Placement autonomique de machines virtuelles sur un système de stockage hybride dans un cloud IaaS. (Autonomic virtual machines placement on hybrid storage system in IaaS cloud)

Les operateurs de cloud IaaS (Infrastructure as a Service) proposent a leurs clients des ressources virtualisees (CPU, stockage et reseau) sous forme de machines virtuelles (VM). L’explosion du marche du cloud les a contraints a optimiser tres finement l’utilisation de leurs centres de donnees afin de proposer des services attractifs a moindre cout. En plus des investissements lies a l’achat des infrastructures et de leur cout d’utilisation, la consommation energetique apparait comme un point de depense important (2% de la consommation mondiale) et en constante augmentation. Sa maitrise represente pour ces operateurs un levier tres interessant a exploiter. D’un point de vue technique, le controle de la consommation energetique s’appuie essentiellement sur les methodes de consolidation. Or la plupart d'entre elles ne prennent en compte que l’utilisation CPU des machines physiques (PM) pour le placement de VM. En effet, des etudes recentes ont montre que les systemes de stockage et les E/S disque constituent une part considerable de la consommation energetique d’un centre de donnees (entre 14% et 40%). Dans cette these nous introduisons un nouveau modele autonomique d’optimisation de placement de VM inspire de MAPE-K (Monitor, Analyze, Plan, Execute, Knowledge), et prenant en compte en plus du CPU, les E/S des VM ainsi que les systemes de stockage associes. Ainsi, notre premiere contribution est relative au developpement d’un outil de trace des E/S de VM multi-niveaux. Les traces collectees alimentent, dans l’etape Analyze, un modele de cout etendu dont l’originalite consiste a prendre en compte le profil d’acces des VM, les caracteristiques du systeme de stockage, ainsi que les contraintes economiques de l’environnement cloud. Nous analysons par ailleurs les caracteristiques des deux principales classes de stockage, pour aboutir a un modele hybride exploitant au mieux les avantages de chacune. En effet, les disques durs magnetiques (HDD) sont des supports de stockage a la fois energivores et peu performants compares aux unites de calcul. Neanmoins, leur prix par gigaoctet et leur longevite peuvent jouer en leur faveur. Contrairement aux HDD, les disques SSD a base de memoire flash sont plus performants et consomment peu d’energie. Leur prix eleve par gigaoctet et leur courte duree de vie (compares aux HDD) representent leurs contraintes majeures. L’etape Plan a donne lieu, d’une part, a une extension de l'outil de simulation CloudSim pour la prise en compte des E/S des VM, du caractere hybride du systeme de stockage, ainsi que la mise en oeuvre du modele de cout propose dans l'etape Analyze. Nous avons propose d’autre part, plusieurs heuristiques se basant sur notre modele de cout et que nous avons integrees dans CloudSim. Nous montrons finalement que notre approche permet d’ameliorer d’un facteur trois le cout de placement de VM obtenu par les approches existantes.

[1]  Pierre Olivier.  Estimation de performances et de consommation énergétique de systèmes de stockage à base de mémoire flash dans les systèmes embarqués. (Performance and power consumption estimation for embedded flash-based storage systems) , 2014 .

[2]  Xifeng Yan,et al.  Workload characterization and prediction in the cloud: A multiple time series approach , 2012, 2012 IEEE Network Operations and Management Symposium.

[3]  Laurent Lefèvre,et al.  A survey on techniques for improving the energy efficiency of large-scale distributed systems , 2014, ACM Comput. Surv..

[4]  Albert G. Greenberg,et al.  The cost of a cloud: research problems in data center networks , 2008, CCRV.

[5]  Thu D. Nguyen,et al.  Reducing electricity cost through virtual machine placement in high performance computing clouds , 2011, 2011 International Conference for High Performance Computing, Networking, Storage and Analysis (SC).

[6]  Ajay Gulati,et al.  Storage Workload Characterization and Consolidation in Virtualized Environments , 2008 .

[7]  Rajkumar Buyya,et al.  GridSim: a toolkit for the modeling and simulation of distributed resource management and scheduling for Grid computing , 2002, Concurr. Comput. Pract. Exp..

[8]  Brian J. Watson,et al.  Autonomic Virtual Machine Placement in the Data Center , 2008 .

[9]  Jing Xu,et al.  Multi-Objective Virtual Machine Placement in Virtualized Data Center Environments , 2010, 2010 IEEE/ACM Int'l Conference on Green Computing and Communications & Int'l Conference on Cyber, Physical and Social Computing.

[10]  Hai Jin,et al.  Performance and energy modeling for live migration of virtual machines , 2011, Cluster Computing.

[11]  Samuel Kounev,et al.  Automated Workload Characterization for I/O Performance Analysis in Virtualized Environments , 2015, Software Engineering.

[12]  Hong Jiang,et al.  HPDA: A hybrid parity-based disk array for enhanced performance and reliability , 2010, 2010 IEEE International Symposium on Parallel & Distributed Processing (IPDPS).

[13]  Anand Sivasubramaniam,et al.  HybridStore: A Cost-Efficient, High-Performance Storage System Combining SSDs and HDDs , 2011, 2011 IEEE 19th Annual International Symposium on Modelling, Analysis, and Simulation of Computer and Telecommunication Systems.

[14]  Stéphane Rubini,et al.  Considering I/O Processing in CloudSim for Performance and Energy Evaluation , 2016, ISC Workshops.

[15]  Alma Riska,et al.  Disk Drive Level Workload Characterization , 2006, USENIX Annual Technical Conference, General Track.

[16]  Jerome A. Rolia,et al.  Resource and virtualization costs up in the cloud: Models and design choices , 2011, 2011 IEEE/IFIP 41st International Conference on Dependable Systems & Networks (DSN).

[17]  Raouf Boutaba,et al.  Cloud computing: state-of-the-art and research challenges , 2010, Journal of Internet Services and Applications.

[18]  Rajkumar Buyya,et al.  SLA-Based Resource Provisioning for Heterogeneous Workloads in a Virtualized Cloud Datacenter , 2011, ICA3PP.

[19]  Rajkumar Buyya,et al.  SLA-based virtual machine management for heterogeneous workloads in a cloud datacenter , 2014, J. Netw. Comput. Appl..

[20]  Gargi Dasgupta,et al.  Server Workload Analysis for Power Minimization using Consolidation , 2009, USENIX Annual Technical Conference.

[21]  Liang Zhong,et al.  EnaCloud: An Energy-Saving Application Live Placement Approach for Cloud Computing Environments , 2009, 2009 IEEE International Conference on Cloud Computing.

[22]  Randy H. Katz,et al.  A case for redundant arrays of inexpensive disks (RAID) , 1988, SIGMOD '88.

[23]  Rajkumar Buyya,et al.  Energy-aware resource allocation heuristics for efficient management of data centers for Cloud computing , 2012, Future Gener. Comput. Syst..

[24]  Mario Südholt,et al.  VMPlaceS: A Generic Tool to Investigate and Compare VM Placement Algorithms , 2015, Euro-Par.

[25]  Lorenzo Rosasco,et al.  Are Loss Functions All the Same? , 2004, Neural Computation.

[26]  Euiseong Seo,et al.  Energy-Based Accounting and Scheduling of Virtual Machines in a Cloud System , 2011, 2011 IEEE/ACM International Conference on Green Computing and Communications.

[27]  Alma Riska,et al.  Evaluation of disk-level workloads at different time-scales , 2009, 2009 IEEE International Symposium on Workload Characterization (IISWC).

[28]  Paul H. Siegel,et al.  Characterizing flash memory: Anomalies, observations, and applications , 2009, 2009 42nd Annual IEEE/ACM International Symposium on Microarchitecture (MICRO).

[29]  José A. B. Fortes,et al.  On the Use of Machine Learning to Predict the Time and Resources Consumed by Applications , 2010, 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing.

[30]  Alma Riska,et al.  Disk Drive Workload Captured in Logs Collected During the Field Return Incoming Test , 2008, WASL.

[31]  Jeffrey Katcher,et al.  PostMark: A New File System Benchmark , 1997 .

[32]  Jan Broeckhove,et al.  Improving the Scalability of SimGrid Using Dynamic Routing , 2009, ICCS.

[33]  G. I. Meijer,et al.  Cooling Energy-Hungry Data Centers , 2010, Science.

[34]  Tal Garfinkel,et al.  The Design and Evolution of Live Storage Migration in VMware ESX , 2011, USENIX Annual Technical Conference.

[35]  Philippe Bonnet,et al.  Flash Device Support for Database Management , 2011, CIDR.

[36]  R. Card,et al.  Design and Implementation of the Second Extended Filesystem , 2001 .

[37]  Johan Tordsson,et al.  Cloud brokering mechanisms for optimized placement of virtual machines across multiple providers , 2012, Future Gener. Comput. Syst..

[38]  Emmanuel Lazard,et al.  Architecture et technologie des ordinateurs. Cours et exercices corrigés , 2013 .

[39]  Garth A. Gibson,et al.  RAID: high-performance, reliable secondary storage , 1994, CSUR.

[40]  Chandra Krintz,et al.  Paravirtualization for HPC Systems , 2006, ISPA Workshops.

[41]  Stéphane Rubini,et al.  Integrating I/Os in Cloudsim for Performance and Energy Estimation , 2017, ACM SIGOPS Oper. Syst. Rev..

[42]  J. Sikora.  Disk failures in the real world : What does an MTTF of 1 , 000 , 000 hours mean to you ? , 2007 .

[43]  Anirudha Sahoo,et al.  On Theory of VM Placement: Anomalies in Existing Methodologies and Their Mitigation Using a Novel Vector Based Approach , 2011, 2011 IEEE 4th International Conference on Cloud Computing.

[44]  Frank Bellosa,et al.  Energy Management for Hypervisor-Based Virtual Machines , 2007, USENIX Annual Technical Conference.

[45]  A. Kivity,et al.  kvm : the Linux Virtual Machine Monitor , 2007 .

[46]  Ali Raza Butt,et al.  CAST: Tiering Storage for Data Analytics in the Cloud , 2015, HPDC.

[47]  Anand Sivasubramaniam,et al.  HybridPlan: a capacity planning technique for projecting storage requirements in hybrid storage systems , 2013, The Journal of Supercomputing.

[48]  Fernando Romero.  Operating Systems. A concept-based approach, Second Edition D.M. Dhamdhere McGraw Hill, 2006 , 2009 .

[49]  Ralf Steinmetz,et al.  Let the Clouds Compute: Cost-Efficient Workload Distribution in Infrastructure Clouds , 2012, GECON.

[50]  Eric A. Hanushek.  4 – Ordinary Least Squares in Practice , 1977 .

[51]  Takahiro Hirofuchi,et al.  SimGrid VM: Virtual Machine Support for a Simulation Framework of Distributed Systems , 2018, IEEE Transactions on Cloud Computing.

[52]  Moustafa Ghanem,et al.  Lightweight Resource Scaling for Cloud Applications , 2012, 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (ccgrid 2012).

[53]  Toby Velte,et al.  Microsoft Virtualization with Hyper-V , 2009 .

[54]  Giang Son Tran,et al.  Cooperative Resource Management in a IaaS , 2015, 2015 IEEE 29th International Conference on Advanced Information Networking and Applications.

[55]  Yale N. Patt,et al.  On-line extraction of SCSI disk drive parameters , 1995, SIGMETRICS '95/PERFORMANCE '95.

[56]  Brendan Gregg,et al.  Systems Performance: Enterprise and the Cloud , 2013 .

[57]  David S. Johnson,et al.  Near-optimal bin packing algorithms , 1973 .

[58]  Raj Jain,et al.  The art of computer systems performance analysis - techniques for experimental design, measurement, simulation, and modeling , 1991, Wiley professional computing.

[59]  Zoltán Ádám Mann,et al.  Allocation of Virtual Machines in Cloud Data Centers—A Survey of Problem Models and Optimization Algorithms , 2015, ACM Comput. Surv..

[60]  Samir Tata,et al.  Traffic-Aware Virtual Machine Migration Scheduling Problem in Geographically Distributed Data Centers , 2016, 2016 IEEE 9th International Conference on Cloud Computing (CLOUD).

[61]  Yuan Chen.  Flash memory reliability NEPP 2008 task final report , 2009 .

[62]  Johan Tordsson,et al.  Virtual Machine Placement for Predictable and Time-Constrained Peak Loads , 2011, GECON.

[63]  A. Tal.  White Paper Two Flash Technologies Compared : NOR vs NAND , 2002 .

[64]  Yanpei Chen,et al.  The Truth About MapReduce Performance on SSDs , 2014, LISA.

[65]  Eric Senn,et al.  A Tracing Toolset for Embedded Linux Flash File Systems , 2014, VALUETOOLS.

[66]  Yellu Sreenivasulu,et al.  FAST TRANSPARENT MIGRATION FOR VIRTUAL MACHINES , 2014 .

[67]  Gokul B. Kandiraju,et al.  Modeling and simulating flash based solid-state disks for operating systems , 2010, WOSP/SIPEW '10.

[68]  Abdullah Al Mamun,et al.  Hard Disk Drive: Mechatronics and Control , 2006 .

[69]  W. Cleveland.  Robust Locally Weighted Regression and Smoothing Scatterplots , 1979 .

[70]  Petter Svärd,et al.  Cost-Optimal Cloud Service Placement under Dynamic Pricing Schemes , 2013, 2013 IEEE/ACM 6th International Conference on Utility and Cloud Computing.

[71]  Vipin Chaudhary,et al.  VMeter: Power modelling for virtualized clouds , 2010, 2010 IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum (IPDPSW).

[72]  Waltenegus Dargie,et al.  Does Live Migration of Virtual Machines Cost Energy? , 2013, 2013 IEEE 27th International Conference on Advanced Information Networking and Applications (AINA).

[73]  John Wilkes,et al.  An introduction to disk drive modeling , 1994, Computer.

[74]  Arnaud Legrand,et al.  Adding Storage Simulation Capacities to the SimGrid Toolkit: Concepts, Models, and API , 2015, 2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing.

[75]  Sang Lyul Min,et al.  A space-efficient flash translation layer for CompactFlash systems , 2002, IEEE Trans. Consumer Electron..

[76]  Rajkumar Buyya,et al.  CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms , 2011, Softw. Pract. Exp..

[77]  Jalil Boukhobza,et al.  A Cost Model for DBaaS Storage , 2016, DEXA.

[78]  David Bernstein,et al.  Containers and Cloud: From LXC to Docker to Kubernetes , 2014, IEEE Cloud Computing.

[79]  Jan Waller,et al.  Performance Benchmarking of Application Monitoring Frameworks , 2014, Softwaretechnik-Trends.

[80]  Stéphane Rubini,et al.  A Cost Model for Virtual Machine Storage in Cloud IaaS Context , 2016, 2016 24th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP).

[81]  Lipeng Wan,et al.  SSD-optimized workload placement with adaptive learning and classification in HPC environments , 2014, 2014 30th Symposium on Mass Storage Systems and Technologies (MSST).

[82]  Jan Broeckhove,et al.  Cost-Optimal Scheduling in Hybrid IaaS Clouds for Deadline Constrained Workloads , 2010, 2010 IEEE 3rd International Conference on Cloud Computing.

[83]  Philippe Bonnet,et al.  Linux block IO: introducing multi-queue SSD access on multi-core systems , 2013, SYSTOR '13.

[84]  Mahesh Balakrishnan,et al.  Extending SSD Lifetimes with Disk-Based Write Caches , 2010, FAST.

[85]  J. Friedman.  Multivariate adaptive regression splines , 1990 .

[86]  Jeffrey O. Kephart,et al.  The Vision of Autonomic Computing , 2003, Computer.

[87]  Jalil Boukhobza.  Flashing in the Cloud: Shedding Some Light on NAND Flash Memory Storage Systems , 2019 .

[88]  Seiichi Sugaya.  Trends in enterprise hard disk drives , 2006 .

[89]  Joonwon Lee,et al.  A comprehensive study of energy efficiency and performance of flash-based SSD , 2011, J. Syst. Archit..

[90]  Hai Nam Tran.  Cache memory aware priority assignment and scheduling simulation of real-time embedded systems. (Affectation de priorité et simulation d'ordonnancement de systèmes temps réel embarqués avec prise en compte de l'effet des mémoires cache) , 2017 .

[91]  Jalil Boukhobza,et al.  Flash Memory Integration: Performance and Energy Issues , 2017 .

[92]  Antony I. T. Rowstron,et al.  Migrating server storage to SSDs: analysis of tradeoffs , 2009, EuroSys '09.

[93]  Edward G. Coffman,et al.  Approximation algorithms for bin packing: a survey , 1996 .

[94]  Heeseung Jo,et al.  SSD-HDD-Hybrid Virtual Disk in Consolidated Environments , 2009, Euro-Par Workshops.

[95]  Vasileios Pappas,et al.  Improving the Scalability of Data Center Networks with Traffic-aware Virtual Machine Placement , 2010, 2010 Proceedings IEEE INFOCOM.

[96]  Thomas Sandholm,et al.  What's inside the Cloud? An architectural map of the Cloud landscape , 2009, 2009 ICSE Workshop on Software Engineering Challenges of Cloud Computing.

[97]  Irfan Ahmad,et al.  Modeling workloads and devices for IO load balancing in virtualized environments , 2010, PERV.

[98]  Cosimo Anglano,et al.  Exploiting VM Migration for the Automated Power and Performance Management of Green Cloud Computing Systems , 2012, E2DC.

[99]  André Brinkmann,et al.  Non-intrusive virtualization management using libvirt , 2010, 2010 Design, Automation & Test in Europe Conference & Exhibition (DATE 2010).

[100]  Anne-Cécile Orgerie,et al.  How Much Does a VM Cost? Energy-Proportional Accounting in VM-Based Environments , 2016, 2016 24th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP).

[101]  Marc Frîncu,et al.  Multi-objective Meta-heuristics for Scheduling Applications with High Availability Requirements and Cost Constraints in Multi-Cloud Environments , 2011, 2011 Fourth IEEE International Conference on Utility and Cloud Computing.

[102]  Tian Luo,et al.  CAFTL: A Content-Aware Flash Translation Layer Enhancing the Lifespan of Flash Memory based Solid State Drives , 2011, FAST.

[103]  Luca Crippa,et al.  Inside NAND Flash Memories , 2010 .

[104]  Gene H. Golub,et al.  Generalized cross-validation as a method for choosing a good ridge parameter , 1979, Milestones in Matrix Computation.

[105]  Feng Xia,et al.  A survey on virtual machine migration and server consolidation frameworks for cloud data centers , 2015, J. Netw. Comput. Appl..

[106]  Erez Zadok,et al.  Filebench: A Flexible Framework for File System Benchmarking , 2016, login Usenix Mag..

[107]  Zhichao Li,et al.  On the Importance of Evaluating Storage Systems' $Costs , 2014, HotStorage.

[108]  P. Mell,et al.  The NIST Definition of Cloud Computing , 2011 .

[109]  Liang Liu,et al.  A multi-objective ant colony system algorithm for virtual machine placement in cloud computing , 2013, J. Comput. Syst. Sci..

[110]  Zoltán Ádám Mann,et al.  Multicore-Aware Virtual Machine Placement in Cloud Data Centers , 2016, IEEE Transactions on Computers.

[111]  David M. Eyers,et al.  IO Tetris: Deep Storage Consolidation for the Cloud via Fine-Grained Workload Analysis , 2011, 2011 IEEE 4th International Conference on Cloud Computing.

[112]  Fang Liu,et al.  Optimizing virtual machine live storage migration in heterogeneous storage environment , 2013, VEE '13.

[113]  Jignesh M. Patel,et al.  Towards Cost-Effective Storage Provisioning for DBMSs , 2011, Proc. VLDB Endow..

[114]  Devarshi Ghoshal,et al.  I/O performance of virtualized cloud environments , 2011, DataCloud-SC '11.

[115]  Takahiro Hirofuchi,et al.  Adding a Live Migration Model into SimGrid: One More Step Toward the Simulation of Infrastructure-as-a-Service Concerns , 2013, 2013 IEEE 5th International Conference on Cloud Computing Technology and Science.

[116]  Sandip Roy,et al.  On Demand IOPS Calculation in Cloud Environment to Ease Linux-Based Application Delivery , 2017 .

[117]  Dan Kusnetzky,et al.  Virtualization: A Manager's Guide , 2011 .

[118]  Kenneth A. Ross,et al.  An Object Placement Advisor for DB2 Using Solid State Storage , 2009, Proc. VLDB Endow..

[119]  Ethan L. Miller,et al.  The effectiveness of deduplication on virtual machine disk images , 2009, SYSTOR '09.

[120]  Rajkumar Buyya,et al.  Energy Efficient Allocation of Virtual Machines in Cloud Data Centers , 2010, 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing.

[121]  L. Vivier,et al.  The new ext 4 filesystem : current status and future plans , 2007 .

[122]  Randy H. Katz,et al.  A view of cloud computing , 2010, CACM.

[123]  Bin Zhang,et al.  A deep learning approach for VM workload prediction in the cloud , 2016, 2016 17th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD).

[124]  Marco Cesati,et al.  Understanding the Linux Kernel - from I / O ports to process management: covers Linux Kernel version 2.4 (2. ed.) , 2005 .

[125]  Stéphane Rubini,et al.  A multi-level I/O tracer for timing and performance storage systems in IaaS cloud , 2014, REACTION.

[126]  Al Muller,et al.  Virtualization with vmware esx server , 2005 .

[127]  Richard W. Hamming,et al.  Error detecting and error correcting codes , 1950 .

[128]  Lars Wirzenius.  The Linux System Administrator's Guide , 2007 .

[129]  James O'reilly.  Network Storage: Tools and Technologies for Storing Your Company’s Data , 2016 .

[130]  Feng Zhao,et al.  Virtual machine power metering and provisioning , 2010, SoCC '10.

[131]  Hyojun Kim,et al.  BPLRU: A Buffer Management Scheme for Improving Random Writes in Flash Storage , 2008, FAST.

[132]  Yao Lu,et al.  RVLBPNN: A Workload Forecasting Model for Smart Cloud Computing , 2016, Sci. Program..

[133]  Henri Casanova,et al.  Simgrid: a toolkit for the simulation of application scheduling , 2001, Proceedings First IEEE/ACM International Symposium on Cluster Computing and the Grid.

[134]  Navendu Jain,et al.  Managing cost, performance, and reliability tradeoffs for energy-aware server provisioning , 2011, 2011 Proceedings IEEE INFOCOM.

[135]  Rajkumar Buyya,et al.  Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in Cloud data centers , 2012, Concurr. Comput. Pract. Exp..

[136]  Olivier Barais,et al.  Seeking for the Optimal Energy Modelisation Accuracy to Allow Efficient Datacenter Optimizations , 2016, 2016 16th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid).

[137]  Tharam S. Dillon,et al.  A Survey on SLA and Performance Measurement in Cloud Computing , 2011, OTM Conferences.

[138]  Ada Gavrilovska,et al.  VM power metering: feasibility and challenges , 2011, PERV.

[139]  Qiang Huang,et al.  Power Consumption of Virtual Machine Live Migration in Clouds , 2011, 2011 Third International Conference on Communications and Mobile Computing.

[140]  Rina Panigrahy,et al.  Design Tradeoffs for SSD Performance , 2008, USENIX ATC.

[141]  Dávid Bartók,et al.  A branch-and-bound approach to virtual machine placement , 2016 .

[142]  Irfan Ahmad.  Easy and Efficient Disk I/O Workload Characterization in VMware ESX Server , 2007, 2007 IEEE 10th International Symposium on Workload Characterization.

[143]  Jerome A. Rolia,et al.  An integrated approach to resource pool management: Policies, efficiency and quality metrics , 2008, 2008 IEEE International Conference on Dependable Systems and Networks With FTCS and DCC (DSN).

[144]  Fabrice Bellard,et al.  QEMU, a Fast and Portable Dynamic Translator , 2005, USENIX Annual Technical Conference, FREENIX Track.

[145]  Olivier Barais,et al.  Enhancing Cloud Energy Models for Optimizing Datacenters Efficiency , 2015, 2015 International Conference on Cloud and Autonomic Computing.

[146]  Romain Rouvoy,et al.  Process-level power estimation in VM-based systems , 2015, EuroSys.

[147]  W. Cleveland,et al.  Smoothing by Local Regression: Principles and Methods , 1996 .

[148]  Norman W. Paton,et al.  Adaptation in cloud resource configuration: a survey , 2016, Journal of Cloud Computing.

[149]  Darrell D. E. Long,et al.  Which Storage Device Is the Greenest? Modeling the Energy Cost of I/O Workloads , 2014, 2014 IEEE 22nd International Symposium on Modelling, Analysis & Simulation of Computer and Telecommunication Systems.