Burstable Instances for Clouds: Performance Modeling, Equilibrium Analysis, and Revenue Maximization
暂无分享,去创建一个
[1] Sangtae Ha,et al. Offering Supplementary Network Technologies: Adoption Behavior and Offloading Benefits , 2015, IEEE/ACM Transactions on Networking.
[2] Ricardo Bianchini,et al. Resource Central: Understanding and Predicting Workloads for Improved Resource Management in Large Cloud Platforms , 2017, SOSP.
[3] Qingsheng Zhu,et al. Percentile Performance Estimation of Unreliable IaaS Clouds and Their Cost-Optimal Capacity Decision , 2017, IEEE Access.
[4] R. Srikant,et al. The Price of Simplicity , 2007, 2007 Conference Record of the Forty-First Asilomar Conference on Signals, Systems and Computers.
[5] Liang Zheng,et al. How to Bid the Cloud , 2015, Comput. Commun. Rev..
[6] Philipp Leitner,et al. Bursting with Possibilities -- An Empirical Study of Credit-Based Bursting Cloud Instance Types , 2015, 2015 IEEE/ACM 8th International Conference on Utility and Cloud Computing (UCC).
[7] Eric Bouillet,et al. Efficient resource provisioning in compute clouds via VM multiplexing , 2010, ICAC '10.
[8] Erik Elmroth,et al. Incentivizing self-capping to increase cloud utilization , 2017, SoCC.
[9] Danny H. K. Tsang,et al. On Power-Peak-Aware Scheduling for Large-Scale Shared Clusters , 2020, IEEE Transactions on Big Data.
[10] Robert Birke,et al. Optimizing for Tail Sojourn Times of Cloud Clusters , 2018, IEEE Transactions on Cloud Computing.
[11] Baochun Li,et al. Dynamic Cloud Pricing for Revenue Maximization , 2013, IEEE Transactions on Cloud Computing.
[12] Liang Zheng,et al. On the Viability of a Cloud Virtual Service Provider , 2016, SIGMETRICS.
[13] Narendra Karmarkar,et al. A new polynomial-time algorithm for linear programming , 1984, Comb..
[14] Jimmy J. Lin,et al. Large-scale machine learning at twitter , 2012, SIGMOD Conference.
[15] Sergey Ioffe,et al. Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning , 2016, AAAI.
[16] Danny H. K. Tsang,et al. M-Convex VM Consolidation: Towards a Better VM Workload Consolidation , 2016, IEEE Transactions on Cloud Computing.
[17] Evgenia Smirni,et al. CEDULE: A Scheduling Framework for Burstable Performance in Cloud Computing , 2018, 2018 IEEE International Conference on Autonomic Computing (ICAC).
[18] Jun Li,et al. ArA: Adaptive resource allocation for cloud computing environments under bursty workloads , 2011, 30th IEEE International Performance Computing and Communications Conference.
[19] Baochun Li,et al. Dynamic Cloud Resource Reservation via Cloud Brokerage , 2013, 2013 IEEE 33rd International Conference on Distributed Computing Systems.
[20] Bhuvan Urgaonkar,et al. BurScale: Using Burstable Instances for Cost-Effective Autoscaling in the Public Cloud , 2019, SoCC.
[21] Evgenia Smirni,et al. How to Supercharge the Amazon T2: Observations and Suggestions , 2017, 2017 IEEE 10th International Conference on Cloud Computing (CLOUD).
[22] Sriram Ramabhadran,et al. Cloud control with distributed rate limiting , 2007, SIGCOMM '07.
[23] David Wentzlaff,et al. Burstable Instances for Clouds: Performance Modeling, Equilibrium Analysis, and Revenue Maximization , 2019, IEEE INFOCOM 2019 - IEEE Conference on Computer Communications.
[24] Haifeng Chen,et al. Proactive Workload Management in Hybrid Cloud Computing , 2014, IEEE Transactions on Network and Service Management.
[25] Costas Courcoubetis,et al. Managing and pricing service level agreements for differentiated services , 1999, 1999 Seventh International Workshop on Quality of Service. IWQoS'99. (Cat. No.98EX354).
[26] Sangtae Ha,et al. Improving user QoE for residential broadband: Adaptive traffic management at the network edge , 2015, 2015 IEEE 23rd International Symposium on Quality of Service (IWQoS).
[27] Theodore P. Hill,et al. Finite-state Markov Chains , 2010 .
[28] Randy H. Katz,et al. Heterogeneity and dynamicity of clouds at scale: Google trace analysis , 2012, SoCC '12.
[29] Andrew M. Odlyzko,et al. Paris metro pricing for the internet , 1999, EC '99.
[30] Giuliano Casale,et al. Performance Prediction for Burstable Cloud Resources , 2016, VALUETOOLS.
[31] George Kesidis,et al. Using Burstable Instances in the Public Cloud: Why, When and How? , 2017, SIGMETRICS.
[32] Evgenia Smirni,et al. It's not a Sprint, it's a Marathon: Stretching Multi-resource Burstable Performance in Public Clouds (industry track) , 2019, Middleware Industry.
[33] Zhi-Quan Luo,et al. Semidefinite Relaxation of Quadratic Optimization Problems , 2010, IEEE Signal Processing Magazine.
[34] Hojin Park,et al. More IOPS for Less: Exploiting Burstable Storage in Public Clouds , 2020, HotCloud.
[35] George Kesidis,et al. Exploiting Spot and Burstable Instances for Improving the Cost-efficacy of In-Memory Caches on the Public Cloud , 2017, EuroSys.
[36] Mohsen Guizani,et al. Toward energy-efficient cloud computing: Prediction, consolidation, and overcommitment , 2015, IEEE Network.
[37] Baochun Li,et al. Reducing electricity demand charge for data centers with partial execution , 2013, e-Energy.