Using new scheduling heuristics based on resource consumption information for increasing throughput on rule‐based spam filtering systems

The large increase of spam deliveries since the first half of 2013 entailed hard to solve troubles in spam filters. In order to adequately fight spam, the throughput of spam filtering platforms should be necessarily increased. In this context, and taking into consideration the widespread utilization of rule‐based filtering frameworks in the spam filtering domain, this work proposes three novel scheduling strategies for optimizing the time needed to classify new incoming e‐mails through an intelligent management of computational resources depending on the Central Processing Unit (CPU) usage and Input/Output (I/O) delays. In order to demonstrate the suitability of our approaches, we include in our experiments a comparative study in contrast to other successful heuristics previously published in the scientific literature. Results achieved demonstrated that one of our alternative heuristics allows time savings of up to 10% in message filtering, while keeping the same classification accuracy. Copyright © 2015 John Wiley & Sons, Ltd.     

Load‐balanced and locality‐aware scheduling for data‐intensive workloads at extreme scales

Data‐driven programming models such as many‐task computing (MTC) have been prevalent for running data‐intensive scientific applications. MTC applies over‐decomposition to enable distributed scheduling. To achieve extreme scalability, MTC proposes a fully distributed task scheduling architecture that employs as many schedulers as the compute nodes to make scheduling decisions. Achieving distributed load balancing and best exploiting data locality are two important goals for the best performance of distributed scheduling of data‐intensive applications. Our previous research proposed a data‐aware work‐stealing technique to optimize both load balancing and data locality by using both dedicated and shared task ready queues in each scheduler. Tasks were organized in queues based on the input data size and location. Distributed key‐value store was applied to manage task metadata. We implemented the technique in MATRIX, a distributed MTC task execution framework. In this work, we devise an analytical suboptimal upper bound of the proposed technique, compare MATRIX with other scheduling systems, and explore the scalability of the technique at extreme scales. Results show that the technique is not only scalable but can achieve performance within 15% of the suboptimal solution. Copyright © 2015 John Wiley & Sons, Ltd.     

Controlled tracking in urban terrain: Closing the loop

We investigate the challenging problem of integrating detection, signal processing, target tracking, and adaptive waveform scheduling with lookahead in urban terrain. We propose a closed‐loop active sensing system to address this problem by exploiting three distinct levels of diversity: (1) spatial diversity through the use of coordinated multistatic radars; (2) waveform diversity by adaptively scheduling the transmitted waveform; and (3) motion model diversity by using a bank of parallel filters matched to different motion models. Specifically, at every radar scan, the waveform that yields the minimum trace of the one‐step‐ahead error covariance matrix is transmitted; the received signal goes through a matched‐filter, and curve fitting is used to extract range and range‐rate measurements that feed the LMIPDA‐VSIMM algorithm for data association and filtering. Monte Carlo simulations demonstrate the effectiveness of the proposed system in an urban scenario contaminated by dense and uneven clutter, strong multipath, and limited line‐of‐sight.     

An optimization approach for decentralized QoS‐based scheduling based on utility and pricing in Grid computing

This paper presents an optimization approach for decentralized Quality of Service (QoS)‐based scheduling based on utility and pricing in Grid computing. The paper assumes that the quality dimensions can be easily formulated as utility functions to express quality preferences for each task agent. The utility values are calculated by the user‐supplied utility function that can be formulated with the task parameters. The QoS constraint Grid resource scheduling problem is formulated into a utility optimization problem. The QoS‐based Grid resource scheduling optimization is decomposed into two subproblems by applying the Lagrangian method. In the Grid, a Grid task agent acts as a consumer paying for the Grid resource and the resource providers receive profits from task agents. A pricing‐based QoS scheduling algorithm is used to perform optimally decentralized QoS‐based resource scheduling. The experiments investigate the effect of the QoS metrics on the global utility and compare the performance of the proposed algorithm with other economical Grid resource scheduling algorithms. Copyright © 2006 John Wiley & Sons, Ltd.     

Context‐aware scheduling in MapReduce: a compact review

It is a fact that the attention of research community in computer science, business executives, and decision makers is drastically drawn by big data. As the volume of data becomes bigger, it needs performance‐oriented data‐intensive processing frameworks such as MapReduce, which can scale computation on large commodity clusters. Hadoop MapReduce processes data in Hadoop Distributed File System as jobs scheduled according to YARN fair scheduler and capacity scheduler. However, with advancement and dynamic changes in hardware and operating environments, the performance of clusters is greatly affected. Various efforts in literature have been made to address the issues of heterogeneity (i.e., clusters consisting of virtual machines and machines with different hardware), network communication, data locality, better resource utilization, and run‐time scheduling. In this paper, we present a survey to discuss various research efforts made so far to improve Hadoop MapReduce scheduling. We classify scheduling algorithms and techniques proposed in the literature so far based on their addressing areas and present a taxonomy. Furthermore, we also discuss various aspects of open issues and challenges in the scheduling of MapReduce to improve its performance. Copyright © 2015 John Wiley & Sons, Ltd.     

Scheduling model‐to‐model transformations with continuations

Model transformations are at the heart of model‐driven engineering because they allow the automation of diverse kinds of model manipulations. Transformation scheduling is a key issue in the design and implementation of many transformation languages. This paper reports our results using continuations as the underlying technique for building a scheduling mechanism implicitly driven by data dependence among transformation rules. To support our experiments, we have built a proof‐of‐concept model transformation language, which is also reported here. First, we motivate the problem by analyzing the scheduling mechanism of current model transformation languages. Then, we introduce the notion of continuation, showing its applicability to model transformations. Afterwards, we present our approach, notably explaining how dependence is specified and giving the scheduling algorithm. We also analyze the lazy resolution of rules and how to deal with collection operations. The approach is validated by an implementation that targets the Java Virtual Machine and by running of the performance benchmarks that show its efficiency and scalability. Besides, we discuss how it can be applied to other existing transformation languages and present several applicability scenarios. Copyright © 2013 John Wiley & Sons, Ltd.     

Near‐optimal dynamic priority scheduling strategy for instance‐intensive business workflows in cloud computing

Utilization of cloud computing resources has made a fast growth in e‐business. Business and government agencies often need to handle large volume of service requests, the so‐called instance‐intensive business processes in a constrained period. On‐time completion for instance‐intensive business processes within the constrained time is a very important issue. In the past few years, traditional optimal task scheduling has been well researched and proven to be a nondeterministic polynomial (NP) time–complete problem. So many heuristic and metaheuristic algorithms are put forward to solve the issue with near‐optimal solutions. However, most of them just treat a single workflow instance as a multistep task without considering that steps within a task can be different types of activities. To explain multistep features of business workflows, a typical motivating instance‐intensive business example of security exchange and a multistep scheduling model for business workflows are introduced in this paper. Then our near‐optimal dynamic priority scheduling (DPS) strategy is proposed on the basis of the idea of Min‐Min heuristic algorithm and greedy philosophy. Compared to the first come first served and constrained Min‐Min by makespan and standard deviation, DPS can make a more optimized choice in each round of scheduling towards overall outcome. To show the effectiveness of DPS, theoretical minimum execution time (MET_theory) is used as a benchmark for evaluation based on simulation. The results show that the ratios between MET_theory and DPS are more than 98.5% by scheduling different orders of magnitude tasks from 1000 to 1 000 000. In particular, the ratio between MET_theory and DPS is nearly 99.9% with 1 000 000 tasks, which means that our DPS can get the near‐optimal result when scheduling large number of tasks.     

开放式实时环境中的集成调度框架

开放式实时环境中的多类型实时任务并存的情况给实时调度机制带来了新的需求和挑战．提出了一种适用于开放式实时环境的3层集成调度框架，它基于服务器方式的实时调度理论，把5种既有相关性又有功能互补特征的服务器有机的集成到统一的调度机制中．可支持多种类型的实时任务及非实时任务共存于同一系统的混合调度，并且根据它们的属性自动选择最合适的服务器与之搭配．相对于现有方法，该调度框架可支持的调度对象类型更广泛，易于进行服务质量控制，并且有良好的可扩展性．因此，更适合于开放式实时环境．     

Bandwidth‐aware divisible task scheduling for cloud computing

Task scheduling is a fundamental issue in achieving high efficiency in cloud computing. However, it is a big challenge for efficient scheduling algorithm design and implementation (as general scheduling problem is NP‐complete). Most existing task‐scheduling methods of cloud computing only consider task resource requirements for CPU and memory, without considering bandwidth requirements. In order to obtain better performance, in this paper, we propose a bandwidth‐aware algorithm for divisible task scheduling in cloud‐computing environments. A nonlinear programming model for the divisible task‐scheduling problem under the bounded multi‐port model is presented. By solving this model, the optimized allocation scheme that determines proper number of tasks assigned to each virtual resource node is obtained. On the basis of the optimized allocation scheme, a heuristic algorithm for divisible load scheduling, called bandwidth‐aware task‐scheduling (BATS) algorithm, is proposed. The performance of algorithm is evaluated using CloudSim toolkit. Experimental result shows that, compared with the fair‐based task‐scheduling algorithm, the bandwidth‐only task‐scheduling algorithm, and the computation‐only task‐scheduling algorithm, the proposed algorithm (BATS) has better performance. Copyright © 2012 John Wiley & Sons, Ltd.     

多核环境下基于图模型的实时规则调度方法

安全攸关反应式系统的核心要求是：必须在指定时间期限内完成对外部事件的检测和目标事件的响应,否则会产生灾难性的后果.随着安全攸关反应式系统对智能化需求的日益增加,将规则推理应用于这类系统成为必然趋势.规则调度是保证规则推理硬实时约束的关键.为此,提出了一种基于图模型的实时规则调度方法（graph-based real-time rule scheduling,简称GBRRS）.该方法对基于事件图的实时规则推理过程进行建模,提出了基于图的端到端推理任务模型,并给出了端到端推理任务的调度算法,保证了规则调度的安全性.采用模拟实验对GBRRS方法进行了验证,实验结果表明,与DM-EDF方法（通过直接映射把规则上的推理操作转成推理任务后,用全局EDF算法对其进行调度的方法）相比,GBRRS方法在规则调度成功率上平均高出13%~15%,且在规则集的平均负载较高时,仍保持着80%以上的调度成功率.     

Loosely coordinated coscheduling in the context of other approaches for dynamic job scheduling: a survey

Loosely coordinated (implicit/dynamic) coscheduling is a time‐sharing approach that originates from network of workstations environments of mixed parallel/serial workloads and limitedsoftware support. It is meant to be an easy‐to‐implement and scalable approach. Considering that the percentage of clusters in parallel computing is increasing and easily portable software is needed, loosely coordinated coscheduling becomes an attractive approach for dedicated machines. Loose coordination offers attractive features as a dynamic approach. Static approaches for local job scheduling assign resources exclusively and non‐preemptively. Such approaches still remain beyond the desirable resource utilization and average response times. Conversely, approaches for dynamic scheduling of jobs can preempt resources and/or adapt their allocation. They typically provide better resource utilization and response times. Existing dynamic approaches are full preemption with checkpointing, dynamic adaptation of node/CPU allocation, and time sharing via gang or loosely coordinated coscheduling. This survey presents and compares the different approaches, while particularly focusing on the less well‐explored loosely coordinated time sharing. The discussion particularly focuses on the implementation problems, in terms of modification of standard operating systems, the runtime system and the communication libraries. Copyright © 2005 John Wiley & Sons, Ltd.     

Scheduling communication in multithreaded programs: experimental results

When the critical path of a communication session between end points includes the actions of operating system kernels, there are attendant overheads. Along with other factors, such as functionality and flexibility, such overheads motivate and favor the implementation of communication protocols in user space. When implemented with threads, such protocols may hold the key to optimal communication performance and functionality. Based on implementations of reliable user‐space protocols supported by a threads framework, we focus on our experiences with internal threads' scheduling techniques and their potential impact on performance. We present scheduling strategies that enable threads to do both application‐level and communication‐related processing. With experiments performed on a Sun SPARC‐5 LAN environment, we show how different scheduling strategies yield different levels of application‐processing efficiency, communication latency and packet‐loss. This work forms part of a larger study on the implementation of multiple thread‐based protocols in a single address space, and the benefits of coupling protocols with applications. Copyright © 2005 John Wiley & Sons, Ltd.     

Discrete‐time network‐based control under scheduling and actuator constraints

This paper is concerned with the solution bounds for discrete‐time networked control systems via delay‐dependent Lyapunov–Krasovskii methods. Solution bounds are widely used for systems with input saturation caused by actuator saturation or by the quantizers with saturation. The time‐delay approach has been developed recently for the stabilization of continuous‐time networked control systems under the round‐robin protocol and under a weighted try‐once‐discard protocol, respectively. Actuator saturation has not been taken into account. In the present paper, for the first time, the time‐delay approach is extended to the stability analysis of the discrete‐time networked control systems under both scheduling protocols and actuators saturation. The communication delays are allowed to be larger than the sampling intervals. A novel Lyapunov‐based method is presented for finding the domain of attraction. Polytopic uncertainties in the system model can be easily included in our analysis. The efficiency of the time‐delay approach is illustrated on the example of a cart–pendulum system. Copyright © 2014 John Wiley & Sons, Ltd.     

网格计算中时间和费用限制下的任务调度算法

在网格计算中，一个好的管理系统应有出色的算法来调度用户提交的任务。前人基于不同目的已设计了各种调度算法，但通常不易施行：重点考虑工作完成时间和所耗费用，给出确定的模型以调度独立的任务。通过恰当的建模，所求解的问题将演变成0—1规划问题，而这种问题清晰明了，且有现成算法求解，从而使得时间和费用最小化。给出一个例子验证了该算法的有效性。     

Time‐selective integrated scheduling algorithm with backtracking adaptation strategy

Currently, integrated scheduling algorithms schedule processes using fixed rules, making it difficult to balance serial and parallel processes in product craftsmanship trees while conducting complicated single product scheduling. To solve this problem, we propose a time‐selective integrated scheduling algorithm with a backtracking adaptation (TISAWBA) strategy. The proposed process sequence sorting strategy aims to determine process scheduling sequences based on the overall structure of the machining craftsmanship tree. The proposed time‐selective scheduling strategy aims to select the process portfolio with the minimum total elapsed time for machining from a process portfolio set based on craftsmanship tree structure. The proposed backtracking adaptation strategy conducts backtracking adaptation to find process portfolios having a total elapsed machining time greater than the “scheduling reference time.” Finally, illustrative use cases verify that TISAWBA guarantees parallel processing for parallel processes and elevates the proximity of serial processes, generating optimized integrated scheduling results.     

Pragmatic integrated scheduling for clustered VLIW architectures

Clustered architecture processors are preferred for embedded systems because centralized register file architectures scale poorly in terms of clock rate, chip area, and power consumption. Scheduling for clustered architectures involves spatial concerns (where to schedule) as well as temporal concerns (when to schedule). Various clustered VLIW configurations, connectivity types, and inter‐cluster communication models present different performance trade‐offs to a scheduler. The scheduler is responsible for resolving the conflicting requirements of exploiting the parallelism offered by the hardware and limiting the communication among clusters to achieve better performance. In this paper, we describe our experience with developing a pragmatic scheme and also a generic graph‐matching‐based framework for cluster scheduling based on a generic and realistic clustered machine model. The proposed scheme effectively utilizes the exact knowledge of available communication slots, functional units, and load on different clusters as well as future resource and communication requirements known only at schedule time. The proposed graph‐matching‐based framework for cluster scheduling resolves the phase‐ordering and fixed‐ordering problem associated with earlier schemes for scheduling clustered VLIW architectures. The experimental evaluation in the context of a state‐of‐art commercial clustered architecture (using real‐world benchmark programs) reveals a significant performance improvement over the earlier proposals, which were mostly evaluated using compiled simulation of hypothetical clustered architectures. Our results clearly highlight the importance of considering the peculiarities of commercial clustered architectures and the hard‐nosed performance measurement. Copyright © 2007 John Wiley & Sons, Ltd.     

Multi‐organization scheduling approximation algorithms

In this paper we consider the problem of scheduling on computing platforms composed of several independent organizations, known as the Multi‐Organization Scheduling Problem (MOSP). Each organization provides both resources and jobs and follows its own objectives. We are interested in the best way to minimize the makespan on the entire platform when the organizations behave in a selfish way. We study the complexity of the MOSP problem with two different local objectives—makespan and average completion time—and show that MOSP is strongly NP‐Hard in both cases. We formally define a selfishness notion, by means of restrictions on the schedules. We prove that selfish behavior imposes a lower bound of 2 on the approximation ratio for the global makespan. We present various approximation algorithms of ratio 2 which validate selfishness restrictions. These algorithms are experimentally evaluated through simulation, exhibiting good average performances and presenting good fairness to organizations' local objectives. Copyright © 2011 John Wiley & Sons, Ltd.     

A new task scheduling method for distributed programs that require memory management

In parallel and distributed applications, it is very likely that object‐oriented languages, such as Java and Ruby, and large‐scale semistructured data written in XML will be employed. However, because of their inherent dynamic memory management, parallel and distributed applications must sometimes suspend the execution of all tasks running on the processors. This adversely affects their execution on the parallel and distributed platform. In this paper, we propose a new task scheduling method called CP/MM (Critical Path/Memory Management) which can efficiently schedule tasks for applications requiring memory management. The underlying concept is to consider the cost due to memory management when the task scheduling system allocates ready (executable) coarse‐grain tasks, or macro‐tasks, to processors. We have developed three task scheduling modules, including CP/MM, for a task scheduling system which is implemented on a Java RMI (Remote Method Invocation) communication infrastructure. Our experimental results show that CP/MM can successfully prevent high‐priority macro‐tasks from being affected by the garbage collection arising from memory management, so that CP/MM can efficiently schedule distributed programs whose critical paths are relatively long. Copyright © 2005 John Wiley & Sons, Ltd.     

Scheduling time‐critical requests for multiple data objects in on‐demand broadcast

On‐demand broadcast is an effective data dissemination approach in mobile computing environments. Most of the recent studies on on‐demand data broadcast assume that clients request only a single‐data‐object at a time. This assumption may not be practical for the increasingly sophisticated mobile applications. In this paper, we investigate the scheduling problem of time‐critical requests for multiple data objects in on‐demand broadcast environments and observe that existing scheduling algorithms designed for single‐data‐object requests perform unsatisfactorily in this new setting. Based on our analysis, we propose new algorithms to improve the system performance. Copyright © 2010 John Wiley & Sons, Ltd.     

An energy‐aware scheduling algorithm for divisible loads in a bus network

The schedule of divisible loads is one of the most typical problems in the research and application of parallel and distributed systems. For these large‐scale systems, the energy consumption problem has drawn great attention in recent years because of falling hardware costs and the growing concern of energy costs. In computing‐intensive systems, energy is primarily consumed by CPUs, and dynamic voltage‐frequency scaling technology is capable of adjusting CPUs' speed as well as saving energy. In this paper, we focus on computing‐intensive applications and study the energy‐aware scheduling problem for divisible loads in a bus network. The energy‐speed model is introduced to characterize the problem based on dynamic voltage scaling, and the energy‐aware scheduling problem is analyzed in the application layer above the operating system. The problem can be formulated mathematically as a nonlinear programming problem, and the solution is achieved using the Lagrange multiplier method under Kuhn–Tucker conditions. Based on the analytical results, an energy‐aware scheduling scheme called ENERG for divisible loads is presented. Finally, the energy‐aware scheme is compared with two other schemes to show the effectiveness and efficiency of the energy savings of our algorithm. Additionally, the experimental results illustrate the influence of network transmission delay on energy consumption. Copyright © 2015 John Wiley & Sons, Ltd.