3E: Energy-Efficient Elastic Scheduling for Independent Tasks in Heterogeneous Computing Systems

Reducing energy consumption is a major design constraint for modern heterogeneous computing systems to minimize electricity cost, improve system reliability and protect environment. Conventional energy-efficient scheduling strategies developed on these systems do not sufficiently exploit the system elasticity and adaptability for maximum energy savings, and do not simultaneously take account of user expected finish time. In this paper, we develop a novel scheduling strategy named energy-efficient elastic (3E) scheduling for aperiodic, independent and non-real-time tasks with user expected finish times on DVFS-enabled heterogeneous computing systems. The 3E strategy adjusts processors’ supply voltages and frequencies according to the system workload, and makes trade-offs between energy consumption and user expected finish times. Compared with other energy-efficient strategies, 3E significantly improves the scheduling quality and effectively enhances the system elasticity

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

A New Duplication Task Scheduling Algorithm in Heterogeneous Distributed Computing Systems

The efficient scheduling algorithm is critical to achieve high performance in parallel and distributed systems. The main objective of task scheduling is to assign the tasks onto the available processors with the aim of producing minimum schedule length and without violating the precedence constraints. So we developed new algorithm called Mean Communication Node with Duplication MCND algorithm to achieve high performance task scheduling. The MCND algorithm has two phases namely, task priority and processor selection. Our algorithm takes into account the average of parents' communication costs for each task to reduce the overhead communication. The algorithm uses new task duplication algorithm. We build a simulation to compare the MCND algorithm with CPOP with duplication algorithm. The algorithms are applied on real application. From results, the MCND algorithm shows the best result

Reliable Provisioning of Spot Instances for Compute-intensive Applications

Cloud computing providers are now offering their unused resources for leasing in the spot market, which has been considered the first step towards a full-fledged market economy for computational resources. Spot instances are virtual machines (VMs) available at lower prices than their standard on-demand counterparts. These VMs will run for as long as the current price is lower than the maximum bid price users are willing to pay per hour. Spot instances have been increasingly used for executing compute-intensive applications. In spite of an apparent economical advantage, due to an intermittent nature of biddable resources, application execution times may be prolonged or they may not finish at all. This paper proposes a resource allocation strategy that addresses the problem of running compute-intensive jobs on a pool of intermittent virtual machines, while also aiming to run applications in a fast and economical way. To mitigate potential unavailability periods, a multifaceted fault-aware resource provisioning policy is proposed. Our solution employs price and runtime estimation mechanisms, as well as three fault tolerance techniques, namely checkpointing, task duplication and migration. We evaluate our strategies using trace-driven simulations, which take as input real price variation traces, as well as an application trace from the Parallel Workload Archive. Our results demonstrate the effectiveness of executing applications on spot instances, respecting QoS constraints, despite occasional failures.Comment: 8 pages, 4 figure

On exploiting task duplication in parallel program scheduling

One of the main obstacles in obtaining high performance from message-passing multicomputer systems is the inevitable communication overhead which is incurred when tasks executing on different processors exchange data. Given a task graph, duplication-based scheduling can mitigate this overhead by allocating some of the tasks redundantly on more than one processor. In this paper, we focus on the problem of using duplication in static scheduling of task graphs on parallel and distributed systems. We discuss five previously proposed algorithms and examine their merits and demerits. We describe some of the essential principles for exploiting duplication in a more useful manner and, based on these principles, propose an algorithm which outperforms the previous algorithms. The proposed algorithm generates optimal solutions for a number of task graphs. The algorithm assumes an unbounded number of processors. For scheduling on a bounded number of processors, we propose a second algorithm which controls the degree of duplication according to the number of available processors. The proposed algorithms are analytically and experimentally evaluated and are also compared with the previous algorithms. © 1998 IEEE.published_or_final_versio

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