Thanks to the emerging 3D integration technology, The multiprocessor system on chips （MPSoCs） can now integrate more IP cores on chip with improved energy efficiency. However, several severe challenges also rise up ...
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Thanks to the emerging 3D integration technology, The multiprocessor system on chips （MPSoCs） can now integrate more IP cores on chip with improved energy efficiency. However, several severe challenges also rise up for 3D ICs due to the die-stacking architecture. Among them, power supply noise becomes a big concern. In the paper, we investigate power supply noise （PSN） interactions among different cores and tiers and show that PSN variations largely depend on task assignments. On the other hand, high integration density incurs a severe thermal issue on 3D ICs. In the paper, we propose a novel taskscheduling framework considering both the PSN and the thermal issue. It mainly consists of three parts. First, we extract current stimuli of running tasks by analyzing their power traces derived from architecture level simulations. Second, we develop an efficient power delivery network （PDN） solver to evaluate PSN magnitudes efficiently. Third, we propose a heuristic algorithm to solve the formulated taskscheduling problem. Compared with the state-of-the-art task assignment algorithm, the proposed method can reduce PSN by 12% on a 2 × 2 × 2 3D MPSoCs and by 14% on a 3 × 3 × 3 3D MPSoCs. The end-to-end task execution time also improves as much as 5.5% and 7.8% respectively due to the suppressed PSN.
Satellite transmission scheduling problem (STSP) is an important part of satellite mission planning and one of the bottlenecks in satellite development. This paper established a mathematical model for the STSP problem...
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ISBN:
(纸本)9781538645093
Satellite transmission scheduling problem (STSP) is an important part of satellite mission planning and one of the bottlenecks in satellite development. This paper established a mathematical model for the STSP problem. After that, we proposed an effective heuristic schedulingalgorithm (HSA) and task scheduling algorithm (TSA) based on the characteristics of the problem. In addition, in the TSA, a greedy taskscheduling method is adopted to ensure that the task arrangement is more compact in order to obtain a better solution. We verified the effectiveness of HSA and TSA algorithms under different task scales and proved that this algorithm can solve the STSP problem well. HSA and TSA all have good performance in terms of total revenue, number of tasks completed, and time utilization.
Resource management and efficient performance of taskscheduling is one of the main research issues in cloud computing. In this paper, an enhanced task scheduling algorithm based on the concept of lion's lifestyle...
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ISBN:
(纸本)9781538644270
Resource management and efficient performance of taskscheduling is one of the main research issues in cloud computing. In this paper, an enhanced task scheduling algorithm based on the concept of lion's lifestyle has been introduced for cloud computing environment. The enhancement is applied by benchmarking the performance of the task scheduling algorithm using real workload traces to evaluate high level of realism performance and effectiveness. A comparative study has been done among the enhanced algorithm, taskscheduling based on Genetic algorithm (GA), and taskscheduling based on Particle Swarm Optimization (PSO) to evaluate the performance of the enhanced algorithm. The results demonstrate higher performance than the other algorithms.
Companies seek technological alternatives that provide competitiveness for their business processes. Among these alternatives, there are integration platforms that allow you to connect applications to your software ec...
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ISBN:
(纸本)9781450353267
Companies seek technological alternatives that provide competitiveness for their business processes. Among these alternatives, there are integration platforms that allow you to connect applications to your software ecosystems. These ecosystems are often composed of local applications and cloud computing services, such as SaaS and PaaS, and still, interact with social media. Integration platforms are specialized software that allows you to design, execute and monitor integration solutions, which connect functionality and data from different applications. Integration platforms typically provide a specific domain language, development toolkit, runtime engine, and monitoring tool. The efficiency of the engine in scheduling and performing integration tasks has a direct impact on the performance of a solution and this is one of the challenges faced by integration platforms. Our literature review has identified that integration engines adopt task scheduling algorithms based on the textit First-In-First-Out discipline, which may be inefficient. Therefore, it is appropriate to seek a task scheduling algorithm that optimizes engine performance, providing a positive impact on the performance of the integration solution in different scenarios. This article proposes an algorithm for taskscheduling based on the meta-heuristic optimization technique, which assigns the tasks to the computational resources, considering the waiting time in the queue of ready tasks and the computational complexity of Each task in order to optimize the performance of the integration solution.
This paper proposes the grouped tasks scheduling (GTS) algorithm that is used to schedule tasks in cloud computing network by applying quality of service to satisfy user's needs. The proposed algorithm distributes...
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This paper proposes the grouped tasks scheduling (GTS) algorithm that is used to schedule tasks in cloud computing network by applying quality of service to satisfy user's needs. The proposed algorithm distributes tasks into five categories;each category has tasks with similar attributes (user type, task type, task size, and task latency). After adding tasks into right category, it starts scheduling these tasks into available services. scheduling is done in two steps: first step is deciding which category will be scheduled first. This depends on the attributes of the tasks that belong to each category so the category that has tasks with high value of attributes will be scheduled first. Second step is deciding which task inside the chosen category will be scheduled first. This depends on the execution time of task so the task that has minimum execution time will be scheduled first. (C) 2016 Production and hosting by Elsevier B.V. on behalf of Faculty of Computers and Information, Cairo University. This is an open access article under the CC BY-NC-ND license.
The majority of recent embedded systems are based on MPSoCs (Multi-Processors System on Chip) architectures. The topologies and the interconnections inside multi processors almost adopt NoCs (Networks on Chip) whose t...
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The majority of recent embedded systems are based on MPSoCs (Multi-Processors System on Chip) architectures. The topologies and the interconnections inside multi processors almost adopt NoCs (Networks on Chip) whose topology and task scheduling algorithm have a direct impact on its performances. In this paper, by using static data flow, a task scheduling algorithm which would automatically assign the application tasks onto different processors is proposed based on complex network. The goal of our algorithm is to replace the static data flow subnetwork by a single dynamic data flow actor such that the global performance in terms of latency and throughput is optimized. Through complex network, it greatly enhances the power of our algorithm in terms of avoiding deadlock, saving energy and providing for integration with more general models of computation. Experimental results show up to 60% performance improvement for real-world examples.
Meeting user task's Quality of Service(QoS) requirements,providing different QoS requirement for Grid users is an important target and a new issue of taskscheduling in Service Grid *** the basis of the price mode...
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Meeting user task's Quality of Service(QoS) requirements,providing different QoS requirement for Grid users is an important target and a new issue of taskscheduling in Service Grid *** the basis of the price model drove by supply and demand in economy,this paper designs the Grid task scheduling algorithm in the market economy. The improved algorithm can achieves the optimized resource allocation strategy on user demand,and improves the system's *** is implemented and the advantages of the new ant algorithm are investigated in a grid simulator called OptorSim after the simulator has been *** experiment results demonstrate the effectiveness of the newly proposed algorithm in terms of users'QoS guarantee,which can reduce data access latency,decrease bandwidth consumption and distribute storage site *** approach is compared with others based on the quality of the prediction formulated by inaccurate information
task scheduling algorithms in heterogeneous computing environment often provide limited performance owing to their low efficiency. This paper models and implements the first two phases of the Critical Path(CP) on a Pr...
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ISBN:
(纸本)9781510830363
task scheduling algorithms in heterogeneous computing environment often provide limited performance owing to their low efficiency. This paper models and implements the first two phases of the Critical Path(CP) on a Processor algorithm with the functional programming paradigm. Firstly, an overall research is conducted on the CPOP algorithms, then the logical relationship of the first two phrases of the algorithm is modeled based on Pi calculus theories, and finally the algorithm is implemented by n Pict programming language. The experimental results demonstrate that the implementation efficiency of the new programming algorithm is higher than that of the traditional C++ language. Therefore, Pi calculus can be applied to the three phases of the CPOP algorithm to improve the taskscheduling efficiency. This paper aims to prove that the CPOP algorithm can be more efficient than ever by using Pi calculus, through modeling and comparing the topological structures of the different types of taskscheduling models.
task scheduling algorithm in heterogeneous environment is programmed in a serial manner in the underlying by using traditional programming language. In this paper, we proposed that using the characteristics of paralle...
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task scheduling algorithm in heterogeneous environment is programmed in a serial manner in the underlying by using traditional programming language. In this paper, we proposed that using the characteristics of parallel computation of Pi-calculus, to parallel analysis and modeling of the heterogeneous earliest finish time(HEFT) algorithm based on pi-calculus. Then programmed it using PICT, which is a programming language that corresponds to the pi-calculus. By creating different topologies of the taskscheduling graph and compared with traditional programming language(C++ language) prove that the efficiency of using pi-calculus to develop the HEFT algorithm is higher than that of traditional programming language.
With increasing demand of data-intensive applications, a workflow with multiple distributed parallel applications becomes a useful way to build various large scale data processing applications on cloud environments. H...
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ISBN:
(纸本)9781479961238
With increasing demand of data-intensive applications, a workflow with multiple distributed parallel applications becomes a useful way to build various large scale data processing applications on cloud environments. However, previous scheduling approaches for executing workflows are focused on estimating minimum make span of executions on pre-configured clusters. In this paper, we present two time-targeted selective taskscheduling schemes which consider malleability and rigidity of each task, named SDER (Sequential Deployment with Expanded Resources) and DDMS (Distributed Deployment on Multiple Streams). These schedulingalgorithms deploy all task of workflow in sequential and distributed ways each, and estimate optimized number of resources for completing workflow execution within given deadline. We show the experimental results which compares the performance of two scheduling schemes for various workflows.
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