Cloud Cost Optimization: A Comprehensive Review of Strategies and Case Studies

Cloud computing has revolutionized the way organizations manage their IT infrastructure, but it has also introduced new challenges, such as managing cloud costs. This paper explores various techniques for cloud cost optimization, including cloud pricing, analysis, and strategies for resource allocation. Real-world case studies of these techniques are presented, along with a discussion of their effectiveness and key takeaways. The analysis conducted in this paper reveals that organizations can achieve significant cost savings by adopting cloud cost optimization techniques. Additionally, future research directions are proposed to advance the state of the art in this important field

SPA : Harnessing Availability in the AWS Spot Market

Amazon Web Services (AWS) offers transient virtual servers at a discounted price as a way to sell unused spare capacity in its data centers. Although transient servers are very appealing as some instances have up to 90% discount, they are not bound to regular availability guarantees as they are opportunistic resources sold on the spot market. In this paper, we present SPA, a framework that remarkably increases the spot instance reliability over time due to insights gained from the analysis of historical data, such as cross-region price variability and intervals between evictions. We implemented the SPA reliability strategy, evaluated them using over one year of historical pricing data from AWS, and found out that we can increase the transient instance lifetime by adding a pricing overhead of 3.5% in the spot price in the best scenario.Peer reviewe

Container deployment strategy for edge networking

Conference code: 156753 Cited By :2 Export Date: 1 February 2021 References: AlertManager, , https://prometheus.io/docs/alerting/alertmanager/, Accessed: 2019-01-30; Docker Swarm Mode Overview, , https://docs.docker.com/engine/swarm/, Accessed: 2019-01-30; Google cAdvisor, , https://github.com/google/cadvisor, Accessed: 2019-01-30; Prometheus - Monitoring System & Time Series Database, , https://prometheus.io, Accessed: 2019-01-30; The Kubernetes Scheduler, , https://kubernetes.io/docs/reference/command-line-tools-reference/kube-scheduler/, Accessed: 2019-01-30; (2018) Ericsson Mobility Report, , https://www.ericsson.com/assets/local/mobility-report/documents/2018/ericsson-mobilityreport-june-2018.pdf, Technical Report; Balan, R., Flinn, J., Satyanarayanan, M., Sinnamohideen, S., Yang, H.-I., The case for cyber foraging (2002) Proceedings of the 10th Workshop on ACM SIGOPS European Workshop (EW 10), pp. 87-92. , https://doi.org/10.1145/1133373.1133390, ACM, New York, NY, USA; Gordon, M.S., Anoushe Jamshidi, D., Mahlke, S., Mao, Z.M., Chen, X., CoMET: Code offload by migrating execution transparently (2012) Proceedings of the 10th USENIX Conference on Operating Systems Design and Implementation (OSDI’12), pp. 93-106. , http://dl.acm.org/citation.cfm?id=2387880.2387890, USENIX Association, Berkeley, CA, USA; Habak, K., Ammar, M., Harras, K.A., Zegura, E., Femto clouds: Leveraging mobile devices to provide cloud service at the edge (2015) 2015 IEEE 8th International Conference on Cloud Computing, pp. 9-16. , https://doi.org/10.1109/CLOUD.2015.12; Hindman, B., Konwinski, A., Zaharia, M., Ghodsi, A., Joseph, A.D., Katz, R., Shenker, S., Stoica, I., Mesos: A Platform for Fine-grained Resource Sharing in the Data Center (2011) Proceedings of the 8th USENIX Conference on Networked Systems Design and Implementation (NSDI’11), pp. 295-308. , http://dl.acm.org/citation.cfm?id=1972457.1972488, USENIX Association, Berkeley, CA, USA; Pahl, C., Lee, B., Containers and clusters for edge cloud architectures – A technology review (2015) 2015 3rd International Conference on Future Internet of Things and Cloud, pp. 379-386. , https://doi.org/10.1109/FiCloud.2015.35; Roughan, M., Simplifying the synthesis of internet traffic matrices (2005) SIGCOMM Comput. Commun. Rev., 35 (5), pp. 93-96. , https://doi.org/10.1145/1096536.1096551, Oct. 2005; Satyanarayanan, M., Bahl, P., Caceres, R., Davies, N., The case for VM-based cloudlets in mobile computing (2009) IEEE Pervasive Computing, 8 (4), pp. 14-23. , https://doi.org/10.1109/MPRV.2009.82, Oct 2009; Saurez, E., Hong, K., Lillethun, D., Ramachandran, U., Ottenwälder, B., Incremental Deployment and Migration of Geo-distributed Situation Awareness Applications in the Fog (2016) Proceedings of the 10th ACM International Conference on Distributed and Event-Based Systems (DEBS’16), pp. 258-269. , https://doi.org/10.1145/2933267.2933317, ACM, New York, NY, USA; Shi, W., Cao, J., Zhang, Q., Li, Y., Xu, L., Edge computing: Vision and challenges (2016) IEEE Internet of Things Journal, 3 (5), pp. 637-646. , https://doi.org/10.1109/JIOT.2016.2579198, Oct 2016; Wu, C.-P., Suresh, M.A., Silva, D.D., Container lifecycle management for edge nodes: Poster (2017) Proceedings of the Second ACM/IEEE Symposium on Edge Computing (SEC’17), p. 2; Yi, S., Hao, Z., Qin, Z., Li, Q., Fog computing: Platform and applications (2015) 2015 Third IEEE Workshop on Hot Topics in Web Systems and Technologies (HotWeb), pp. 73-78Edge computing paradigm has been proposed to support latency-sensitive applications such as Augmented Reality (AR)/ Virtual Reality(VR) and online gaming, by placing computing resources close to where they are most demanded, at the edge of the network. Many solutions have proposed to deploy virtual resources as close as possible to the consumers using virtual machines and containers. However, the most popular container orchestration tools, e.g., Docker Swarm and Kubernetes, do not take into account the locality aspect during deployment, resulting in poor location choices at the edge of the network. In this paper, we propose an edge deployment strategy to tackle the lack of locality awareness of the container orchestrator. In this strategy, the orchestrator collects information about latency and the real-time resource consumption from the current container deployments, providing a bird’s-eye view of the most demanded locations and the best places for deployment to cover the largest number of clients. We evaluated the proposed model using 16 AWS regions across the globe and compared to the standard deployment strategies. The experimental results show our edge strategy reduces the average latency between serving container to the clients by up to 4 times compared to the standard deployment algorithms. © 2019 Association for Computing Machinery.Peer reviewe

An infrastructure service recommendation system for cloud applications with real-time QoS requirement constraints

The proliferation of cloud computing has revolutionized the hosting and delivery of Internet-based application services. However, with the constant launch of new cloud services and capabilities almost every month by both big (e.g., Amazon Web Service and Microsoft Azure) and small companies (e.g., Rackspace and Ninefold), decision makers (e.g., application developers and chief information officers) are likely to be overwhelmed by choices available. The decision-making problem is further complicated due to heterogeneous service configurations and application provisioning QoS constraints. To address this hard challenge, in our previous work, we developed a semiautomated, extensible, and ontology-based approach to infrastructure service discovery and selection only based on design-time constraints (e.g., the renting cost, the data center location, the service feature, etc.). In this paper, we extend our approach to include the real-time (run-time) QoS (the end-to-end message latency and the end-to-end message throughput) in the decision-making process. The hosting of next-generation applications in the domain of online interactive gaming, large-scale sensor analytics, and real-time mobile applications on cloud services necessitates the optimization of such real-time QoS constraints for meeting service-level agreements. To this end, we present a real-time QoS-aware multicriteria decision-making technique that builds over the well-known analytic hierarchy process method. The proposed technique is applicable to selecting Infrastructure as a Service (IaaS) cloud offers, and it allows users to define multiple design-time and real-time QoS constraints or requirements. These requirements are then matched against our knowledge base to compute the possible best fit combinations of cloud services at the IaaS layer. We conducted extensive experiments to prove the feasibility of our approach

Reducing the price of resource provisioning using EC2 spot instances with prediction models

The increasing demand of computing resources has boosted the use of cloud computing providers. This has raised a new dimension in which the connections between resource usage and costs have to be considered from an organizational perspective. As a part of its EC2 service, Amazon introduced spot instances (SI) as a cheap public infrastructure, but at the price of not ensuring reliability of the service. On the Amazon SI model, hired instances can be abruptly terminated by the service provider when necessary. The interface for managing SI is based on a bidding strategy that depends on non-public Amazon pricing strategies, which makes complicated for users to apply any scheduling or resource provisioning strategy based on such (cheaper) resources. Although it is believed that the use of the EC2 SIs infrastructure can reduce costs for final users, a deep review of literature concludes that their characteristics and possibilities have not yet been deeply explored. In this work we present a framework for the analysis of the EC2 SIs infrastructure that uses the price history of such resources in order to classify the SI availability zones and then generate price prediction models adapted to each class. The proposed models are validated through a formal experimentation process. As a result, these models are applied to generate resource provisioning plans that get the optimal price when using the SI infrastructure in a real scenario. Finally, the recent changes that Amazon has introduced in the SI model and how this work can adapt to these changes is discussed

Analyzing Spark Performance on Spot Instances

Amazon Spot Instances provide inexpensive service for high-performance computing. With spot instances, it is possible to get at most 90% off as discount in costs by bidding spare Amazon Elastic Computer Cloud (Amazon EC2) instances. In exchange for low cost, spot instances bring the reduced reliability onto the computing environment, because this kind of instance could be revoked abruptly by the providers due to supply and demand, and higher-priority customers are first served. To achieve high performance on instances with compromised reliability, Spark is applied to run jobs. In this thesis, a wide set of spark experiments are conducted to study its performance on spot instances. Without stateful replicating, Spark suffers from cascad- ing rollback and is forced to regenerate these states for ad hoc practices repeatedly. Such downside leads to discussion on trade-off between compatible slow checkpointing and regenerating on rollback and inspires us to apply multiple fault tolerance schemes. And Spark is proven to finish a job only with proper revocation rate. To validate and evaluate our work, prototype and simulator are designed and implemented. And based on real history price records, we studied how various checkpoint write frequencies and bid level affect performance. In case study, experiments show that our presented techniques can lead to ~20% shorter completion time and ~25% lower costs than those cases without such techniques. And compared with running jobs on full-price instance, the absolute saving in costs can be ~70%