Task scheduling techniques for asymmetric multi-core systems

As performance and energy efficiency have become the main challenges for next-generation high-performance computing, asymmetric multi-core architectures can provide solutions to tackle these issues. Parallel programming models need to be able to suit the needs of such systems and keep on increasing the application’s portability and efficiency. This paper proposes two task scheduling approaches that target asymmetric systems. These dynamic scheduling policies reduce total execution time either by detecting the longest or the critical path of the dynamic task dependency graph of the application, or by finding the earliest executor of a task. They use dynamic scheduling and information discoverable during execution, fact that makes them implementable and functional without the need of off-line profiling. In our evaluation we compare these scheduling approaches with two existing state-of the art heterogeneous schedulers and we track their improvement over a FIFO baseline scheduler. We show that the heterogeneous schedulers improve the baseline by up to 1.45 in a real 8-core asymmetric system and up to 2.1 in a simulated 32-core asymmetric chip.This work has been supported by the Spanish Government (SEV2015-0493), by the Spanish Ministry of Science and Innovation (contract TIN2015-65316-P), by Generalitat de Catalunya (contracts 2014-SGR-1051 and 2014-SGR-1272), by the RoMoL ERC Advanced Grant (GA 321253) and the European HiPEAC Network of Excellence. The Mont-Blanc project receives funding from the EU’s Seventh Framework Programme (FP7/2007-2013) under grant agreement no 610402 and from the EU’s H2020 Framework Programme (H2020/2014-2020) under grant agreement no 671697. M. Moretó has been partially supported by the Ministry of Economy and Competitiveness under Juan de la Cierva postdoctoral fellowship number JCI-2012-15047. M. Casas is supported by the Secretary for Universities and Research of the Ministry of Economy and Knowledge of the Government of Catalonia and the Cofund programme of the Marie Curie Actions of the 7th R&D Framework Programme of the European Union (Contract 2013 BP B 00243).Peer ReviewedPostprint (author's final draft

Load Balancing Scheduling Algorithm for Concurrent Workflow

Concurrent workflow scheduling algorithm works in three phases, namely rank computation, tasks selection, and resource selection. In this paper, we introduce a new ranking algorithm that computes the rank of a task, based on its successor rank and its predecessors average communication time, instead of its successors rank. The advantage of this ranking algorithm is that two dependent tasks are assigned to the same machine and as a result the scheduled length is reduced. The task selection phase selects a ready task from each workflow and creates a task pool. The resource selection phase initially assigns tasks using min-min heuristic, after initial assignment, tasks are moved from the highly loaded machines to the lightly loaded machines. Our resource selection algorithm increases the load balance among the resources due to tasks assignment heuristic and reassignment of tasks from the highly loaded machines. The simulation results show that our proposed scheduling algorithm performs better over existing approaches in terms of load balance, makespan and turnaround time

Hybrid scheduling algorithms in cloud computing: a review

Cloud computing is one of the emerging fields in computer science due to its several advancements like on-demand processing, resource sharing, and pay per use. There are several cloud computing issues like security, quality of service (QoS) management, data center energy consumption, and scaling. Scheduling is one of the several challenging problems in cloud computing, where several tasks need to be assigned to resources to optimize the quality of service parameters. Scheduling is a well-known NP-hard problem in cloud computing. This will require a suitable scheduling algorithm. Several heuristics and meta-heuristics algorithms were proposed for scheduling the user's task to the resources available in cloud computing in an optimal way. Hybrid scheduling algorithms have become popular in cloud computing. In this paper, we reviewed the hybrid algorithms, which are the combinations of two or more algorithms, used for scheduling in cloud computing. The basic idea behind the hybridization of the algorithm is to take useful features of the used algorithms. This article also classifies the hybrid algorithms and analyzes their objectives, quality of service (QoS) parameters, and future directions for hybrid scheduling algorithms

Dual-phase just-in-time workflow scheduling in P2P grid systems

This paper presents a fully decentralized justin-time workflow scheduling method in a P2P Grid system. The proposed solution allows each peer node to autonomously dispatch inter-dependent tasks of workflows to run on geographically distributed computers. To reduce the workflow completion time and enhance the overall execution efficiency, not only does each node perform as a scheduler to distribute its tasks to execution nodes (or resource nodes), but the resource nodes will also set the execution priorities for the received tasks. By taking into account the unpredictability of tasks' finish time, we devise an efficient task scheduling heuristic, namely dynamic shortest makespan first (DSMF), which could be applied at both scheduling phases for determining the priority of the workflow tasks. We compare the performance of the proposed algorithm against seven other heuristics by simulation. Our algorithm achieves 20%~60% reduction on the average completion time and 37.5%~90% improvement on the average workflow execution efficiency over other decentralized algorithms. © 2010 IEEE.published_or_final_versionProcessing (ICPP 2010), San Diego, CA., 13-16 September 2010. In Proceedings of the 39th ICCP, 2010, p. 238-24

Workflow Scheduling Techniques and Algorithms in IaaS Cloud: A Survey

In the modern era, workflows are adopted as a powerful and attractive paradigm for expressing/solving a variety of applications like scientific, data intensive computing, and big data applications such as MapReduce and Hadoop. These complex applications are described using high-level representations in workflow methods. With the emerging model of cloud computing technology, scheduling in the cloud becomes the important research topic. Consequently, workflow scheduling problem has been studied extensively over the past few years, from homogeneous clusters, grids to the most recent paradigm, cloud computing. The challenges that need to be addressed lies in task-resource mapping, QoS requirements, resource provisioning, performance fluctuation, failure handling, resource scheduling, and data storage. This work focuses on the complete study of the resource provisioning and scheduling algorithms in cloud environment focusing on Infrastructure as a service (IaaS). We provided a comprehensive understanding of existing scheduling techniques and provided an insight into research challenges that will be a possible future direction to the researchers

Proactive and Dynamic Task Scheduling in Fog-cloud Environment

Fog computing was introduced for the first time by Cisco in 2012. Since then, there has been a great number of studies on fog computing, in which vacant and free-of-charge computing resources in local networks provide low-latency services to end devices. Even though traditional architecture with scalable and powerful central servers in cloud can accommodate those tasks, it is costly to allocate resources in cloud to execute all those tasks. In addition, it falls short of satisfying Quality of Service (QoS) requirements in terms of waiting time because of long distance communication between servers and user end devices. In this thesis, we discuss dynamic scheduling problem in fog-cloud collaboration environment for real-time applications when QoS is strict and when an answer is useless if the corresponding application finishes its execution after a pre-defined deadline. By taking into account an admission control procedure to grant only requests whose deadline requirements are feasible with respect to the available resources in the network, we study a proactive scenario using different strategies to calculate schedules and to assign resources, within the admission control procedure to accommodate an incoming request. Then, we propose our heuristic with four variants corresponding to four different strategies, with the adjustment of a trade-off cost-makespan factor in an utility function. When evaluating performance with some baseline methods in such proactive scenario, the numerical results show that our variants can meet deadline requirements for more applications while exploiting more efficiently the resources in the fog layer and being charged less for using cloud. Keywords: fog computing, cloud computing, dynamic scheduling, real-time scheduling, task scheduling, workflow applications, DAG, QoS requirements, heterogeneous systems

QoS-aware predictive workflow scheduling

This research places the basis of QoS-aware predictive workflow scheduling. This research novel contributions will open up prospects for future research in handling complex big workflow applications with high uncertainty and dynamism. The results from the proposed workflow scheduling algorithm shows significant improvement in terms of the performance and reliability of the workflow applications