A Heuristic Algorithm for Task Scheduling Based on Mean Load on Grid

Efficient task scheduling is critical to achieving high performance on grid computing environment. The task scheduling on grid is studied as optimization problem in this paper. A heuristic task scheduling algorithm satisfying resources load balancing on grid environment is presented. The algorithm schedules tasks by employing mean load based on task predictive execution time as heuristic information to obtain an initial scheduling strategy. Then an optimal scheduling strategy is achieved by selecting two machines satisfying condition to change their loads via reassigning their tasks under the heuristic of their mean load. Methods of selecting machines and tasks are given in this paper to increase the throughput of the system and reduce the total waiting time. The efficiency of the algorithm is analyzed and the performance of the proposed algorithm is evaluated via extensive simulation experiments. Experimental results show that the heuristic algorithm performs significantly to ensure high load balancing and achieve an optimal scheduling strategy almost all the time. Furthermore, results show that our algorithm is high efficient in terms of time complexity.     

A model for dynamic adaptive coscheduling

is paper proposes a dynamic adaptive coscheduling model DASIC to take advantage of excess available resources in a network of workstations(NOW). Besides coscheduling related subtasks dynamically,DASIC can scale up or down the process space depending upon the number of available processors on an NOW. Based on the dynamic idle processor group(IPG),DASIC employs thre modules:the coscheduling module,the scalabele scheduling module and the load balancing module,and uses six algorithms to achieve scalability.A simplified DASIC was also implemented,and experimental results are presented in this paper,which show that it can maximize system utilization,and achieve task parallelism as much as possible.     

An Effective Cloud Workflow Scheduling Approach Combining PSO and Idle Time Slot-Aware Rules

Workflow scheduling is a key issue and remains a challenging problem in cloud computing.Faced with the large number of virtual machine(VM)types offered by cloud providers,cloud users need to choose the most appropriate VM type for each task.Multiple task scheduling sequences exist in a workflow application.Different task scheduling sequences have a significant impact on the scheduling performance.It is not easy to determine the most appropriate set of VM types for tasks and the best task scheduling sequence.Besides,the idle time slots on VM instances should be used fully to increase resources'utilization and save the execution cost of a workflow.This paper considers these three aspects simultaneously and proposes a cloud workflow scheduling approach which combines particle swarm optimization(PSO)and idle time slot-aware rules,to minimize the execution cost of a workflow application under a deadline constraint.A new particle encoding is devised to represent the VM type required by each task and the scheduling sequence of tasks.An idle time slot-aware decoding procedure is proposed to decode a particle into a scheduling solution.To handle tasks'invalid priorities caused by the randomness of PSO,a repair method is used to repair those priorities to produce valid task scheduling sequences.The proposed approach is compared with state-of-the-art cloud workflow scheduling algorithms.Experiments show that the proposed approach outperforms the comparative algorithms in terms of both of the execution cost and the success rate in meeting the deadline.     

A Resource-Efficient Communication Architecture for Chip Multiprocessors on FPGAs

Significant advances in field-programmable gate arrays (FPGAs) have made it viable to explore innovative multiprocessor solutions on a single FPGA chip.For multiprocessors,an efficient communication network that matches the needs of the target application is always critical to the overall performance.Wormhole packet-switching network-on-chip (NoC) solutions are replacing conventional shared buses to deal with scalability and complexity challenges coming along with the increasing number of processing elements (PEs).However,the quest for high performance networks has led to very complex and resource-expensive NoC designs,leaving little room for the real computing force,i.e.,PEs.Moreover,many techniques offer very small performance gains or none at all when network traffic is light while increasing the resource usage of routers.We argue that computation is still the primary task of multiprocessors and sufficient resources should be reserved for PEs.This paper presents our novel design and implementation of a resource-efficient communication network for multiprocessors on FPGAs.We reduce not only the required number of routers for a given number of PEs by introducing a new PE-router topology,but also the resource requirement of each router.Our communication network relies on the NEWS channels to transfer packets in a pipelined fashion following the path determined by the routing network.The implementation results on various Xilinx FPGAs show good performance in the typical range of network load for multiprocessor applications.     

A Weakly-Supervised Crowd Density Estimation Method Based on Two-Stage Linear Feature Calibration

In a crowd density estimation dataset, the annotation of crowd locations is an extremely laborious task, and they are not taken into the evaluation metrics. In this paper, we aim to reduce the annotation cost of crowd datasets, and propose a crowd density estimation method based on weakly-supervised learning, in the absence of crowd position supervision information, which directly reduces the number of crowds by using the number of pedestrians in the image as the supervised information. For this...     

QoS Negotiation and Renegotiation Based on Mobile Agents

As the Quality of Service （QoS） becomes increasingly important in the Internet development, there are more and more researches focusing on the issues of QoS negotiation. Mobile software agents represent a valid alternative to the implementation of strategies for the negotiation. In this paper, a QoS negotiation and renegotiation system architecture based on mobile agents is proposed. These agents perform the task in the whole process and therefore reduce the network load, overcome latency, and avoid frequent exchange information between clients and server. The simulation results show that it can improve the network resource utility about 10%.     

Fault-Tolerant Scheduling for Real-Time Embedded Control Systems

With the increasing complexity of industrial application, an embedded control system (ECS) requires processing a number of hard real-time tasks and needs fault-tolerance to assure high reliability. Considering the characteristics of real-time tasks in ECS, an integrated algorithm is proposed to schedule real-time tasks and to guarantee that all real-time tasks are completed before their deadlines even in the presence of faults. Based on the nonpreemptive critical-section protocol (NCSP), this paper analyzes the blocking time introduced by resource conflicts of relevancy tasks in fault-tolerant multiprocessor systems. An extended schedulability condition is presented to check the assignment feasibility of a given task to a processor. A primary/backup approach and on-line replacement of failed processors are used to tolerate processor failures. The analysis reveals that the integrated algorithm bounds the blocking time, requires limited overhead on the number of processors, and still assures good processor utilization. This is also demonstrated by simulation results. Both analysis and simulation show the effectiveness of the proposed algorithm in ECS.     

Bandwidth Reservation Using Velocity and Handoff Statistics for Cellular Networks

The percentages of blocking and forced termination rates as parameters representing quality of services （QoS） requirements are presented. The relation between the connection statistics of mobile users in a cell and the handoff number and new call number in next duration in each cell is explored. Based on the relation, statistic reservation tactics are raised. The amount of bandwidth for new calls and handoffs of each cell in next period is determined by using the strategy. Using this method can guarantee the communication system suits mobile connection request dynamic. The QoS parameters： forced termination rate and blocking rate can be maintained steadily though they may change with the offered load, Some numerical experiments demonstrate this is a practical method with affordable overhead.     

A Compressed Sensing Based DV-Hop Location Algorithm for Wireless Sensor Networks

A compressed sensing based DV-hop location algorithm is presented to improve the performance of the conventional DV-hop location algorithm in WSNs （wireless sensor networks）. The sensor network can be divided into multiple grids. Compared with the grid number, the number of targets in the network is generally sparse. Therefore, the localization of the targets in the network can be transformed into a sparse signal reconstruction issue. Theoretical analysis and experimental results on the proposed algorithm show that it is able to greatly reduce the amount of data flow in the network, balance the load of communication, prolong the lifetime of the WSNs, and improve the target location accuracy, compared to the DV-hop location ones.     

A Load Balancing Algorithm in OpenFIow Networks

The goal of load balancing algorithm in OpenFlow networks is to combine the load balancing technology with the Open lqow technology, so that load balancing can adapt to the characteristics of the OpenFlow networks.The study of this paper focused on load balancing in OpenFlow networks, proposed a new load balancing algorithm.The algorithm considered the status of servers and network comprehensively, and provided the metrics for them.To distribute the network traffic to the servers and links reasonably, the goal of load balancing is reached.     

Granularity analysis for exploiting adaptive parallelism of declarative programs on multiprocessors

Declarative Programming Languages (DPLs) apply a grocess model of Horn clauses such as PARLOG^[8] or a reduction model of λ-calculus such as SML^[7] and are,in principle,well suited to multiprocessor implementation.However,the performance of a paralled declarative program can be impaired by a mismatch between the parallelism available in an application and the parallelism available in the architecture.A particularly attractive solution is to automatically match the parallelism of the program to the parallelism of the target hardware as a compilation step.In this paper,we present an optimizing compilation technique called granularity analysis which identifies and removes excess parallelism that would degrade performance.The main steps are:an analysis of the flow of data to form an attributed call graph between function (or predicate) arguments;and an asymptotic estimation of granularity of a function (or predicate) to generate approximate grain size.Compiled procedure calls can be annotated with grain size and a task scheduler can make scheduling decisions with the classification scheme of grains to control parallelism at run-time.The resulting granularity analysis scheme is suitable for exploiting adaptive parallelism of declarative programming languages on multiprocessors.     

A new technique for islanding operation of distribution network connected with mini hydro

An islanding operation of a distribution network is a topic of interest due to the significant penetration of distributed generation(DG) in a power system network. However, controlling the frequency of an islanded distribution system remains an unresolved issue, especially when the load exceeds the generation. This paper presents a new technique for a successful islanding operation of a distribution network connected with multiple mini hydro based DGs. The proposed technique is based on three main parts. The first part uses an islanding detection technique to detect the islanding event correctly. The second part consists of a power imbalance estimation module(PIEM), which determines the power imbalance between the generation and load demand. The third part consists of a load shedding controller, which receives the power imbalance value and performs load shedding according to load priority. The proposed technique is validated on an 11 k V existing Malaysia distribution network. The simulation results show that the proposed technique is effective in performing a successful islanding operation by shedding a significant number of loads.     

Modular supervisory control for multi-floor manufacturing processes

Due to space availability limitations and high land costs, there is an increasing development of multi-floor manufacturing (MFM) systems in urban and industrial areas. The problem of coordination in a multi-floor manufacturing process, in the Ramadge Wonham framework, is introduced. The manufacturing chain of each floor and the elevator system are modeled in the form of finite deterministic automata. The models of the multi-floor manufacturing process are parametric with respect to the number of floors and the number of manufacturing machines on each floor. The coordination desired performance is formulated in the form of desired regular languages in analytic forms. The languages are realized by appropriate supervisors in the form of finite deterministic automata. The models of the supervisors are also parametric with respect to the number of floors and the number of manufacturing machines on each floor. The total control of the coordination of the multi-floor manufacturing process is accomplished via a modular supervisory control architecture. The complexity of the supervisors as well as the complexity of the total modular supervisory architecture are determined in analytic forms with respect to the number of floors and the number of manufacturing machines on each floor. The special case of a two floor manufacturing process is presented as an illustrative example.     

Deep Imitation Learning for Autonomous Vehicles Based on Convolutional Neural Networks

Providing autonomous systems with an effective quantity and quality of information from a desired task is challenging. In particular, autonomous vehicles, must have a reliable vision of their workspace to robustly accomplish driving functions. Speaking of machine vision, deep learning techniques, and specifically convolutional neural networks, have been proven to be the state of the art technology in the field. As these networks typically involve millions of parameters and elements, designing an optimal architecture for deep learning structures is a difficult task which is globally under investigation by researchers. This study experimentally evaluates the impact of three major architectural properties of convolutional networks, including the number of layers, filters, and filter size on their performance. In this study, several models with different properties are developed,equally trained, and then applied to an autonomous car in a realistic simulation environment. A new ensemble approach is also proposed to calculate and update weights for the models regarding their mean squared error values. Based on design properties,performance results are reported and compared for further investigations. Surprisingly, the number of filters itself does not largely affect the performance efficiency. As a result, proper allocation of filters with different kernel sizes through the layers introduces a considerable improvement in the performance.Achievements of this study will provide the researchers with a clear clue and direction in designing optimal network architectures for deep learning purposes.     

A Knowledge-Based Approach to Program Synthesis from Examples

This paper proposes an approach to synthesize functional programs of Backus‘ FP system^[1,2] from input/output instances.Based on a theory of orthogonal expansion of programs^3[,4],the task of program synthesis is expressed in program equations,and fulfilled by solving them according to the knowledge about the equivalence between programs.Some general knowledge of solving program equations with a number of examples are given in the paper.     

Hybrid partial least squares and neural network approach for short-term electrical load forecasting

Intelligent systems and methods such as the neural network （NN） are usually used in electric power systems for short-term electrical load forecasting. However, a vast amount of electrical load data is often redundant, and linearly or nonlinearly correlated with each other. Highly correlated input data can result in erroneous prediction results given out by an NN model. Besides this, the determination of the topological structure of an NN model has always been a problem for designers. This paper presents a new artificial intelligence hybrid procedure for next day electric load forecasting based on partial least squares （PLS） and NN. PLS is used for the compression of data input space, and helps to determine the structure of the NN model. The hybrid PLS-NN model can be used to predict hourly electric load on weekdays and weekends. The advantage of this methodology is that the hybrid model can provide faster convergence and more precise prediction results in comparison with abductive networks algorithm. Extensive testing on the electrical load data of the Puget power utility in the USA confirms the validity of the proposed approach.     

Optimal Checkpoint Placement on Real-Time Tasks with Harmonic Periods

This paper presents an optimal checkpoint strategy for fault-tolerance in real-time systems where transient faults occur in Poisson distribution. In our environment, multiple real-time tasks with different deadlines and harmonic periods are scheduled in the system by rate-monotonic algorithm, and checkpoints are inserted at a constant interval in each task. When a fault is detected, the system carries out rollback to the latest checkpoint and re-executes tasks. The maximum number of re-executable checkpoints and an equation to check schedulability are derived, and the optimal number of checkpoints is selected to maximize the probability of completing all the tasks within their deadlines.     

Optimizing Linpack Benchmark on GPU-Accelerated Petascale Supercomputer

In this paper we present the programming of the Linpack benchmark on TianHe-1 system,the first petascale supercomputer system of China,and the largest GPU-accelerated heterogeneous system ever attempted before.A hybrid programming model consisting of MPI,OpenMP and streaming computing is described to explore the task parallel,thread parallel and data parallel of the Linpack.We explain how we optimized the load distribution across the CPUs and GPUs using the two-level adaptive method and describe the implementation in details.To overcome the low-bandwidth between the CPU and GPU communication,we present a software pipelining technique to hide the communication overhead.Combined with other traditional optimizations,the Linpack we developed achieved 196.7 GFLOPS on a single compute element of TianHe-1.This result is 70.1% of the peak compute capability,3.3 times faster than the result by using the vendor’s library.On the full configuration of TianHe-1 our optimizations resulted in a Linpack performance of 0.563 PFLOPS,which made TianHe-1 the 5th fastest supercomputer on the Top500 list in November,2009.     

An Effective Framework for Fast Expert Mining in Collaboration Networks: A Group-Oriented and Cost-Based Method

The growth of social networks in modern information systems has enabled the collaboration of experts at a scale that was unseen before.Given a task and a graph of experts where each expert possesses some skills,we tend to find an effective team of experts who are able to accomplish the task.This team should consider how team members collaborate in an effective manner to perform the task as well as how efficient the team assignment is,considering each expert has the minimum required level of skill.Here,we generalize the problem in multiple perspectives.First,a method is provided to determine the skill level of each expert based on his/her skill and collaboration among neighbors.Second,the graph is aggregated to the set of skilled expert groups that are strongly correlated based on their skills as well as the best connection among them.By considering the groups,search space is significantly reduced and moreover it causes to prevent from the growth of redundant communication costs and team cardinality while assigning the team members.Third,the existing RarestFirst algorithm is extended to more generalized version,and finally the cost definition is customized to improve the efficiency of selected team.Experiments on DBLP co-authorship graph show that in terms of efficiency and effectiveness,our proposed framework is achieved well in practice.     

Learning a hierarchical image manifold for Web image classification

Image classification is an essential task in content-based image retrieval.However,due to the semantic gap between low-level visual features and high-level semantic concepts,and the diversification of Web images,the performance of traditional classification approaches is far from users’ expectations.In an attempt to reduce the semantic gap and satisfy the urgent requirements for dimensionality reduction,high-quality retrieval results,and batch-based processing,we propose a hierarchical image manifold with novel distance measures for calculation.Assuming that the images in an image set describe the same or similar object but have various scenes,we formulate two kinds of manifolds,object manifold and scene manifold,at different levels of semantic granularity.Object manifold is developed for object-level classification using an algorithm named extended locally linear embedding(ELLE) based on intra-and inter-object difference measures.Scene manifold is built for scene-level classification using an algorithm named locally linear submanifold extraction(LLSE) by combining linear perturbation and region growing.Experimental results show that our method is effective in improving the performance of classifying Web images.