HeporCloud: An energy and performance efficient resource orchestrator for hybrid heterogeneous cloud computing environments

Abstract In major Information Technology (IT) companies such as Google, Rackspace and Amazon Web Services (AWS), virtualisation and containerisation technologies are usually used to execute customers' workloads and applications. The computational resources are provided through large-scale datacenters, which consume substantial amount of energy and have, therefore, ecological impacts. Since long, Google runs users' applications in containers, Rackspace offers bare-metal hardware, whereas AWS runs them either in VMs (EC2), containers (ECS) and/or containers inside VMs (Lambda); therefore, making resource management a tedious activity. The role of a resource management system is of the greatest importance, principally, if IT companies practice various kinds of sand-boxing technologies, for instance, bare-metal, VMs, containers, and/or nested containers in their datacenters (hybrid platforms). The absence of centralised, workload-aware resource managers and consolidation policies produces questions on datacenters energy efficiency, workloads performance, and users' costs. In this paper, we demonstrate, through several experiments, using the Google workload data for 12,583 hosts and approximately one million tasks that belong to four different kinds of workload, the likelihood of: (i) using workload-aware resource managers in hybrid clouds; (ii) achieving energy and cost savings, in heterogeneous hybrid datacenters such that the workload performance is not affected, negatively; and (iii) how various allocation policies, combined with different migration approaches, will impact on datacenter's energy and performance efficiencies. Using plausible assumptions for hybrid datacenters set-up, our empirical evaluation suggests that, for no migration, a single scheduler is at most 16.86% more energy efficient than distributed schedulers. Moreover, when migrations are considered, our resource manager can save up to 45.61% energy and can improve up to 17.9% workload performance.

[1]  Richard E. Brown,et al.  United States Data Center Energy Usage Report , 2016 .

[2]  Dan Wang,et al.  Communication-Aware Container Placement and Reassignment in Large-Scale Internet Data Centers , 2019, IEEE Journal on Selected Areas in Communications.

[3]  Nicolas Vuillerme,et al.  Software Consolidation as an Efficient Energy and Cost Saving Solution for a SaaS/PaaS Cloud Model , 2015, Euro-Par.

[4]  Shripad Nadgowda,et al.  Voyager: Complete Container State Migration , 2017, 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS).

[5]  Ricardo Bianchini,et al.  Resource Central: Understanding and Predicting Workloads for Improved Resource Management in Large Cloud Platforms , 2017, SOSP.

[6]  Yonggang Wen,et al.  Data Center Energy Consumption Modeling: A Survey , 2016, IEEE Communications Surveys & Tutorials.

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

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

[9]  V. Caron,et al.  United states. , 2018, Nursing standard (Royal College of Nursing (Great Britain) : 1987).

[10]  Albert Y. Zomaya,et al.  A Multi-Objective Optimization Scheme for Job Scheduling in Sustainable Cloud Data Centers , 2022, IEEE Transactions on Cloud Computing.

[11]  Rajkumar Buyya,et al.  ContainerCloudSim: An environment for modeling and simulation of containers in cloud data centers , 2017, Softw. Pract. Exp..

[12]  Ian Foster,et al.  Predicting application run times with historical information , 2004, J. Parallel Distributed Comput..

[13]  Lucas Chaufournier,et al.  Containers and Virtual Machines at Scale: A Comparative Study , 2016, Middleware.

[14]  Sai Venkat Naresh Kotikalapudi.  Comparing Live Migration between Linux Containers and Kernel Virtual Machine : Investigation study in terms of parameters , 2017 .

[15]  Henryk Krawczyk,et al.  Multi-level Virtualization and Its Impact on System Performance in Cloud Computing , 2016, CN.

[16]  Rahim Khan,et al.  H$^2$—A Hybrid Heterogeneity Aware Resource Orchestrator for Cloud Platforms , 2019, IEEE Systems Journal.

[17]  Muhammad Zakarya,et al.  An Extended Energy-Aware Cost Recovery Approach for Virtual Machine Migration , 2019, IEEE Systems Journal.

[18]  Lee Gillam,et al.  Performance Evaluation for Cost-Efficient Public Infrastructure Cloud Use , 2014, GECON.

[19]  John O'Loughlin.  A workload-specific performance brokerage for infrastructure clouds , 2018 .

[20]  Mohsen Guizani,et al.  An Energy-Efficient VM Prediction and Migration Framework for Overcommitted Clouds , 2018, IEEE Transactions on Cloud Computing.

[21]  Richard O. Sinnott,et al.  A performance comparison of container-based technologies for the Cloud , 2017, Future Gener. Comput. Syst..

[22]  Wolf-Dietrich Weber,et al.  Power provisioning for a warehouse-sized computer , 2007, ISCA '07.

[23]  Helen D. Karatza,et al.  Performance and Overhead Study of Containers Running on Top of Virtual Machines , 2017, 2017 IEEE 19th Conference on Business Informatics (CBI).

[24]  Gregory R. Ganger,et al.  Bigger , Longer , Fewer : what do cluster jobs look like outside Google ? , 2017 .

[25]  Karim Djemame,et al.  Energy Prediction for Cloud Workload Patterns , 2016, GECON.

[26]  Mohammad Masdari,et al.  A survey and classification of the workload forecasting methods in cloud computing , 2019, Cluster Computing.

[27]  Lee Gillam,et al.  An Energy Aware Cost Recovery Approach for Virtual Machine Migration , 2016, GECON.

[28]  Y. C. Tay,et al.  A Performance Comparison of Containers and Virtual Machines in Workload Migration Context , 2017, 2017 IEEE 37th International Conference on Distributed Computing Systems Workshops (ICDCSW).

[29]  Claris Castillo,et al.  A Cloud-Agnostic Framework to Enable Cost-Aware Scheduling of Applications in a Multi-Cloud Environment , 2020, NOMS 2020 - 2020 IEEE/IFIP Network Operations and Management Symposium.

[30]  Guillaume Pierre,et al.  An experiment-driven energy consumption model for virtual machine management systems , 2018, Sustain. Comput. Informatics Syst..

[31]  Yang Hu,et al.  Multi-objective Container Deployment on Heterogeneous Clusters , 2019, 2019 19th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID).

[32]  Mor Harchol-Balter,et al.  Stochastic Models and Analysis for Resource Management in Server Farms , 2011 .

[33]  Malgorzata Steinder,et al.  Performance Evaluation of Microservices Architectures Using Containers , 2015, 2015 IEEE 14th International Symposium on Network Computing and Applications.

[34]  Valerio Schiavoni,et al.  Heats: Heterogeneity-and Energy-Aware Task-Based Scheduling , 2019, 2019 27th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP).

[35]  Hai Jin,et al.  A Performance Study of Containers in Cloud Environment , 2016, APSCC.

[36]  Mor Harchol-Balter,et al.  Energy-Efficient Dynamic Capacity Provisioning in Server Farms , 2010 .

[37]  Lee Gillam,et al.  Managing energy, performance and cost in large scale heterogeneous datacenters using migrations , 2019, Future Gener. Comput. Syst..

[38]  Mainak Adhikari,et al.  Multi-objective accelerated particle swarm optimization with a container-based scheduling for Internet-of-Things in cloud environment , 2019, J. Netw. Comput. Appl..

[39]  Muhammad Zakarya,et al.  Energy and performance aware resource management in heterogeneous cloud datacenters , 2017 .

[40]  Adrien Lebre,et al.  Putting the Next 500 VM Placement Algorithms to the Acid Test: The Infrastructure Provider Viewpoint , 2018, IEEE Transactions on Parallel and Distributed Systems.

[41]  Dan Tsafrir,et al.  Backfilling Using System-Generated Predictions Rather than User Runtime Estimates , 2007, IEEE Transactions on Parallel and Distributed Systems.

[42]  David E. Culler,et al.  The ganglia distributed monitoring system: design, implementation, and experience , 2004, Parallel Comput..

[43]  Lee Gillam,et al.  Energy efficient computing, clusters, grids and clouds: A taxonomy and survey , 2017, Sustain. Comput. Informatics Syst..

[44]  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..

[45]  Angela H. Jiang,et al.  JamaisVu: Robust Scheduling with Auto-Estimated Job Runtimes , 2016 .

[46]  Rajiv Ranjan,et al.  Renewable Energy-Based Multi-Indexed Job Classification and Container Management Scheme for Sustainability of Cloud Data Centers , 2019, IEEE Transactions on Industrial Informatics.

[47]  Yang Hu,et al.  Concurrent container scheduling on heterogeneous clusters with multi-resource constraints , 2020, Future Gener. Comput. Syst..

[48]  César A. F. De Rose,et al.  Server consolidation with migration control for virtualized data centers , 2011, Future Gener. Comput. Syst..

[49]  Rajkumar Buyya,et al.  Holistic resource management for sustainable and reliable cloud computing: An innovative solution to global challenge , 2019, J. Syst. Softw..

[50]  Mohsen Guizani,et al.  Release-time aware VM placement , 2014, 2014 IEEE Globecom Workshops (GC Wkshps).

[51]  Miika Komu,et al.  Hypervisors vs. Lightweight Virtualization: A Performance Comparison , 2015, 2015 IEEE International Conference on Cloud Engineering.

[52]  Ramakrishnan Rajamony,et al.  An updated performance comparison of virtual machines and Linux containers , 2015, 2015 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS).

[53]  Helen D. Karatza,et al.  Combining containers and virtual machines to enhance isolation and extend functionality on cloud computing , 2019, Future Gener. Comput. Syst..

[54]  Dror G. Feitelson,et al.  Heuristics for Resource Matching in Intel's Compute Farm , 2013, JSSPP.

[55]  Dingwen Tao,et al.  Progress-based Container Scheduling for Short-lived Applications in a Kubernetes Cluster , 2019, 2019 IEEE International Conference on Big Data (Big Data).

[56]  Hai Jin,et al.  Heterogeneity and Interference-Aware Virtual Machine Provisioning for Predictable Performance in the Cloud , 2016, IEEE Transactions on Computers.

[57]  Konstantinos Vandikas,et al.  Bare-metal, virtual machines and containers in OpenStack , 2017, 2017 20th Conference on Innovations in Clouds, Internet and Networks (ICIN).

[58]  Syed Hassan Ahmed,et al.  KEIDS: Kubernetes-Based Energy and Interference Driven Scheduler for Industrial IoT in Edge-Cloud Ecosystem , 2020, IEEE Internet of Things Journal.

[59]  Muhammad Zakarya,et al.  Energy, performance and cost efficient datacenters: A survey , 2018, Renewable and Sustainable Energy Reviews.

[60]  Rahim Khan,et al.  Energy-aware dynamic resource management in elastic cloud datacenters , 2019, Simul. Model. Pract. Theory.

[61]  Atta ur Rehman Khan,et al.  An energy, performance efficient resource consolidation scheme for heterogeneous cloud datacenters , 2020, J. Netw. Comput. Appl..

[62]  Omer F. Rana,et al.  Modelling Performance & Resource Management in Kubernetes , 2016, 2016 IEEE/ACM 9th International Conference on Utility and Cloud Computing (UCC).

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

[64]  Noel De Palma,et al.  Software consolidation as an efficient energy and cost saving solution , 2016, Future Gener. Comput. Syst..

[65]  Rajkumar Buyya,et al.  An Energy and Performance Aware Consolidation Technique for Containerized Datacenters , 2019, IEEE Transactions on Cloud Computing.

[66]  Brian F. Goldiez,et al.  Combining virtualization and containerization to support interactive games and simulations on the cloud , 2019, Simul. Model. Pract. Theory.

[67]  Wei-Mei Chen,et al.  Self-adaptive resource allocation for energy-aware virtual machine placement in dynamic computing cloud , 2018, J. Netw. Comput. Appl..

[68]  MinSu Chae,et al.  A performance comparison of linux containers and virtual machines using Docker and KVM , 2017, Cluster Computing.