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Enabling resource sharing between transactional and batch workloads using dynamic application placement

by David Carrera, Malgorzata Steinder, Ian Whalley, Jordi Torres - In 9th ACM/IFIP/USENIX International Conference on Middleware , 2008
"... Abstract. We present a technique that enables existing middleware to fairly manage mixed workloads: batch jobs and transactional applications. The tech-nique leverages a generic application placement controller, which dynamically al-locates compute resources to application instances. The controller ..."
Abstract - Cited by 12 (2 self) - Add to MetaCart
Abstract. We present a technique that enables existing middleware to fairly manage mixed workloads: batch jobs and transactional applications. The tech-nique leverages a generic application placement controller, which dynamically al-locates compute resources to application instances. The controller works towards a fairness goal while also trying to maximize individual workload performance. We use relative performance functions to drive the application placement con-troller. Such functions are derived from workload-specific performance models— in the case of transactional workloads, we use queuing theory to build the perfor-mance model. For batch workloads, we evaluate a candidate placement by calcu-lating long-term estimates of the completion times that are achievable with that placement according to a scheduling policy. In this paper, we propose a lowest rel-ative performancwe first scheduling policy as a way to also achieve fair resource allocation among batch jobs. Our technique permits collocation of the workload types on the same physical hardware, and leverages control mechanisms such as suspension and migration to perform online system reconfiguration. In our ex-periments we demonstrate that our technique maximizes mixed workload perfor-mance while providing service differentiation based on high-level performance goals. 1
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Fault-aware, utility-based job scheduling on Blue Gene/P systems

by Wei Tang, Zhiling Lan, Narayan Desai, Daniel Buettner - in IEEE International Conference on Cluster Computing and Workshops, 2009, CLUSTER ’09 , 2009
"... Abstract—Job scheduling on large-scale systems is an in-creasingly complicated affair, with numerous factors influencing scheduling policy. Addressing these concerns results in sophisti-cated scheduling policies that can be difficult to reason about. In this paper, we present a general utility-based ..."
Abstract - Cited by 11 (7 self) - Add to MetaCart
Abstract—Job scheduling on large-scale systems is an in-creasingly complicated affair, with numerous factors influencing scheduling policy. Addressing these concerns results in sophisti-cated scheduling policies that can be difficult to reason about. In this paper, we present a general utility-based scheduling frame-work to balance various scheduling requirements and priorities. It enables system owners to customize scheduling policies under different circumstances without changing the scheduling code. We also develop a fault-aware job allocation strategy for Blue Gene/P systems to address the increasing concern of system failures. We demonstrate the effectiveness of these facilities by means of event-driven simulations with real job traces collected from the production Blue Gene/P system at Argonne National Laboratory. I.
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Optimizing on-demand data broadcast scheduling in pervasive environments

by Rinku Dewri, Indrakshi Ray, Indrajit Ray, Darrell Whitley - In EDBT , 2008
"... Data dissemination in pervasive environments is often accomplished by on-demand broadcasting. The time critical nature of the data requests plays an important role in scheduling these broadcasts. Most research in on-demand broadcast scheduling has focused on the timely servicing of requests so as to ..."
Abstract - Cited by 7 (0 self) - Add to MetaCart
Data dissemination in pervasive environments is often accomplished by on-demand broadcasting. The time critical nature of the data requests plays an important role in scheduling these broadcasts. Most research in on-demand broadcast scheduling has focused on the timely servicing of requests so as to minimize the number of missed deadlines. However, there exists many pervasive environments where the utility of the data is an equally important criterion as its timeliness. Missing the deadline reduces the utility of the data but does not make it zero. In this work, we address the problem of scheduling on-demand data broadcasts with soft deadlines. We investigate search based optimization techniques to develop broadcast schedulers that make explicit attempts to maximize the utility of data requests as well as service as many requests as possible within the acceptable time limit. Our analysis shows that heuristic driven methods for such problems can be improved by hybridizing them with local search algorithms. We further investigate the option of employing a dynamic optimization technique to facilitate utility gain, thereby surpassing the requirement of a heuristic in the process. An evolution strategy based stochastic hill climber is investigated in this context. 1.
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Asymmetry Aware Scheduling Algorithms for Asymmetric Multiprocessors

by Nagesh Lakshminarayana, Sushma Rao, Hyesoon Kim
"... Multiprocessor architecture is becoming popular in both desktop processors and mobile processors. Especially asymmetric architecture shows promise in saving energy and power. However, how to design applications and how to schedule applications in asymmetric multiprocessors are still challenging prob ..."
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Multiprocessor architecture is becoming popular in both desktop processors and mobile processors. Especially asymmetric architecture shows promise in saving energy and power. However, how to design applications and how to schedule applications in asymmetric multiprocessors are still challenging problems. In this paper, we evaluate the performance of applications in asymmetric multiprocessors to understand the characteristics of asymmetric processors. We also evaluate a task size aware scheduling algorithm and a critical section length aware scheduling algorithm in asymmetric multiprocessors. We show that when workload is asymmetric, the task size aware scheduler can improve performance by up to 14 % compared to a scheduler which does not consider asymmetric characteristics. 1.
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