Amdahl's law for tail latency
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[1] Christina Delimitrou,et al. Workload characterization of interactive cloud services on big and small server platforms , 2017, 2017 IEEE International Symposium on Workload Characterization (IISWC).
[2] Thomas F. Wenisch,et al. Power management of online data-intensive services , 2011, 2011 38th Annual International Symposium on Computer Architecture (ISCA).
[3] Christina Delimitrou,et al. Paragon: QoS-aware scheduling for heterogeneous datacenters , 2013, ASPLOS '13.
[4] Lieven Eeckhout,et al. Scheduling heterogeneous multi-cores through performance impact estimation (PIE) , 2012, 2012 39th Annual International Symposium on Computer Architecture (ISCA).
[5] Trevor N. Mudge,et al. Challenges and Opportunities for Extremely Energy-Efficient Processors , 2010, IEEE Micro.
[6] Eric Sprangle,et al. Increasing processor performance by implementing deeper pipelines , 2002, ISCA.
[7] Chenyang Lu,et al. Work stealing for interactive services to meet target latency , 2016, PPoPP.
[8] Kishor S. Trivedi. Probability and Statistics with Reliability, Queuing, and Computer Science Applications , 1984 .
[9] Gu-Yeon Wei,et al. Profiling a warehouse-scale computer , 2015, 2015 ACM/IEEE 42nd Annual International Symposium on Computer Architecture (ISCA).
[10] Yale N. Patt,et al. MorphCore: An Energy-Efficient Microarchitecture for High Performance ILP and High Throughput TLP , 2012, 2012 45th Annual IEEE/ACM International Symposium on Microarchitecture.
[11] Luiz André Barroso,et al. The tail at scale , 2013, CACM.
[12] Parag Agrawal,et al. The case for RAMClouds: scalable high-performance storage entirely in DRAM , 2010, OPSR.
[13] Kushagra Vaid,et al. Web search using mobile cores: quantifying and mitigating the price of efficiency , 2010, ISCA.
[14] G. Amdhal,et al. Validity of the single processor approach to achieving large scale computing capabilities , 1967, AFIPS '67 (Spring).
[15] Coniferous softwood. GENERAL TERMS , 2003 .
[16] Thomas F. Wenisch,et al. Does low-power design imply energy efficiency for data centers? , 2011, IEEE/ACM International Symposium on Low Power Electronics and Design.
[17] Kishor S. Trivedi,et al. Probability and Statistics with Reliability, Queuing and Computer Science Applications, Second Edition , 2002 .
[18] Urs Hölzle,et al. Brawny cores still beat wimpy cores, most of the time , 2010 .
[19] Pradeep Dubey,et al. Architecting to achieve a billion requests per second throughput on a single key-value store server platform , 2015, 2015 ACM/IEEE 42nd Annual International Symposium on Computer Architecture (ISCA).
[20] Christina Delimitrou,et al. Quasar: resource-efficient and QoS-aware cluster management , 2014, ASPLOS.
[21] Mor Harchol-Balter,et al. Performance Modeling and Design of Computer Systems: Queueing Theory in Action , 2013 .
[22] Hao Che,et al. Wimpy or brawny cores: A throughput perspective , 2013, J. Parallel Distributed Comput..
[23] Ricardo Bianchini,et al. Few-to-Many: Incremental Parallelism for Reducing Tail Latency in Interactive Services , 2015, ASPLOS.
[24] Luiz André Barroso,et al. The Case for Energy-Proportional Computing , 2007, Computer.
[25] Babak Falsafi,et al. Scale-out NUMA , 2014, ASPLOS.
[26] Mark D. Hill,et al. Amdahl's Law in the Multicore Era , 2008, Computer.