Scheduling multiple data visualization query workloads on a shared memory machine

Multiple query scheduling for distributed semantic caches

PurposeThe purpose of this paper is to provide insights into the environment, resources and surroundings factors to develop a system dynamic model of dynamic project scheduling that aids on-time project delivery by reducing the project delay for the road construction industry in New Zealand (NZ).Design/methodology/approachThis study adopted narrative inquiry methodology that involved semi-structured interviews (SSI)/expert opinion and systematic literature review (SLR) data to determine the environment, resources and surroundings factors to develop a system dynamic model of dynamic project scheduling that aids on-time project delivery by reducing the project delay for the road construction industry in NZ. The data were analysed by using descriptive analysis, Likert scale and thematic analysis techniques to understand the relationship of these factors to propose a system dynamic model.FindingsThis study concludes that weather, pandemic, material, geotechnical and disaster factors highly influence while other factors such as equipment shortage, breakdown, design error, labour and event had mixed impact on the dynamic scheduling (DS) that aids on-time project delivery. The proposed system dynamic model can enhance the understanding of factors affecting DS.Research limitations/implicationsSLR is limited to English literature. The limitations of an SSI and a small sample size are acknowledged.Practical implicationsThe proposed model can reduce the uncertainty and scheduling errors during the planning phase and aid in the lesser scheduling modification during the execution phase. In practice, this study will be helpful for road contractors to understand environment, surroundings and resource in-control and out-of-control factors, overcome road construction delays, reduce cost, aid in stakeholder management and sustainable development.Social implicationsThe inclusion of environment, resource and surroundings factors in force majeure clauses will bring an understanding between contracting parties and in turn reduce disputes and delays and help social causes such as on-time infrastructure delivery.Originality/valueFor the first time in a road construction, dynamic project scheduling model that collectively included and linked environment, resource, and surroundings factors to determine the in-control and out-of-control factors for an organisation is proposed. The novelty in the paper is provided by the inclusion of the events, disasters, and pandemics influence on DS in the NZ road construction industry for the first time.

When the current method is used to schedule railway transportation, the scheduling takes a long time, and the difference between the obtained scheduling result and the expected one is large, and there is a problem of low scheduling efficiency and poor validity. A dynamic scheduling method based on pre-classification is proposed, which pre-classifies railway transportation resources, improves the utilization rate of railway transportation resources, and shortens the time required for scheduling. The execution time is taken as the optimization target, and the railway transportation dynamic scheduling model is constructed based on the pre-classification results. The genetic algorithm is used to solve the dynamic scheduling model of railway transportation, and the dynamic scheduling of railway transportation is realized. The experimental results show that the proposed method takes less time for railway transportation scheduling, and requires less time to perform tasks after scheduling. The proposed method has high scheduling efficiency and good effectiveness.

PurposeBasic project control through traditional methods is not sufficient to manage the majority of real-time events in most construction projects. The purpose of this paper is to propose a Dynamic Scheduling (DS) model that utilizes multi-objective optimization of cost, time, resources and cash flow, throughout project construction.Design/methodology/approachUpon reviewing the topic of DS, a worldwide internet survey with 364 respondents was conducted to define end-user requirements. The model was formulated and solution algorithms discussed. Verification was reported using predefined problem sets and a real-life case. Validation was performed via feedback from industry experts.FindingsThe need for multi-objective dynamic software optimization of construction schedules and the ability to choose among a set of optimal alternatives were highlighted. Model verification through well-known test cases and a real-life project case study showed that the model successfully achieved the required dynamic functionality whether under the small solved example or under the complex case study. The model was validated for practicality, optimization of various DS schedule quality gates, ease of use and software integration with contemporary project management practices.Practical implicationsOptimized real-time scheduling can provide better resources management including labor utilization and cost efficiency. Furthermore, DS contributes to optimum materials procurement, thus minimizing waste.Social implicationsOptimized real-time scheduling can provide better resources management including labor utilization and cost efficiency. Furthermore, DS contributes to optimum materials procurement, thus minimizing waste.Originality/valueThe paper illustrates the importance of DS in construction, identifies the user needs and overviews the development, verification and validation of a model that supports the generation of high-quality schedules beneficial to large-scale projects.

In the steelmaking-continuous casting (SCC) production process, machine breakdown is one of the most common disturbances which may make the current schedule unrealisable. Existing rescheduling models for machine breakdown only employ one constraint that charges cannot be processed on this machine in its failure period. However, this method is effective for steelmaking furnace breakdown and refining furnace breakdown but invalid for continuous caster breakdown. Due to the production characteristics of continuous caster, reallocating a casting order and a continuous caster for each unfinished charge on the broken down continuous caster is necessary before making a new schedule. Different reallocation strategies have different impacts on charge’s processing time and processing stage route in the dynamic scheduling process. Therefore, SCC dynamic scheduling for the continuous caster breakdown is different from the other machines. In this paper, the impacts of these strategies are studied, and a dynamic scheduling model which can be used to generate a new schedule for each strategy is built. To obtain a high-quality solution in acceptable computational time for this model with NP-hard feature, a hybrid algorithm featuring a genetic algorithm combined with a general variable neighbourhood search is developed based on the problem-specific characteristics. Computational experiments on practical production data show that the proposed rescheduling method is effective for SCC dynamic scheduling with continuous caster breakdown.

In the Vehicle Ad-hoc NETwork (VANET), moving vehicles organize into a mobile wireless Ad-hoc network to share online traffic information. Each vehicle can issue a declarative query for aggregating the traffic information from others in order to facilitate the navigation and avoid traffic jam. Existing query methods suffer from high latency, incomplete results, and large messages due to the movement of the vehicles in VANET. In this paper, we propose an adaptive query evaluation method based on the road network. In order to overcome the problems incurred by the movement, a relative static query evaluation plan is constructed based on the road network, and each vehicle can participate in the query evaluation plan autonomously. We also introduce control messages to notify the changed location of the query originator to other vehicles involved in the evaluation plan. In addition, we propose an one time message transferring based results collecting method to reduce the message cost. The optimization over the multiple queries is also discussed to reduce the messages further. We evaluate the performance of our method by extensive simulations. Experimental results show that our method can provide complete results within a short response time and small traffic overhead.

In the formulation of single process fault free intelligent RGV dynamic scheduling strategy, a double objective optimization model is established to improve the efficiency of RGV and shorten the production time of single material. For the solution of the model, firstly, the shortest moving path of RGV is determined according to Greedy algorithm and Tabu search algorithm, and the loading and unloading sequence of RGV is determined according to the short job priority scheduling algorithm. For intelligent RGV dynamic scheduling problem with single process failure, The failure time is determined by the cumulative failure probability model of the system, According to the period of the fault point, after determining the fault CNC, a new dynamic scheduling model is obtained according to the no fault scheduling strategy. Finally, the model and the algorithm are verified by the data.

The increasing frequency of space launch missions underscores their vital role in national security and technological advancement. These missions involve a complex set of tasks, each reliant on limited resources and subject to various constraints. Traditionally, mission planning has depended on the expertise of personnel, leading to potential biases and inefficiencies. Thus, there is a critical need for automated methods that facilitate objective and rapid task planning, minimizing human error. Modeling the task planning for these missions poses significant challenges due to the vast search space and multiple resource constraints. As the complexity of tasks and their inter-dependencies increase, finding optimal solutions becomes more difficult. To address this, this paper introduces a dynamic resource intelligent scheduling model that incorporates executable task lists, resource constraints, and the progress of ongoing tasks. Utilizing advanced optimization techniques such as genetic algorithms, simulated annealing, and particle swarm optimization, our model effectively identifies solutions consistent with manual calculations. This innovative approach enhances task scheduling efficiency and ensures optimal outcomes, marking a significant advancement in space launch mission planning.

We present a new framework for indexing, locating and ranking XML documents based on content and structural synopses extracted from the documents. Instead of indexing each single element or term in a document, we extract a structural summary and a small number of data synopses from the document, which are indexed in an efficient way suitable for query evaluation. Our query language is XPath extended with full-text search. The result of query evaluation is a ranked list of document locations that best match the query. We propose a novel aggregated ranking scheme, which is integrated into the query evaluation to score the documents based on those data synopses. Our experimental evaluation shows that our indexing scheme outperforms the standard XML indexing scheme based on inverted lists and our ranking scheme is effective in terms of precision and recall.

Aiming at the problem of limited resources and operational time combat air defense combat, naval combat formation analysis dynamic resources scheduling needs, on the basis of the establishment of ship dynamic scheduling model of warship formation combat resources dynamic combat formation resource scheduling, the operational resources and operational objectives, relationship between reasonable allocation of resources to play the warship formation combat operations the maximum efficiency. The design of three kinds of air defense command models: centralized platform free attack mode, command mode, request attack mode of air defense strategy, put forward the dynamic scheduling hybrid algorithm for solving the model, according to the naval fleet air defense combat scenario example for simulation. The simulation results show that the air defense strategy with the request attack mode can have effects on multi-target attacks under the shared combat situation, and the hybrid algorithm based on combat resource dynamic scheduling can meet the real-time operational requirements.

There has been a good deal of progress made recently towards the efficient parallelization of individual phases of single queries in multiprocessor database systems. In this paper we devise and evaluate a number of scheduling algorithms designed to handle multiple parallel queries. One of these algorithms emerges as a clear winner. This algorithm is hierarchical in nature: In the first phase, a good quality precedence-based schedule is created for each individual query and each possible number of processors. This component employs dynamic programming. In the second phase, the results of the first phase are used to create an overall schedule of the full set of queries. This component is based on previously published work on nonprecedence-based malleable scheduling. Even though the problem we are considering is NP-hard in the strong sense, the multiple query schedules generated by our hierarchical algorithm are seen experimentally to achieve results which are close to optimal.

An important issue arising from XML query relaxation is how to efficiently search the top-k best answers from a large number of XML data sources, while minimizing the searching cost, i.e., finding the k matches with the highest computed scores by only traversing part of the documents. This paper resolves this issue by proposing a bound-threshold based scheduling strategy. It can answer a top-k XML query as early as possible by dynamically scheduling the query over XML documents. In this work, the total amount of documents that need to be visited can be greatly reduced by skipping those documents that will not produce the desired results with the bound-threshold strategy. Furthermore, most of the candidates in each visited document can also be pruned based on the intermediate results. Most importantly, the partial results can be output immediately during the query execution, rather than waiting for the end of all results to be determined. Our experimental results show that our query scheduling and processing strategies are both practical and efficient.

MQO is a distributed multiple query processing middleware that can use resources available on the Grid to optimize query processing for data analysis and visualization applications. It does so by introducing one or more proxies that act as front-ends to a collection of backend servers. The basic idea behind this architecture is active semantic caching, whereby queries can leverage available cached results in the proxy either directly or through transformations. While this approach has been shown to speed up query evaluation under multi-client workloads, the caching infrastructure in the backend servers is not used well for query processing. Because this collective caching infrastructure scales with the number of servers, it is an important asset. In this paper, we describe a distributed multidimensional indexing scheme that enables the proxy to directly consider the cache contents available at the backend servers for query planning and scheduling. This approach is shown to produce better query plans and faster query response times as we experimentally demonstrate.

Complex event processing (CEP) enables reactive and predictive applications through the continuous evaluation of queries over streams of event data. In a network of event sources, efficient query evaluation is achieved through distribution: Queries are split into operators (query decomposition), which are then assigned to some of the nodes (operator placement). Yet, existing solutions limit the decomposition to the operator hierarchy of a query, ignoring possible rewritings of it, and place each operator at exactly one node in the network. That neglects optimizations based on pattern composition through multiple queries as results are always gathered at a single sink node. In this paper, we propose a new evaluation model for CEP, coined Multi-Sink Evaluation (MuSE) graphs. It incorporates arbitrary projections of queries for distribution and assigns them to potentially many nodes. We prove correctness of query evaluation with MuSE graphs and provide a cost model to assess its efficiency. Since the construction of cost-optimal MuSE graphs is intractable, we present an approximation algorithm and several pruning trategies. Our evaluation results show that MuSE graphs reduce network transmission costs by up to three orders of magnitude over baseline strategies.

An effective exploitation of distributed computing environments to improve the performance of large advanced database applications requires parallel query processing techniques. This is because parallel query execution can be achieved by operations of pipelining and data partitioning, as well as independent subqueries. In order to develop efficient parallel query services in distributed environments, we present a query optimization, a dynamic parallel query scheduling algorithm, and an implementation mechanism for querying distributed objects. Our proposal has four important features. First, the query schedule dynamically schedules independent subqueries in parallel. Second, distributed processors are allowed to execute independent subqueries in random. Third, it doesn't limit the number of processors to deal with parallel query processing in a network based computing environment. Fourth, it provides simple operations to synchronize parallel query programs. We give the query optimization for parallel processing, show an algorithm of dynamic parallel query scheduling, describe an implementation method for the parallel query processing in a network-based distributed computing environment, and present the design of communication and synchronization for the parallel query processing.