Polynomial Scheduling Algorithm for Parallel Applications on Hybrid Platforms

2020 ◽  
pp. 143-155
Author(s):  
Massinissa Ait Aba ◽  
Lilia Zaourar ◽  
Alix Munier
Keyword(s):  
Scheduling Algorithm ◽  
Parallel Applications

scholarly journals Optimizing Cost of Irregularly Structured Problems in Cloud

2020 ◽  
Vol 9 (7) ◽  
pp. 560-569
Keyword(s):  
Dynamic Method ◽  
Data Transfer ◽  
Cost Model ◽  
Scheduling Algorithm ◽  
Parallel Applications ◽  
Cloud Environment ◽  
Monetary Cost ◽  
Target Time ◽  
Structured Problems ◽  
The Cost

This paper deals with maximizing the cost of parallel applications in a cloud-based environment. The cost belongs to monetary cost and cost of efficiency. The core argument seems to be that the parallel program robustness should affect the current monetary cost. Dynamic method of optimization is used to minimize the cost of computation. In order to measure the overall monetary cost of parallel computation, a cost model is used to evaluate the cost of parallel infrastructure as well as the cost of delayed performance. The main purpose of this cost model is to identify the necessary resources for performing this type of operation. Different methods have been used in the cloud environment. But these solutions do not take into account the uncertainties in the scheduling system, namely task start / perform / finish time, the unpredictable data transfer period between tasks, the unexpected arrival of new tasks. Such factors contribute to the breach of the task deadline and increase the cost of renting the service of executing the task, this effect will increase the monetary cost. Will boost the output by reducing the ambiguity in the scheduling process that requires time for execution of tasks and time for data transfer. In order to be precise a scheduling algorithm, uncertainty-Aware Scheduling Algorithm (ASA) is built to schedule complex and multiple tasks. When a task has been accomplished, its beginning / prosecution / target time is accessible that implies the ambiguity are no longer visible and therefore does not impact its related pending task.

Properties and Performance of Cube-Based Multiprocessor Architectures

2016 ◽  
Vol 7 (1) ◽  
pp. 63-78
Author(s):  
Abdus Samad ◽  
Jamshed Siddiqui ◽  
Zaki Ahmed Khan
Keyword(s):  
Interconnection Network ◽  
Scheduling Algorithm ◽  
Parallel Architectures ◽  
Parallel Applications ◽  
Performance Study ◽  
Actual Performance ◽  
Multiprocessor Architectures ◽  
Multiprocessor Interconnection ◽  
And Performance ◽  
The Comparative Study

Parallel architectures provide the possibility of solving highly computational parallel applications in a variety of ways. Numerous interconnection topologies have been designed to achieve the desired performance. Nevertheless, the actual performance is far below the expectation of users when executing parallel applications on a particular multiprocessor network. This paper presents the performance study on a special class of parallel architectures known as cube based multiprocessor architectures. It describes the issues and challenges related to the design of cube-based architectures. The issues related to the design of highly parallel system such as scalability, complexity of the system and mapping of parallel application on to it are discussed. Furthermore, the problem of routing between nodes has been analyzed along with the topological properties of cube-based architectures. Simulation results are obtained by applying task scheduling algorithm on various multiprocessor networks. The comparative study implies the various aspects while designing an efficient multiprocessor interconnection network with optimal scheduling algorithm.

Enhanced Parallel Application Scheduling Algorithm with Energy Consumption Constraint in Heterogeneous Distributed Systems

2019 ◽  
Vol 28 (11) ◽  
pp. 1950190 ◽  
Author(s):  
Jinghong Li ◽  
Guoqi Xie ◽  
Keqin Li ◽  
Zhuo Tang
Keyword(s):  
Energy Consumption ◽  
Distributed Systems ◽  
Gaussian Elimination ◽  
Scheduling Algorithm ◽  
Parallel Applications ◽  
Parallel Application ◽  
Heterogeneous Distributed Systems ◽  
Dynamic Voltage ◽  
Cluster Computer Systems ◽  
Consumption Constraints

Energy consumption has always been one of the main design problems in heterogeneous distributed systems, whether for large cluster computer systems or small handheld terminal devices. And as energy consumption explodes for complex performance, many efforts and work are focused on minimizing the schedule length of parallel applications that meet the energy consumption constraints currently. In prior studies, a pre-allocation method based on dynamic voltage and frequency scaling (DVFS) technology allocates unassigned tasks with minimal energy consumption. However, this approach does not necessarily result in minimal scheduling length. In this paper, we propose an enhanced scheduling algorithm, which allocates the same energy consumption for each task by selecting a relatively intermediate value among the unequal allocations. Based on the two real-world applications (Fast Fourier transform and Gaussian elimination) and the randomly generated parallel application, experiments show that the proposed algorithm not only achieves better scheduling length while meeting the energy consumption constraints, but also has better performance than the existing parallel algorithms.

scholarly journals Scheduling Parallel Jobs Using Migration and Consolidation in the Cloud

2012 ◽  
Vol 2012 ◽  
pp. 1-18 ◽  
Author(s):  
Xiaocheng Liu ◽  
Bin Chen ◽  
Xiaogang Qiu ◽  
Ying Cai ◽  
Kedi Huang
Keyword(s):  
Quality Of Service ◽  
High Performance ◽  
Large Scale ◽  
Job Scheduling ◽  
Scheduling Algorithm ◽  
Parallel Applications ◽  
Parallel Job Scheduling ◽  
Parallel Job ◽  
Job Scheduling Problem

An increasing number of high performance computing parallel applications leverages the power of the cloud for parallel processing. How to schedule the parallel applications to improve the quality of service is the key to the successful host of parallel applications in the cloud. The large scale of the cloud makes the parallel job scheduling more complicated as even simple parallel job scheduling problem is NP-complete. In this paper, we propose a parallel job scheduling algorithm named MEASY. MEASY adopts migration and consolidation to enhance the most popular EASY scheduling algorithm. Our extensive experiments on well-known workloads show that our algorithm takes very good care of the quality of service. For two common parallel job scheduling objectives, our algorithm produces an up to 41.1% and an average of 23.1% improvement on the average response time; an up to 82.9% and an average of 69.3% improvement on the average slowdown. Our algorithm is robust even in terms that it allows inaccurate CPU usage estimation and high migration cost. Our approach involves trivial modification on EASY and requires no additional technique; it is practical and effective in the cloud environment.

scholarly journals Energy-Efficient Reliability-Aware Scheduling Algorithm on Heterogeneous Systems

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Xiaoyong Tang ◽  
Weizhen Tan
Keyword(s):  
Energy Consumption ◽  
System Performance ◽  
High Performance ◽  
Scheduling Algorithm ◽  
Heterogeneous Systems ◽  
High Energy ◽  
Parallel Applications ◽  
Energy Aware ◽  
Simulation Performance ◽  
Energy Aware Scheduling

The amount of energy needed to operate high-performance computing systems increases regularly since some years at a high pace, and the energy consumption has attracted a great deal of attention. Moreover, high energy consumption inevitably contains failures and reduces system reliability. However, there has been considerably less work of simultaneous management of system performance, reliability, and energy consumption on heterogeneous systems. In this paper, we first build the precedence-constrained parallel applications and energy consumption model. Then, we deduce the relation between reliability and processor frequencies and get their parameters approximation value by least squares curve fitting method. Thirdly, we establish a task execution reliability model and formulate this reliability and energy aware scheduling problem as a linear programming. Lastly, we propose a heuristic Reliability-Energy Aware Scheduling (REAS) algorithm to solve this problem, which can get good tradeoff among system performance, reliability, and energy consumption with lower complexity. Our extensive simulation performance evaluation study clearly demonstrates the tradeoff performance of our proposed heuristic algorithm.

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