 strategies for further improving the queries. Extensive experiments on synthetic and real datasets with real deployment are conducted to validate the effectiveness and efficiency of our proposals.