Parallel Asynchronous Particle Swarm Optimization For Job Scheduling In Grid Environment

Grid computing is a new, large and powerful self managing virtual computer out of large collection of connected heterogeneous systems sharing various combination of resources and it is the combination of computer resources from multiple administrative domains applied to achieve a goal, it is used to solve scientific, technical or business problem that requires a great number of processing cycles and needs large amounts of data. One primary issue associated with the efficient utilization of heterogeneous resources in a grid environment is task scheduling. Task Scheduling is an important issue of current implementation of grid computing. The demand for scheduling is to achieve high performance computing. If large number of tasks is computed on the geographically distributed resources, a reasonable scheduling algorithm must be adopted in order to get the minimum completion time. Typically, it is difficult to find an optimal resource allocation for specific job that minimizes the schedule length of jobs. So the scheduling problem is defined as NP-complete problem and it is not trivial. Heuristic algorithms are used to solve the task scheduling problem in the grid environment and may provide high performance or high throughput computing or both. In this paper, a parallel asynchronous particle swarm optimization algorithm is proposed for job scheduling. The proposed scheduler allocates the best suitable resources to each task with minimal makespan and execution time. The experimental results are compared which shows that the algorithm produces better results when compared with the existing ant colony algorithm

Scheduling strategies for time-sensitive distributed applications on edge computing

Edge computing is a distributed computing paradigm that shifts the computation capabilities close to the data sources. This new paradigm, coupled with the use of parallel embedded processor architectures, is becoming a very promising solution for time-sensitive distributed applications used in Internet of Things and large Cyber-Physical Systems (e.g., those used in smart cities) to alleviate the pressure on centralized solutions. However, the distribution and heterogeneity nature of the edge computing complicates the response-time analysis on these type of applications. This thesis addresses this challenge by proposing a new Directed Acyclic Graph (DAG)-task based system model to characterize: (1) the distribution nature of applications executed on the edge; and (2) the heterogeneous computation and network communication capabilities of edge computing platforms. Based on this system model, this work presents five different scheduling strategies: four sub-optimal but tractable heuristics and an optimal but costly approach based on a mixed integer linear programming (MILP), that minimize the overall response time of distributed time-sensitive applications. To address both issues, and as a proof of concept, we use COMPSs, a framework composed of a task-based programming model and a runtime used to program and efficiently distribute time-sensitive applications across the compute continuum. However, COMPSs is agnostic of time-sensitive applications, hence in this work we extend it to consider the dynamic scheduling based on the proposed scheduling strategies. Our results show that our scheduling heuristics outperform current scheduling solutions, while providing an average and upper-bound execution time comparable to the optimal one provided by the MILP allocation approach

Application Partitioning and Mapping Techniques for Heterogeneous Parallel Platforms

Proceedings of the First PhD Symposium on Sustainable Ultrascale Computing Systems (NESUS PhD 2016) Timisoara, Romania. February 8-11, 2016.Parallelism has become one of the most extended paradigms used to improve performance. Legacy source code needs to be re-written so that it can take advantage of multi-core and many-core computing devices, such as GPGPU, FPGA, DSP or specific accelerators. However, it forces software developers to adapt applications and coding mechanisms in order to exploit the available computing devices. It is a time consuming and error prone task that usually results in expensive and sub-optimal parallel software. In this work, we describe a parallel programming model, a set of annotating techniques and a static scheduling algorithm for parallel applications. Their purpose is to simplify the task of transforming sequential legacy code into parallel code capable of making full use of several different computing devices with the objetive of increasing performance, lowering energy consumption and increase the productivity of the developer.European Cooperation in Science and Technology. COSTThe work presented in this paper has been partially supported by EU under the COST programme Action IC1305, ’Network for Sustainable Ultrascale Computing (NESUS)’ The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement n. 609666 and by the Spanish Ministry of Economics and Competitiveness under the grant TIN2013-41350-P

PGGA: A predictable and grouped genetic algorithm for job scheduling

This paper presents a predictable and grouped genetic algorithm (PGGA) for job scheduling. The novelty of the PGGA is twofold: (1) a job workload estimation algorithm is designed to estimate a job workload based on its historical execution records, (2) the divisible load theory (DLT) is employed to predict an optimal fitness value by which the PGGA speeds up the convergence process in searching a large scheduling space. Comparison with traditional scheduling methods such as first-come-first-serve (FCFS) and random scheduling, heuristics such as a typical genetic algorithm, Min-Min and Max-Min indicates that the PGGA is more effective and efficient in finding optimal scheduling solutions

Efficient Task Replication for Fast Response Times in Parallel Computation

One typical use case of large-scale distributed computing in data centers is to decompose a computation job into many independent tasks and run them in parallel on different machines, sometimes known as the "embarrassingly parallel" computation. For this type of computation, one challenge is that the time to execute a task for each machine is inherently variable, and the overall response time is constrained by the execution time of the slowest machine. To address this issue, system designers introduce task replication, which sends the same task to multiple machines, and obtains result from the machine that finishes first. While task replication reduces response time, it usually increases resource usage. In this work, we propose a theoretical framework to analyze the trade-off between response time and resource usage. We show that, while in general, there is a tension between response time and resource usage, there exist scenarios where replicating tasks judiciously reduces completion time and resource usage simultaneously. Given the execution time distribution for machines, we investigate the conditions for a scheduling policy to achieve optimal performance trade-off, and propose efficient algorithms to search for optimal or near-optimal scheduling policies. Our analysis gives insights on when and why replication helps, which can be used to guide scheduler design in large-scale distributed computing systems.Comment: Extended version of the 2-page paper accepted to ACM SIGMETRICS 201

A WOA-based optimization approach for task scheduling in cloud Computing systems

Task scheduling in cloud computing can directly affect the resource usage and operational cost of a system. To improve the efficiency of task executions in a cloud, various metaheuristic algorithms, as well as their variations, have been proposed to optimize the scheduling. In this work, for the first time, we apply the latest metaheuristics WOA (the whale optimization algorithm) for cloud task scheduling with a multiobjective optimization model, aiming at improving the performance of a cloud system with given computing resources. On that basis, we propose an advanced approach called IWC (Improved WOA for Cloud task scheduling) to further improve the optimal solution search capability of the WOA-based method. We present the detailed implementation of IWC and our simulation-based experiments show that the proposed IWC has better convergence speed and accuracy in searching for the optimal task scheduling plans, compared to the current metaheuristic algorithms. Moreover, it can also achieve better performance on system resource utilization, in the presence of both small and large-scale tasks