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  1. Proceedings of the 4th Workshop on Distributed Cloud Computing (DCC '16)
  2. The misbelief in delay scheduling
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Next generation virtual network architecture for multi-tenant distributed clouds: challenges and emerging techniques
vMCN: virtual mobile cloud network for realizing scalable, real-time cyber physical systems
Software-defined consistency group abstractions for virtual machines
Slicing in locavore infrastructures
Adaptive resilient routing via preorders in SDN
The CAT theorem and performance of transactional distributed systems
New techniques to curtail the tail latency in stream processing systems
Optimized VM memory allocation based on monitored cache hit ratio
The misbelief in delay scheduling
Towards migrating computation to distributed memory caches
DSB-SEIS: a deduplicating secure backup system with encryption intensity selection

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The misbelief in delay scheduling

Content Provider ACM Digital Library
Author Ardekani, Masoud Saeida Ravi, Srivatsan Eugster, Patrick Noh, Youngtae Schatzlein, Derek
Abstract Big-data processing frameworks like Hadoop and Spark, often used in multi-user environments, have struggled to achieve a balance between the full utilization of cluster resources and fairness between users. In particular, data locality becomes a concern, as enforcing fairness policies may cause poor placement of tasks in relation to the data on which they operate. To combat this, the schedulers in many frameworks use a heuristic called delay scheduling, which involves waiting for a short, constant interval for data-local task slots to become free if none are available; however, a fixed delay interval is inefficient, as the ideal time to delay varies depending on input data size, network conditions, and other factors. We propose an adaptive solution (Dynamic Delay Scheduling), which uses a simple feedback metric from finished tasks to adapt the delay scheduling interval for subsequent tasks at runtime. We present a dynamic delay implementation in Spark, and show that it outperforms a fixed delay in TPC-H benchmarks. Our preliminary experiments confirm our intuition that job latency in batch-processing scheduling can be improved using simple adaptive techniques with almost no extra state overhead.
Starting Page 1
Ending Page 6
Page Count 6
File Format PDF
ISBN 9781450342209
DOI 10.1145/2955193.2955203
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2016-07-25
Publisher Place New York
Access Restriction Subscribed
Content Type Text
Resource Type Article
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