Workloads in the clouds

Despite the fast evolution of cloud computing, up to now the characterization of cloud workloads has received little attention. Nevertheless, a deep understanding of their properties and behavior is essential for an effective deployment of cloud technologies and for achieving the desired service levels. While the general principles applied to parallel and distributed systems are still valid, several peculiarities require the attention of both researchers and practitioners. The aim of this chapter is to highlight the most relevant characteristics of cloud workloads as well as identify and discuss the main issues related to their deployment and the gaps that need to be filled.

[1]  Ewa Deelman,et al.  Dynamic and Fault-Tolerant Clustering for Scientific Workflows , 2016, IEEE Transactions on Cloud Computing.

[2]  Weisong Shi,et al.  Workload characterization on a production Hadoop cluster: A case study on Taobao , 2012, 2012 IEEE International Symposium on Workload Characterization (IISWC).

[3]  Giuseppe Serazzi,et al.  On load balancing: a mix-aware algorithm for heterogeneous systems , 2013, ICPE '13.

[4]  Kishor S. Trivedi,et al.  Software Rejuvenation and its Application in Distributed Systems , 2015 .

[5]  Jun Li,et al.  ArA: Adaptive resource allocation for cloud computing environments under bursty workloads , 2011, 30th IEEE International Performance Computing and Communications Conference.

[6]  Jie Xu,et al.  Analysis, Modeling and Simulation of Workload Patterns in a Large-Scale Utility Cloud , 2014, IEEE Transactions on Cloud Computing.

[7]  Albert Y. Zomaya,et al.  Profiling Applications for Virtual Machine Placement in Clouds , 2011, 2011 IEEE 4th International Conference on Cloud Computing.

[8]  Randy H. Katz,et al.  Heterogeneity and dynamicity of clouds at scale: Google trace analysis , 2012, SoCC '12.

[9]  Domenico Cotroneo,et al.  A survey of software aging and rejuvenation studies , 2014, ACM J. Emerg. Technol. Comput. Syst..

[10]  Jose M. Alcaraz Calero,et al.  Comparative analysis of architectures for monitoring cloud computing infrastructures , 2015, Future Gener. Comput. Syst..

[11]  Stephen Dawson,et al.  Markovian Workload Characterization for QoS Prediction in the Cloud , 2011, 2011 IEEE 4th International Conference on Cloud Computing.

[12]  Marius Hillenbrand,et al.  High performance cloud computing , 2013, Future Gener. Comput. Syst..

[13]  M. Tech,et al.  Dynamic Heterogeneity-Aware Resource Provisioning in the Cloud , 2015 .

[14]  Ioan Raicu,et al.  Many-Task Computing: Bridging the Gap between High Throughput Computing and High Performance Computing , 2009 .

[15]  Gang Ren,et al.  Google-Wide Profiling: A Continuous Profiling Infrastructure for Data Centers , 2010, IEEE Micro.

[16]  Rubén S. Montero,et al.  Multicloud Deployment of Computing Clusters for Loosely Coupled MTC Applications , 2011, IEEE Transactions on Parallel and Distributed Systems.

[17]  Balaji Viswanathan,et al.  SmartScale: Automatic Application Scaling in Enterprise Clouds , 2012, 2012 IEEE Fifth International Conference on Cloud Computing.

[18]  Changjun Jiang,et al.  Heterogeneity-Aware Workload Placement and Migration in Distributed Sustainable Datacenters , 2014, 2014 IEEE 28th International Parallel and Distributed Processing Symposium.

[19]  Ravishankar K. Iyer,et al.  Characterization of operational failures from a business data processing SaaS platform , 2014, ICSE Companion.

[20]  Rajkumar Buyya,et al.  Article in Press Future Generation Computer Systems ( ) – Future Generation Computer Systems Cloud Computing and Emerging It Platforms: Vision, Hype, and Reality for Delivering Computing as the 5th Utility , 2022 .

[21]  Swapna S. Gokhale,et al.  Cloud Incident Data: An Empirical Analysis , 2013, 2013 IEEE International Conference on Cloud Engineering (IC2E).

[22]  Farokh B. Bastani,et al.  Workload Estimation for Improving Resource Management Decisions in the Cloud , 2015, 2015 IEEE Twelfth International Symposium on Autonomous Decentralized Systems.

[23]  Xin Chen,et al.  Failure Prediction of Jobs in Compute Clouds: A Google Cluster Case Study , 2014, 2014 IEEE International Symposium on Software Reliability Engineering Workshops.

[24]  Gang Quan,et al.  On-Line Scheduling of Real-Time Services for Cloud Computing , 2010, 2010 6th World Congress on Services.

[25]  Jianwei Yin,et al.  System resource utilization analysis and prediction for cloud based applications under bursty workloads , 2014, Inf. Sci..

[26]  Willy Zwaenepoel,et al.  Performance profiling of virtual machines , 2011, VEE '11.

[27]  Alexandru Iosup,et al.  Performance Analysis of Cloud Computing Services for Many-Tasks Scientific Computing , 2011, IEEE Transactions on Parallel and Distributed Systems.

[28]  Franck Cappello,et al.  Characterizing and modeling cloud applications/jobs on a Google data center , 2014, The Journal of Supercomputing.

[29]  Calton Pu,et al.  Variations in Performance and Scalability: An Experimental Study in IaaS Clouds Using Multi-Tier Workloads , 2014, IEEE Transactions on Services Computing.

[30]  Paul Marshall,et al.  Elastic Site: Using Clouds to Elastically Extend Site Resources , 2010, 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing.

[31]  Song Jiang,et al.  Workload analysis of a large-scale key-value store , 2012, SIGMETRICS '12.

[32]  Rajkumar Buyya,et al.  Performance Modelling and Simulation of Three-Tier Applications in Cloud and Multi-Cloud Environments , 2015, Comput. J..

[33]  Olivier Beaumont,et al.  Analyzing real cluster data for formulating allocation algorithms in cloud platforms , 2016, Parallel Comput..

[34]  Rajkumar Buyya,et al.  A Particle Swarm Optimization-Based Heuristic for Scheduling Workflow Applications in Cloud Computing Environments , 2010, 2010 24th IEEE International Conference on Advanced Information Networking and Applications.

[35]  Odej Kao,et al.  Exploiting Dynamic Resource Allocation for Efficient Parallel Data Processing in the Cloud , 2011, IEEE Transactions on Parallel and Distributed Systems.

[36]  Jorge-Arnulfo Quiané-Ruiz,et al.  Runtime measurements in the cloud , 2010, Proc. VLDB Endow..

[37]  Jin-Soo Kim,et al.  Cost optimized provisioning of elastic resources for application workflows , 2011, Future Gener. Comput. Syst..

[38]  David M. Nicol,et al.  Trust mechanisms for cloud computing , 2013, Journal of Cloud Computing: Advances, Systems and Applications.

[39]  Inderveer Chana,et al.  Intelligent failure prediction models for scientific workflows , 2015, Expert Syst. Appl..

[40]  Fadi H. Gebara,et al.  Introduction to special issue on reliability and device degradation in emerging technologies , 2014, JETC.

[41]  Ke Wang,et al.  Achieving Efficient Distributed Scheduling with Message Queues in the Cloud for Many-Task Computing and High-Performance Computing , 2014, 2014 14th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing.

[42]  Theo Lynn,et al.  A survey of Cloud monitoring tools: Taxonomy, capabilities and objectives , 2014, J. Parallel Distributed Comput..

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

[44]  Archana Ganapathi,et al.  Statistics-driven workload modeling for the Cloud , 2010, 2010 IEEE 26th International Conference on Data Engineering Workshops (ICDEW 2010).

[45]  Yanpei Chen,et al.  Interactive Analytical Processing in Big Data Systems: A Cross-Industry Study of MapReduce Workloads , 2012, Proc. VLDB Endow..

[46]  Bingsheng He,et al.  A Survey of Resource Management in Multi-Tier Web Applications , 2014, IEEE Communications Surveys & Tutorials.

[47]  Chita R. Das,et al.  Towards characterizing cloud backend workloads: insights from Google compute clusters , 2010, PERV.

[48]  Jie Xu,et al.  An Empirical Failure-Analysis of a Large-Scale Cloud Computing Environment , 2014, 2014 IEEE 15th International Symposium on High-Assurance Systems Engineering.

[49]  Keqin Li,et al.  Future Generation Computer Systems ( ) – Future Generation Computer Systems Multi-objective Scheduling of Many Tasks in Cloud Platforms , 2022 .

[50]  Kannan Govindarajan,et al.  CLOUDRB: A framework for scheduling and managing High-Performance Computing (HPC) applications in science cloud , 2014, Future Gener. Comput. Syst..

[51]  Depei Qian,et al.  MapReduce Workload Modeling with Statistical Approach , 2011, Journal of Grid Computing.

[52]  Carlos Becker Westphall,et al.  Cloud resource management: A survey on forecasting and profiling models , 2015, J. Netw. Comput. Appl..

[53]  Prashant J. Shenoy,et al.  Provisioning multi-tier cloud applications using statistical bounds on sojourn time , 2012, ICAC '12.

[54]  Jennifer G. Dy,et al.  Workload Characterization at the Virtualization Layer , 2011, 2011 IEEE 19th Annual International Symposium on Modelling, Analysis, and Simulation of Computer and Telecommunication Systems.

[55]  Massoud Pedram,et al.  Prediction and control of bursty cloud workloads: A fractal framework , 2014, 2014 International Conference on Hardware/Software Codesign and System Synthesis (CODES+ISSS).

[56]  Xin Chen,et al.  Failure Analysis of Jobs in Compute Clouds: A Google Cluster Case Study , 2014, 2014 IEEE 25th International Symposium on Software Reliability Engineering.

[57]  Kishor S. Trivedi,et al.  Software aging in the eucalyptus cloud computing infrastructure , 2014, ACM J. Emerg. Technol. Comput. Syst..

[58]  Moustafa Ghanem,et al.  Future Generation Computer Systems ( ) – Future Generation Computer Systems Enabling Cost-aware and Adaptive Elasticity of Multi-tier Cloud Applications , 2022 .

[59]  Yong Zhao,et al.  Opportunities and Challenges in Running Scientific Workflows on the Cloud , 2011, 2011 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery.

[60]  Jun Zhang,et al.  Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches , 2015, ACM Comput. Surv..

[61]  Ravi Iyer,et al.  Modeling virtual machine performance: challenges and approaches , 2010, PERV.

[62]  Sangyeun Cho,et al.  Characterizing Machines and Workloads on a Google Cluster , 2012, 2012 41st International Conference on Parallel Processing Workshops.

[63]  Ewa Deelman,et al.  Failure analysis of distributed scientific workflows executing in the cloud , 2012, 2012 8th international conference on network and service management (cnsm) and 2012 workshop on systems virtualiztion management (svm).

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

[65]  Ann L. Chervenak,et al.  Characterizing and profiling scientific workflows , 2013, Future Gener. Comput. Syst..

[66]  Alexandru Iosup,et al.  Statistical Characterization of Business-Critical Workloads Hosted in Cloud Datacenters , 2015, 2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing.

[67]  Rolf Stadler,et al.  Resource Management in Clouds: Survey and Research Challenges , 2015, Journal of Network and Systems Management.

[68]  Ernesto Damiani,et al.  Scalability Patterns for Platform-as-a-Service , 2012, 2012 IEEE Fifth International Conference on Cloud Computing.

[69]  Massoud Pedram,et al.  Trace-Based Analysis and Prediction of Cloud Computing User Behavior Using the Fractal Modeling Technique , 2014, 2014 IEEE International Congress on Big Data.

[70]  Kuo-Chan Huang,et al.  Scheduling Concurrent Workflows in HPC Cloud through Exploiting Schedule Gaps , 2011, ICA3PP.

[71]  Qingbo Wu,et al.  Workflow scheduling in cloud: a survey , 2015, The Journal of Supercomputing.

[72]  Thomas Magedanz,et al.  Monitoring as a service for cloud environments , 2014, 2014 IEEE Fifth International Conference on Communications and Electronics (ICCE).

[73]  Rajiv Ranjan,et al.  An overview of the commercial cloud monitoring tools: research dimensions, design issues, and state-of-the-art , 2013, Computing.

[74]  Antonio Puliafito,et al.  Workload-Based Software Rejuvenation in Cloud Systems , 2013, IEEE Transactions on Computers.

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

[76]  Shicong Meng,et al.  Enhanced Monitoring-as-a-Service for Effective Cloud Management , 2013, IEEE Transactions on Computers.

[77]  Prashant J. Shenoy,et al.  Autonomic mix-aware provisioning for non-stationary data center workloads , 2010, ICAC '10.

[78]  Evgenia Smirni,et al.  Multi-resource characterization and their (in)dependencies in production datacenters , 2014, 2014 IEEE Network Operations and Management Symposium (NOMS).

[79]  Xiaorong Li,et al.  Multi-Objective Game Theoretic Schedulingof Bag-of-Tasks Workflows on Hybrid Clouds , 2014, IEEE Transactions on Cloud Computing.

[80]  Archana Ganapathi,et al.  Analysis and Lessons from a Publicly Available Google Cluster Trace , 2010 .

[81]  El-Ghazali Talbi,et al.  A Pareto-based metaheuristic for scheduling HPC applications on a geographically distributed cloud federation , 2013, Cluster Computing.

[82]  Marty Humphrey,et al.  Scaling and Scheduling to Maximize Application Performance within Budget Constraints in Cloud Workflows , 2013, 2013 IEEE 27th International Symposium on Parallel and Distributed Processing.

[83]  Jelena V. Misic,et al.  Performance Analysis of Cloud Computing Centers Using M/G/m/m+r Queuing Systems , 2012, IEEE Transactions on Parallel and Distributed Systems.

[84]  Maristela Holanda,et al.  ACOsched: A scheduling algorithm in a federated cloud infrastructure for bioinformatics applications , 2013, 2013 IEEE International Conference on Bioinformatics and Biomedicine.

[85]  Qian Zhu,et al.  Resource Provisioning with Budget Constraints for Adaptive Applications in Cloud Environments , 2010, IEEE Transactions on Services Computing.

[86]  Craig A. Knoblock,et al.  A Survey of Digital Map Processing Techniques , 2014, ACM Comput. Surv..

[87]  Sanjay Ghemawat,et al.  MapReduce: Simplified Data Processing on Large Clusters , 2004, OSDI.

[88]  Jeffrey S. Chase,et al.  Automated control in cloud computing: challenges and opportunities , 2009, ACDC '09.

[89]  Sally A. McKee,et al.  Understanding the behavior of in-memory computing workloads , 2014, 2014 IEEE International Symposium on Workload Characterization (IISWC).

[90]  Eric Wohlstadter,et al.  Profiling-as-a-Service: Adaptive Scalable Resource Profiling for the Cloud in the Cloud , 2011, ICSOC.

[91]  Zhiliang Zhu,et al.  Dynamic Provisioning Modeling for Virtualized Multi-tier Applications in Cloud Data Center , 2010, 2010 IEEE 3rd International Conference on Cloud Computing.

[92]  Shuhui Li,et al.  Profit and Penalty Aware Scheduling for Real-Time Online Services , 2012, IEEE Transactions on Industrial Informatics.

[93]  Christos Faloutsos,et al.  Beyond Poisson: Modeling Inter-Arrival Time of Requests in a Datacenter , 2014, PAKDD.