A combined CLP-MILP approach for scheduling commodities in a pipeline

This paper addresses the problem of developing an optimization model to aid the operational scheduling in a real-world pipeline scenario. The pipeline connects refinery and harbor, conveying different types of commodities (gasoline, diesel, kerosene, etc.). An optimization model was developed to determine pipeline scheduling with improved efficiency. This model combines constraint logic programming (CLP) and mixed integer linear programming (MILP) in a CLP-MILP approach. The proposed model uses decomposition strategies, continuous time representation, intervals that indicate time constraints (time windows), and a series of operational issues, such as the seasonal and hourly cost of electric energy (on-peak demand hours). Real cases were solved in a matter of seconds. The computational results have demonstrated that the model is able to define new operational points to the pipeline, providing significant cost savings. Indeed the CLP-MILP model is an efficient tool to aid operational decision-making within this real-world pipeline scenario.     

A model-based approach for multiple QoS in scheduling: from models to implementation

Meeting multiple Quality of Service (QoS) requirements is an important factor in the success of complex software systems. This paper presents an automated, model-based scheduler synthesis approach for scheduling application software tasks to meet multiple QoS requirements. As a first step, it shows how designers can meet deadlock-freedom and timeliness requirements, in a manner that (i) does not over-provision resources, (ii) does not require architectural changes to the system, and that (iii) leaves enough degrees of freedom to pursue further properties. A major benefit of our synthesis methodology is that it increases traceability, by linking each scheduling constraint with a specific pair of QoS property and underlying platform execution model, so as to facilitate the validation of the scheduling constraints and the understanding of the overall system behaviour, required to meet further QoS properties.     

A market-oriented hierarchical scheduling strategy in cloud workflow systems

A cloud workflow system is a type of platform service which facilitates the automation of distributed applications based on the novel cloud infrastructure. One of the most important aspects which differentiate a cloud workflow system from its other counterparts is the market-oriented business model. This is a significant innovation which brings many challenges to conventional workflow scheduling strategies. To investigate such an issue, this paper proposes a market-oriented hierarchical scheduling strategy in cloud workflow systems. Specifically, the service-level scheduling deals with the Task-to-Service assignment where tasks of individual workflow instances are mapped to cloud services in the global cloud markets based on their functional and non-functional QoS requirements; the task-level scheduling deals with the optimisation of the Task-to-VM (virtual machine) assignment in local cloud data centres where the overall running cost of cloud workflow systems will be minimised given the satisfaction of QoS constraints for individual tasks. Based on our hierarchical scheduling strategy, a package based random scheduling algorithm is presented as the candidate service-level scheduling algorithm and three representative metaheuristic based scheduling algorithms including genetic algorithm (GA), ant colony optimisation (ACO), and particle swarm optimisation (PSO) are adapted, implemented and analysed as the candidate task-level scheduling algorithms. The hierarchical scheduling strategy is being implemented in our SwinDeW-C cloud workflow system and demonstrating satisfactory performance. Meanwhile, the experimental results show that the overall performance of ACO based scheduling algorithm is better than others on three basic measurements: the optimisation rate on makespan, the optimisation rate on cost and the CPU time.     

A dynamic data replication strategy using access-weights in data grids

Data grids deal with a huge amount of data regularly. It is a fundamental challenge to ensure efficient accesses to such widely distributed data sets. Creating replicas to a suitable site by data replication strategy can increase the system performance. It shortens the data access time and reduces bandwidth consumption. In this paper, a dynamic data replication mechanism called Latest Access Largest Weight (LALW) is proposed. LALW selects a popular file for replication and calculates a suitable number of copies and grid sites for replication. By associating a different weight to each historical data access record, the importance of each record is differentiated. A more recent data access record has a larger weight. It indicates that the record is more pertinent to the current situation of data access. A Grid simulator, OptorSim, is used to evaluate the performance of this dynamic replication strategy. The simulation results show that LALW successfully increases the effective network usage. It means that the LALW replication strategy can find out a popular file and replicates it to a suitable site without increasing the network burden too much.

A two-phase approach to solve the synchronized bin–forklift scheduling problem

In this paper, we propose a two-phase approach to solve a combined routing and scheduling problem that occurs in the textile industry: fabrics are dyed by dye-jets and transported by forklifts. The objective is to minimize the cost of the unproductive activities, i.e., the dye-jet setup times and the forklift waiting time. The first phase solves an integer linear program to assign jobs (fabrics) to dye-jets while minimizing the setup cost; we compare an arc-based and a path-based formulation. The second phase uses a mixed-integer linear program for the dye-jet scheduling and both the routing and scheduling of forklifts. Experiments are performed on real data provided by a major multinational company, and larger test problems are randomly generated to assess the algorithm. The tests were conducted using Cplex 12.6.0 and a column generation solver. The numerical results show that our approach is efficient in terms of both solution quality and computational time.     

A co-classification approach to learning from multilingual corpora

We address the problem of learning text categorization from a corpus of multilingual documents. We propose a multiview learning, co-regularization approach, in which we consider each language as a separate source, and minimize a joint loss that combines monolingual classification losses in each language while ensuring consistency of the categorization across languages. We derive training algorithms for logistic regression and boosting, and show that the resulting categorizers outperform models trained independently on each language, and even, most of the times, models trained on the joint bilingual data. Experiments are carried out on a multilingual extension of the RCV2 corpus, which is available for benchmarking.     

A novel approach to visualize web anomaly attacks in pervasive computing environment

These days, a pervasive computing environment is a rapidly changing trend towards increasingly always-on connected computing devices in the convergence environment. In a pervasive computing environment, there are various multimedia web services and communications for various devices in order to provide interesting and invaluable information to users. Meanwhile, providing a wide variety of the web-based multimedia services and communications may cause various security threats and abnormal behaviors. In this paper, a multimedia visualization approach for pervasive computing environment is proposed which analyzes HTTP request and response header information to detect and visualize multimedia web attacks based on the Bayesian method. We conducted a few cases’ experiment for the verification of the proposed approach in a real environment. The experimental results such as web attack detection visualization, scanning and password attack visualization, and attacker’s position tracking visualization verify the usability of the proposed approach.     

A hybrid approach to interactive global illumination and soft shadows

We present a hybrid approach to simulate global illumination and soft shadows at interactive frame rates. The strengths of hardware-accelerated GPU techniques are combined with CPU methods to achieve physically consistent results while maintaining reasonable performance. The process of image synthesis is subdivided into multiple passes accounting for the different illumination effects. While direct lighting is rendered efficiently by rasterization, soft shadows are simulated using a novel approach combining the speed of shadow mapping and the accuracy of visibility ray tracing. A shadow refinement mask is derived from the result of the direct lighting pass and from a small number of shadow maps to identify the penumbral region of an area light source. This region is accurately rendered by ray tracing. For diffuse indirect illumination, we introduce radiosity photons to profit from the flexibility of a point-based sampling while maintaining the benefits of interpolation over scattered data approximation or density estimation. A sparse sampling of the scene is generated by particle tracing. An area is approximated for each point sample to compute the radiosity solution using a relaxation approach. The indirect illumination is interpolated between neighboring radiosity photons, stored in a multidimensional search tree. We compare different neighborhood search algorithms in terms of image quality and performance. Our method yields interactive frame rates and results consistent with path tracing reference solutions.     

Adaptive popularity-driven replica placement in hierarchical data grids

Data grids support access to widely distributed storage for large numbers of users accessing potentially many large files. Efficient access is hindered by the high latency of the Internet. To improve access time, replication at nearby sites may be used. Replication also provides high availability, decreased bandwidth use, enhanced fault tolerance, and improved scalability. Resource availability, network latency, and user requests in a grid environment may vary with time. Any replica placement strategy must be able to adapt to such dynamic behavior. In this paper, we describe a new dynamic replica placement algorithm, Popularity Based Replica Placement (PBRP), for hierarchical data grids which is guided by file “popularity”. Our goal is to place replicas close to clients to reduce data access time while still using network and storage resources efficiently. The effectiveness of PBRP depends on the selection of a threshold value related to file popularity. We also present Adaptive-PBRP (APBRP) that determines this threshold dynamically based on data request arrival rates. We evaluate both algorithms using simulation. Results for a range of data access patterns show that our algorithms can shorten job execution time significantly and reduce bandwidth consumption compared to other dynamic replication methods.     

An implementation of parallel file distribution in an agent hierarchy

PC grid is a cost-effective grid-computing platform that attracts users by allocating to their massively parallel applications as many desktop computers as requested. However, a challenge is how to distribute necessary files to remote computing nodes that may be unconnected to the same network file system, equipped with insufficient disk space to keep entire files, and even powered off asynchronously. Targeting PC grid, the AgentTeamwork grid-computing middleware deploys a hierarchy of mobile agents to remote desktops so as to launch, monitor, check-point, and resume a parallel and distributed computing job. To achieve high-speed file distribution, AgentTeamwork takes advantage of its agent hierarchy. The system partitions files into stripes at the tree root if they are random-access files, duplicates them at each tree level if they are shared among all remote nodes, fragments them into smaller messages if they are too large to relay to a lower tree level, aggregates such messages in a larger fragment if they are in transit to the same subtree, and returns output files to the user along multi-paths established within the tree. To achieve fault-tolerant file delivery, each agent periodically takes a snapshot of in-transit and on-memory file messages with its user job, and thus resumes them from the latest snapshot when they crash accidentally. This paper presents an implementation and its competitive performance of AgentTeamwork’s file-distribution algorithm including file partitioning, transfer, check-pointing, and consistency maintenance.

Performance-based dynamic loop scheduling in heterogeneous computing environments

With the rapid advance of computing technologies, it becomes more and more common to construct high-performance computing environments with heterogeneous commodity computers. Previous loop scheduling schemes were not designed for this kind of environments. Therefore, better loop scheduling schemes are needed to further increase the performance of the emerging heterogeneous PC cluster environments. In this paper, we propose a new heuristic for the performance-based approach to partition loop iterations according to the performance weighting of cluster/grid nodes. In particular, a new parameter is proposed to consider HPCC benchmark results as part of performance estimation. A heterogeneous cluster and grid were built to verify the proposed approach, and three kinds of application program were implemented for execution on cluster testbed. Experimental results show that the proposed approach performs better than the previous schemes on heterogeneous computing environments.     

Evaluation of gang scheduling performance and cost in a cloud computing system

Cloud Computing refers to the notion of outsourcing on-site available services, computational facilities, or data storage to an off-site, location-transparent centralized facility or “Cloud.” Gang Scheduling is an efficient job scheduling algorithm for time sharing, already applied in parallel and distributed systems. This paper studies the performance of a distributed Cloud Computing model, based on the Amazon Elastic Compute Cloud (EC2) architecture that implements a Gang Scheduling scheme. Our model utilizes the concept of Virtual Machines (or VMs) which act as the computational units of the system. Initially, the system includes no VMs, but depending on the computational needs of the jobs being serviced new VMs can be leased and later released dynamically. A simulation of the aforementioned model is used to study, analyze, and evaluate both the performance and the overall cost of two major gang scheduling algorithms. Results reveal that Gang Scheduling can be effectively applied in a Cloud Computing environment both performance-wise and cost-wise.     

An evolutionary simulation–optimization approach in solving parallel-machine scheduling problems – A case study

Parallel-machine scheduling research is one of the active fields in the past decade due to its increasing application. Due to the problem complexity, it is a general practice to find an appropriate heuristic rather than an optimal solution for the parallel-machine scheduling problem. The wirebonding workstation is the bottleneck in integrated-circuit packaging manufacturing. Effective scheduling is one of the key factors towards improving the efficiency of the wirebonding operations. The wirebonding scheduling problem is an equal (or identical) parallel-machine scheduling problem. The research solved the wirebonding scheduling problem by using an evolutionary simulation–optimization approach. Empirical results, benchmarked against lower bound solutions, showed the quality solutions of less than 2% deviation for a wide range of production scenarios. However, if the problem size were to increase, the proposed methodology might become computationally prohibitive, and this might well require further development if used to solve the identified problem in such circumstances.     

Learning implicit user interest hierarchy for context in personalization

To provide a more robust context for personalization, we desire to extract a continuum of general to specific interests of a user, called a user interest hierarchy (UIH). The higher-level interests are more general, while the lower-level interests are more specific. A UIH can represent a user’s interests at different abstraction levels and can be learned from the contents (words/phrases) in a set of web pages bookmarked by a user. We propose a divisive hierarchical clustering (DHC) algorithm to group terms (topics) into a hierarchy where more general interests are represented by a larger set of terms. Our approach does not need user involvement and learns the UIH “implicitly”. To enrich features used in the UIH, we used phrases in addition to words. Our experiment indicates that DHC with the Augmented Expected Mutual Information (AEMI) correlation function and MaxChildren threshold-finding method built more meaningful UIHs than the other combinations on average; using words and phrases as features improved the quality of UIHs.     

Language and Space: a two-level semantic approach based on principles of ontological engineering

An increasing number of applications for dialogue systems presuppose an ability to deal appropriately with space. Dialogues with assistance systems, intelligent mobility devices and navigation systems all commonly involve the use of spatial language. For smooth interaction, this spatial language cannot be interpreted ‘in the abstract’—it must instead be related directly to a user’s physical location, orientation, goals and needs and be embedded appropriately in a system’s interaction. This is far from straightforward. The situated interpretation of natural language concerning space, spatial relationships and spatial activities represents an unsolved challenge at this time. Despite extensive work on spatial language involving many disciplines, there are no generally accepted accounts that provide support for the kind of flexible language use observed in real human-human spatial dialogues. In this paper, I review some recent approaches to the semantics for natural language expressions concerning space in order to motivate a two-level semantic-based approach to the interpretation of spatial language. This draws on a new combination of natural language processing and principles of ontological engineering and stands as a foundation for more sophisticated and natural dialogue system behavior where spatial information is involved.     

A general approach to attribute reduction in rough set theory

The concept of a consistent approximation representation space is introduced. Many types of information systems can be treated and unified as consistent ap- proximation representation spaces. At the same time, under the framework of this space, the judgment theorem for determining consistent attribute set is established, from which we can obtain the approach to attribute reductions in information systems. Also, the characterizations of three important types of attribute sets (the core attribute set, the relative necessary attribute set and the unnecessary attribute set) are examined.     

A mobile-agent-based approach to software coordination in the HOOPE system

Software coordination is central to the construction of large-scale high-performance distributed applications with software services scattered over the decentralized Internet. In this paper, a new mobile-agent-based architecture is proposed for the utilization and coordination of geographically distributed computing resources. Under this architecture, a user application is built with a set of software agents that can travel across the network autonomously. These agents utilize the distributed resources and coordinate with each other to complete their task. This approach' s advantages include the natural expression and flexible deployment of the coordination logic, the dynamic adaptation to the network environment and the potential of better application performance. This coordination architecture, together with an object-oriented hierarchical parallel application framework and a graphical application construction tool, is implemented in the HOOPE environment, which provides a systematic support for the de     

Optimizing modulo scheduling to achieve reuse and concurrency for stream processors

Both reuse and concurrency are performance-critical for stream processors. When applying loop unrolling and software pipelining separately to stream-level loops, either reuse or concurrency or both may be inadequately exploited. In this paper, we optimize modulo scheduling to maximize stream reuse and improve concurrency for stream-level loops. The key insight is that an unrolled and software-pipelined stream-level loop could be described by a set of reuse equations. Guided by reuse equations, a reuse-aware modulo scheduling algorithm is developed to simultaneously optimize the two performance objectives, reuse, and concurrency, for a loop in a unified framework. Moreover, we describe a code generation algorithm to automatically produce the optimized loop from a given loop. The experimental results obtained on FT64 and by simulation demonstrate the effectiveness of the proposed approach.     

A particle swarm optimization and min–max-based workflow scheduling algorithm with QoS satisfaction for service-oriented grids

In service-orientated grids (SOG) environments, grid workflow schedulers play a critical role in providing quality-of-service (QoS) satisfaction for various end users (EUs) with diverse QoS objectives and optimization requirements. The EU requirements are not only many and conflicting, but also involve constraints of various degrees—loose, moderate or tight. However, most of the existing scheduling approaches violate EU constraints in tight situations and suffer inferior QoS optimization results. In this paper, a constraints-aware multi-QoS workflow scheduling strategy is proposed based on particle swarm optimization (PSO) and a proposed look-ahead heuristic (LAPSO) to improve performance in such situations. The algorithm selects the best scheduling solutions based on the proposed constraint-handling strategy. It hybridises PSO with a novel look-ahead mechanism based on a min–max heuristic, which deterministically improves the quality of the best solutions. Extensive simulation experiments have been carried out to evaluate the performance of the proposed approach. The simulation results show that the LAPSO algorithm guarantees satisfaction (0% violation) of the EU constraints even in tight situations. It also outperforms the comparison algorithm, with about 30% increase, in terms of cumulative QoS satisfaction of optimization requirements. In addition, the new scheme significantly reduces the CPU time by about 75% compared to the benchmark algorithm.     

Exploiting slack time for just-in-time scheduling in wireless sensor networks

We consider the problem of real-time data collection in wireless sensor networks, in which data need to be delivered to one or more sinks within end-to-end deadlines. To enhance performance with respect to end-to-end deadline miss ratio, existing approaches schedule packets by prioritizing them based on per-packet deadlines and other factors such as the distance to the sink. However, important factors affecting the end-to-end performance such as queuing delays and buffer overruns have largely been ignored in the existing real-time schemes. Packet prioritization by itself cannot assist with these issues, and may in fact, exacerbate them for real-time data collection, since many high priority packets may simultaneously contend for the constrained network resources. In sensor networks, where the channel bandwidth and buffer space are often quite limited, these issues can dramatically impact real-time performance. Based on this observation, we propose Just-in-Time Scheduling (JiTS) strategies where packets are judiciously delayed within their slack time to reduce contention and load balance the use of the network buffers. We explore several policies for delaying data packets at different intermediate nodes considering potential contention. In addition, we also show that the routing protocol has a significant impact on real-time performance. In particular, shortest path routing leads to considerably better performance than geographic forwarding, which is often used for real-time data transmission in wireless sensor networks. Using an extensive simulation study, we demonstrate that JiTS can significantly improve the deadline miss ratio and packet drop ratio compared to two state-of-the-art approaches for real-time packet delivery for sensor networks (RAP and SPEED) under various scenarios. Notably, JiTS requires neither lower layer (e.g., MAC layer) support nor synchronization among the sensor nodes.