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A New Temporal Locality-based Workload Prediction Approach for SaaS Services in a Cloud Environment

Wiem Matoussi, Tarek Hamrouni
2021 Journal of King Saud University: Computer and Information Sciences  
Indeed, cloud providers must provide an optimal quality of service (QoS) for their users in order to survive in such a competitive cloud market.  ...  In this context, we propose a new approach to predict the number of requests arriving at a SaaS service in order to prepare the virtualized resources necessary to respond to user requests.  ...  The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.  ... 
doi:10.1016/j.jksuci.2021.04.008 fatcat:b7dnaqo5gfd53cvpo3ay4s5rdy

A Survey on Machine Learning for Geo-Distributed Cloud Data Center Management [article]

Ninad Hogade, Sudeep Pasricha
2022 arXiv   pre-print
The characterization, prediction, control, and optimization of complex, heterogeneous, and ever-changing distributed cloud resources and workloads employing ML methodologies have received much attention  ...  We examine the challenges and the issues in current research focused on ML for cloud management and explore strategies for addressing these issues.  ...  For simplification, we dissected this problem into three sub-categories: profiling, parameter prediction, and cloud optimization.  ... 
arXiv:2205.08072v1 fatcat:nz3vvmdrard6hgydagnsadrnpm

Challenges and Research Directions in Big Data-driven Cloud Adaptivity

Andreas Tsagkaropoulos, Nikos Papageorgiou, Dimitris Apostolou, Yiannis Verginadis, Gregoris Mentzas
2018 Proceedings of the 8th International Conference on Cloud Computing and Services Science  
We also describe research directions for realising adaptivity in cloud computing and we present a conceptual framework that represents research directions and shows interrelations.  ...  We examine new methods for developing cloud systems operating in a real-time, big data environment that can sense the context of the application environment and can adapt the infrastructure accordingly  ...  ACKNOWLEDGEMENTS This work is partly funded by the European Commission project H2020 PrestoCloud -Proactive Cloud Resources Management at the Edge for Efficient Real-Time Big Data Processing (732339).  ... 
doi:10.5220/0006761901900200 dblp:conf/closer/TsagkaropoulosP18 fatcat:5w2kbdda7fdajgn265jzqrfo4y

Challenges And Research Directions In Big Data-Driven Cloud Adaptivity

Andreas Tsagkaropoulos, Nikos Papageorgiou, Dimitris Apostolou, Yiannis Verginadis, Gregoris Mentzas
2018 Zenodo  
We also describe research directions for realising adaptivity in cloud computing and we present a conceptual framework that represents research directions and shows interrelations.  ...  We examine new methods for developing cloud systems operating in a real-time, big data environment that can sense the context of the application environment and can adapt the infrastructure accordingly  ...  ACKNOWLEDGEMENTS This work is partly funded by the European Commission project H2020 PrestoCloud -Proactive Cloud Resources Management at the Edge for Efficient Real-Time Big Data Processing (732339).  ... 
doi:10.5281/zenodo.1309530 fatcat:si3drvyewjfmbom5jnpevo3u7y

Cost-Effective Reconfiguration for Multi-Cloud Applications

Nikos Parlavantzas, Linh Manh Pham, Arnab Sinha, Christine Morin
2018 2018 26th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP)  
The paper describes experiments in a real cloud testbed that demonstrate that the approach enables multicloud adaptation while increasing the total value of the application for its owner.  ...  This paper proposes an approach for adaptive application deployment that explicitly considers adaptation costs and benefits in making deployment decisions.  ...  In the first category, the MODAClouds [2] European project proposed an approach for continuously adapting the deployment of multi-cloud applications.  ... 
doi:10.1109/pdp2018.2018.00088 dblp:conf/pdp/ParlavantzasPSM18 fatcat:zsdo6msqjvfyrcnldyfa5knj6u

Search-based Methods for Multi-Cloud Configuration [article]

Małgorzata Łazuka, Thomas Parnell, Andreea Anghel, Haralampos Pozidis
2022 arXiv   pre-print
In this work, we consider solutions to this optimization problem. We develop and evaluate possible adaptations of state-of-the-art cloud configuration solutions to the multi-cloud domain.  ...  Our experiments indicate that (a) many state-of-the-art cloud configuration solutions can be adapted to multi-cloud, with best results obtained for adaptations which utilize the hierarchical structure  ...  As future work, we are planning to perform a similar in-depth study, but for another category of workloads, namely ML inference applications.  ... 
arXiv:2204.09437v1 fatcat:dxim7sw6ajccvew5uj46a2ndxe

Efficient Resource Management in Cloud Environment [article]

Smruti Rekha Swain, Ashutosh Kumar Singh, Chung Nan Lee
2022 arXiv   pre-print
In cloud computing resource management plays a significant role in data centres and it is directly dependent on the application workload.  ...  Various services such as Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS) are offered by cloud computing to provide compute, network, and storage capabilities  ...  network connections instead of real-numbered weights.Evolutionary Neural NetworkA workload prediction model suggested by Kumar et al.[29]for cloud environment by applying neural network and self adaptive  ... 
arXiv:2207.12085v1 fatcat:ic27de5ggrbl5lczt6p6bqyqku

Machine Learning-based Orchestration of Containers: A Taxonomy and Future Directions [article]

Zhiheng Zhong, Minxian Xu, Maria Alejandra Rodriguez, Chengzhong Xu, Rajkumar Buyya
2021 arXiv   pre-print
Existing mainstream cloud service providers have prevalently adopted container technologies in their distributed system infrastructures for automated application management.  ...  Machine learning algorithms are accordingly employed by container orchestration systems for behavior modelling and prediction of multi-dimensional performance metrics.  ...  Xu and Buyya [30] survey brownout technologies for adaptive application maintenance in cloud computing systems. Duc et al.  ... 
arXiv:2106.12739v1 fatcat:bewvimekavduba4ku4stq32sny

Machine Learning-based Orchestration of Containers: A Taxonomy and Future Directions

Zhiheng Zhong, Minxian Xu, Maria Alejandra Rodriguez, Chengzhong Xu, Rajkumar Buyya
2022 ACM Computing Surveys  
Existing mainstream cloud service providers have prevalently adopted container technologies in their distributed system infrastructures for automated application management.  ...  Machine learning algorithms are accordingly employed by container orchestration systems for behavior modelling and prediction of multi-dimensional performance metrics.  ...  As diferent cloud service providers may difer in many aspects, such as resource conigurations, price, network latency, and geographic locations, this allows more choices for optimization of application  ... 
doi:10.1145/3510415 fatcat:ykmi7diulbesfcqqbqlvrn5pcq

Research on Auto-Scaling of Web Applications in Cloud: Survey, Trends and Future Directions

Parminder Singh, Pooja Gupta, Kiran Jyoti, Anand Nayyar
2019 Scalable Computing : Practice and Experience  
However, web applications usually have dynamic workload and hard to predict. Cloud service providers and researchers are working to reduce the cost while maintaining the Quality of Service (QoS).  ...  One of the key challenges for web application in cloud computing is auto-scaling.  ...  Time series analysis classified in two categories: direct prediction and identification of a pattern in time series.  ... 
doi:10.12694/scpe.v20i2.1537 fatcat:5zdylggvtjdslichn6mpoleese

Design Support for Performance Aware Dynamic Application (Re-)Distribution in the Cloud

Santiago Gomez Saez, Vasilios Andrikopoulos, Frank Leymann, Steve Strauch
2015 IEEE Transactions on Services Computing  
Database-as-a-service (DBaaS) or Platform-as-a-Service (PaaS), and with them the possibilities in deploying and operating an application partially or completely in the Cloud.  ...  The need for providing design support to application developers in this environment is the focus of this work.  ...  Furthermore, the analysis and generation of application performance models for application workloads in the Cloud can be used to ease capacity management operations and predict the workload behavior to  ... 
doi:10.1109/tsc.2014.2381237 fatcat:dotas7ilanf53pgdvex22cpo2i

Towards Energy-aware IaaS-PaaS Co-design
english

Alexandra Carpen-Amarie, Djawida Dib, Anne-Cécile Orgerie, Guillaume Pierre
2014 Proceedings of the 3rd International Conference on Smart Grids and Green IT Systems  
However, prior studies mostly consider resource-level energy optimizations in IaaS clouds, overlooking the workload-related information locked at higher levels, such as PaaS clouds.  ...  In this position paper, we argue that cross-layer cooperation in clouds is a key to achieving an optimized resource management, both performance and energy-wise.  ...  When facing longer-duration workload peaks, the IaaS cloud can automatically adapt the pool of available physical machines, by enabling nodes in advance.  ... 
doi:10.5220/0004961402030208 dblp:conf/smartgreens/Carpen-AmarieDOP14 fatcat:5bxqv2s7xnfzbiwhdzqbgzjwj4

Machine Learning Methods for Reliable Resource Provisioning in Edge-Cloud Computing

Thang Le Duc, Rafael García Leiva, Paolo Casari, Per-Olov Östberg
2019 ACM Computing Surveys  
The survey is structured around a decomposition of the reliable resource provisioning problem into three categories of techniques: workload characterization and prediction, component placement and system  ...  Reliable Resource Provisioning in Edge-Cloud Computing 1:3 studying the impact of Service Level Agreement (SLA) violations, dealing with different Quality of Service (QoS) parameters, and a need for evaluations  ...  Liu et al. address the problem by an adaptive workload prediction scheme and an autoscaling framework presented in [61] .  ... 
doi:10.1145/3341145 fatcat:vkzofhgipfhqtm2z2w5swivksy

Load Balancing Strategy for Hybrid Cloud-based Rendering Service

G. Vilutis, K. Sutiene, R. Kavaliunas, L. Daugirdas
2014 Elektronika ir Elektrotechnika  
This algorithm is adapted for a rendering service to ensure its Cloud-based delivery.  ...  The workload balancing algorithm for Hybrid Cloud is proposed, assuming that a certain part of incoming projects in the workload can be postponed for a short time period.  ...  for managing workload execution in Cloud computing.  ... 
doi:10.5755/j01.eee.20.2.2044 fatcat:sccak2gaercijblsv3asnvwfhy

Efficient Cloud Resource Scaling based on Prediction Approaches

K Dinesh Kumar, E Umamaheswari
2018 International Journal of Engineering & Technology  
Effective scaling mechanism gives an optimal solutions for computational problems while achieving QoS and avoiding SLA (Service Level Agreement) violations.  ...  To enhance resource scaling mechanism in cloud environment, predicting future workload to the each application in different manners like number of physical machines, number of virtual machines, number  ...  Apart from these services, delivering services in different categories, such as network, security, API and test environments etc.  ... 
doi:10.14419/ijet.v7i4.10.21029 fatcat:w7yptqwl5jeanlgg4nfef2hec4
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