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Energy-efficient Task Scheduling in Heterogeneous Environment 2013/10/25.

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Presentation on theme: "Energy-efficient Task Scheduling in Heterogeneous Environment 2013/10/25."— Presentation transcript:

1 Energy-efficient Task Scheduling in Heterogeneous Environment 2013/10/25

2 Outline Literature survey Preliminary scheduling algorithm design for big.LITTLE cores.

3 Energy-efficient Task Scheduling Goals: Energy Minimize energy consumption. Performance Find an optimal makespan. Satisfy constraints (deadline, QoS, …).

4 Task Scheduling NP-Complete Static scheduling Simple, low runtime overhead. Low resource utilization. Dynamic scheduling Good CPU utilization Runtime overhead.

5 Static Scheduling Scheduling heuristics: Cluster-based Duplication-based List-based

6 Cluster-based Scheduling Mainly for homogeneous systems. Form cluster of tasks based on certain criteria. For example, a set of tasks that need to communicate among themselves are grouped together to form a cluster. Tasks of same cluster are scheduled on the same processor.

7 Duplication-based Scheduling For scheduling task of a DAG. Duplicates the tasks onto one or more processors. Reduce the communication cost, network overhead, and potentially reducing the start times of waiting tasks. Shorter makespan.

8 List-based Scheduling Also called Priority-based Scheduling. Tasks are arranged in the form of a list based on certain priorities. Schedule tasks onto the most suitable processor.

9 Power Management Techniques Dynamic Power Management (DPM) Dynamic Voltage and Frequency Scaling (DVFS) Virtualization and green policies Virtual machine/resource consolidation.

10 Related Works - DVFS Towards Energy Aware Scheduling for Precedence Constrained Parallel Tasks in a Cluster with DVFS, 2010 Use Earliest Task First scheduling algorithm, identify the slack time for non-critical jobs and scale their supply voltages thus reducing the jobs energy consumption. Energy aware DAG scheduling on heterogeneous systems, 2010 Combine Decisive Path Scheduling with DVS to minimize both finish time and energy consumption. Task scheduling and voltage selection for energy minimization, 2002 Formulate the scheduling problem as an Integer Programming problem.

11 Related Works - Consolidation Energy aware consolidation for cloud computing, 2008 Consolidate tasks balancing energy consumption and performance on the basis of the Pareto frontier(optimal points). Reducing wasted resources to help achieve green data centers, 2008 Adopt two techniques, memory compression and request discrimination, to enhance consolidation and reduce overall energy consumption.

12 Related Works - Consolidation Energy aware dynamic resource consolidation algorithm for virtualized service centers based on reinforcement learning, 2011 Energy efficient utilization of resources in cloud computing systems, 2012 Present two energy conscious task consolidation heuristics, ECTC and MaxUtil, which aim to maximize resource utilization and reduce energy consumption.

13 Related Works - Others On Effective Slack Reclamation in Task Scheduling for Energy Reduction, Present a two phases, main scheduling pass and the makespan- conservative energy reduction pass, Energy-Conscious Scheduling(ECS) algorithm with its extension. DAG scheduling Using a Lookahead Variant of the Heterogeneous Earliest Finish Time Algorithm, 2010 Develop lookahead-HEFT(Heterogeneous Earliest Finish Time) algorithm which use lookahead information to foresee how decisions affect other tasks.

14 Related Works - Others Cooperative power-aware scheduling in grid computing environments, 2010 A cooperative game theoretical technique for joint optimization of energy consumption and response time in computational grids, 2009 Presents a cooperative game model and the Nash Bargaining solution to minimize energy consumption while maintaining a specified service quality.

15 Big.LITTLE core Scheduling Assume that we have n pairs of big.LITTLE cores. Initially all pairs use LITTLE core. Assume we know the following information of a task T k. Task deadline. Estimated execution time on big core. Estimated execution time on LITTLE core.

16 Objective Dynamically decide the number of big and LITTLE cores according to task information. Use the smallest number of big cores to achieve power saving. All tasks are finished before their deadline.

17 Our Heuristic First, we define urgency U to indicate the priority of a task. For Task T k 0 U k 1, then task T k can be finished before deadline on LITTLE core U k > 1, then task T k cant be finished before deadline on LITTLE core.

18 Core Switching Switch one LITTLE core to big core if there exists a task T k with urgency U k > 1. Find all the Tasks {T j, with U j > 0.8}, assign these tasks to big cores. Switch big cores to LITTLE cores if there is no task with urgency greater than 0.8.

19 Summary This is a preliminary thought, well need some further discussions about the heuristic. Also we need to conduct experiments to find suitable parameters. 0.8?

20 Pareto frontier Algorithms for computing the Pareto frontier of a finite set of alternatives have been studied in computer science, sometimes referred to as the maximum vector problem or the skyline query.


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