Heng Chen

A novel coverage algorithm based on event-probability-driven mechanism in wireless sensor network

Coverage problem is an important research topic in the field of wireless sensor network (WSN). The coverage algorithm based on event probability driven mechanism (EPDM) is put forward in this paper. First of all, the network probability model is established and the subordinate relation between sensor nodes and the target nodes is presented. Secondly, a series of probability is computed and the related theorems and reasoning are also proven. Thirdly, effective coverage for the monitoring region is achieved through scheduling mechanism of nodes themselves, thus the purpose of increasing network lifetime can be realized. Finally, experimental results show that the proposed algorithm could achieve complete coverage for networks of different scale and increase the network lifetime. It possesses the good quality of effectiveness and stability.

An Optimized Strategy Coverage Control Algorithm for WSN

The problem of using lesser wireless sensor network nodes to achieve coverage and connection of certain areas under given coverage conditions is a priority and hotspot issue of WSN. For this reason, in this paper, an optimized strategy coverage control (OSCC) algorithm is proposed. First of all, a relation mapping model of sensor nodes and target nodes is established by OSCC which is based on geometric figure and related theories, probability theory, converge property, and so forth to complete effective reasoning and calculate certain network models. Secondly, OSCC makes efficient analysis of the calculating results figure out the least number of sensor nodes to cover specific monitoring area. Thirdly, OSCC picks out the optimal routing solution while conducting combinatorial optimization of routing path using ant colony optimization (ACO) algorithm, thus reducing the energy spending of whole network. In the end, this paper verifies OSCC algorithm by simulation experiment and proves it can use least sensor nodes to effectively cover target area. Also, OSCC helps greatly reduce network energy consuming, minimize network resources layout costs, and enhance network life cycle, simultaneously.

Small files storing and computing optimization in Hadoop parallel rendering

The Hadoop framework has been widely used in the animation industry to build a large scale, high performance parallel render system. However, Hadoop Distributed File System (HDFS) and MapReduce programming model are designed to manage large files and suffer performance penalty while rendering and storing small RIB files in rendering system. Therefore, method that merging small RIB files based on two intelligent algorithms is proposed to solve the problem. The method uses Particle Swarm Optimization (PSO) and Support Vector Machine (SVM) to choose the optimal merge value for any scene file, by mainly considering the rendering time, memory limitation and other indicators. Then, the method takes advantage of frame-to-frame coherence to merge RIB files at an interval way with the optimal merge value. Finally, the proposed method is compared with the naive method under three different render scenes. Experimental results show that the proposed method significantly reduces the number of RIB files and render tasks, and improves the storage efficiency and computing efficiency of RIB Files.

Thread Count Prediction Model: Dynamically Adjusting Threads for Heterogeneous Many-Core Systems

Determining an appropriate thread count for a multithread application running on a heterogeneous many-core system is crucial for improving computing performance and reducing energy consumption. This paper investigates the interrelation between thread count and computing performance of applications, and designs a prediction model of the optimum thread count on the basis of Amdahls law combined with regression analysis theory to improve computing performance and reduce energy consumption. The prediction model can estimate the optimum tread count relying on the program running behaviors and the architecture characteristics of heterogeneous many-core system. Using the estimated optimum thread count, the number of the active hardware threads and processing cores on the many-core processor is dynamically adjusted in the process of thread mapping to improve the energy efficiency of entire heterogeneous many-core system. The experimental results show that, using this paper proposed thread count prediction model, on an average, the computing performance is improved by 48.6%, energy consumption is reduced by 59%, and additional overhead introduced is 2.03% compared with that of the traditional thread mapping for the PARSEC benchmark programs run on an Intel MIC heterogeneous many-core system.

A Novel Spatio-Temporal Attributes Index Based Query for Wireless Sensor Networks

Wireless sensor networks (WSNs) are envisioned to consist of hundreds to thousands of wireless sensor nodes. The operator doesnt interest in the data sensed by a specific sensor node generally, on the contrary, he pays more attention to the data gathered from a specific area in granted time. One crucial problem is how to process the great deal of data and respond to the query request. We propose a novel query processing based data attributes, called Spatio-Temporal Attributes R-tree based Query (STARQ). Consider the similarity of data collected by a sensor node and its neighboring nodes, partial clustering algorithm is used to form a storage cluster. Partial clustering algorithm is implemented in two phases. First phase is the beginning of partial clustering, in which an object occurs. In second phase, a certain node (e.g., resumes from failure) senses an existed object. The method provided in this paper aims to obtain the neighboring nodes firstly, and judges whether existing a storage cluster that conforms to metadata of the sensor node or not. If the relevant storage cluster does not exist, first phase works, otherwise second phase. If failed in first phase, partial clustering algorithm is called again after a random time. If there are more than one relevant storage cluster in second phase, exercises a sort algorithm which is in descending order according to the storage nodes capability weight, and tries to join a storage cluster in turn. R-tree [1] is an approximately balanced search tree that is widely used to handle spatial data in traditional database systems. Motivated by the unique characteristic of R-tree, a Saptio-Temporal Attributes R-tree (STAR) is built on the top of storage clusters. Objects in STAR are not restricted to the geographical rectangles and could be any abstract ranges of arbitrary attributes. A top-down approach that achieves energy efficiency is adopted to locate the corresponding storage nodes, which transmit the relevant data back to the operator. We compare STARQ with Directed Diffusion [2] and GHT [3] in NS-2. To measure the performance of these protocols, we consider two metrics: interval of query and the size of network. The simulation results show that STARQ has better performance with different query intervals and network size.

Fine-grained scheduling in multi-resource clusters

In multi-resource clusters, many schedulers allocate resources based on fixed quantities. However, fixed allocations can easily lead to resource fragmentation and over-commitment problems, which may result in lower resource utilization and performance degradation. This paper proposes a fine-grained method (FGM) to improve the allocation granularity of resource allocation. This method divides tasks into execution stages according to the task requirement estimated using similar tasks at the runtime. Then, task resource requirements are matched with the available server resources by stages to refine two aspects of allocation granularity: allocation duration and allocation quantity. In addition, the FGM may over-allocate resources deliberately to further improve resource utilization and performance. The paper tested the FGM in three environments using both online and offline workloads. The test results show that the FGM can resolve resource fragmentation and over-commitment problems by significantly improving resource utilization and performance with acceptable fairness and scheduling response times.

IncPregel: an incremental graph parallel computation model

Large-scale graph computation is often required in a variety of emerging applications such as social network computation and Web services. Such graphs are typically large and frequently updated with minor changes. However, re-computing an entire graph when a few vertices or edges are updated is often prohibitively expensive. To reduce the cost of such updates, this study proposes an incremental graph computation model called IncPregel, which leverages the non-after-effect property of the first-order Markov chain and provides incremental programming abstractions to avoid redundant computation and message communication. This is accomplished by employing an efficient and fine-grained reuse mechanism. We implemented this model on Hama, a popular open source framework based on Pregel, to construct an incremental graph processing system called IncHama. IncHama automatically detects changes in input in order to recognize “changed vertices” and to exchange reusable data by means of shuffling. The evaluation results on large-scale graphs show that, compared with Hama, IncHama is 1.1–2.7 times faster and can reduce communication messages by more than 50% when the incremental edges increase in number from 0.1 to 100k.

An Efficient Framework for Incremental Graph Computation

Graph computation has become increasingly popular in emerging applications such as social networks and web graphs. In practice, graph is typically large and frequently updated with small changes. However, traditional graph processing systems process the evolving graph in a batch manner. To accelerate these large-scale graph applications, this paper proposes an efficient message reuse framework for incremental graph computation, called IncTracker. IncTracker automatically detects the input data changes and only processes the changed vertices by employing an efficient, fine-grained reuse mechanism. By reusing the intermediate messages and results of previous runs, the proposed framework can efficiently avoid redundant message communication in iterations. We compare the framework with Pregel on several SNAP datasets. The results show that the speedup ranges from 1.10x to 2.26x and the network traffic is significantly reduced when the incremental changes from 0% to 20%.

An efficient predicting delay algorithm for short jobs

Traditional delay scheduling algorithm processes the task scheduling through a fixed time waiting threshold to achieve better data locality. However, this fixed manner often leads to low resource utilization and job performance degradation, especially for short jobs. This paper proposes a predicting delay scheduling algorithm, called P-Delay. P-Delay leverages both the job characteristic and dynamic resource availability to accelerate the short job execution. It provides a dynamic delay scheduling by comparing the transferring cost with the predicted waiting cost to make reasonable wait. Experimental results show that P-Delay can achieve a speedup of 1.45× for short jobs than traditional Delay Scheduling algorithm.

Dynamic Token Based Improving MapReduce Performance in Cloud Computing

In recent years, Hadoop, the open-source implementation of Googles MapReduce, is widely used and has become the de facto standard of big data processing. A typical running environment of Hadoop is cloud computing in which resource heterogeneity is very common due to varied factors including the different hardware of nodes and the different workload on the nodes and etc. The slot-based scheduling in Hadoop causes the inefficient utilization of computing resources in cloud computing environment, which lead to the performance degradation. To solve the problem mentioned above, we propose a dynamic token based method which dynamically controls the number of tasks running on each node according to the available computing resources on a node and the resource requirement of a task. The results of evaluations show that the completion times of single jobs with the proposed method are approaching to the static optimum in the dedicated environment and better than the static optimums in the other two competitive environments. Moreover, the proposed method significantly improves the throughput of mixed workloads in all computing environments and performance in real cloud computing environment have been improved by 45.9% on average.