Microservices architecture, known for its agility and efficiency, is an ideal framework for cloud-based soft-ware development and deployment, particularly when inte-grated with containerization and orchestration systems that streamline resource management. However, the cost of cloud computing remains a critical concern for many organizations, prompting the need for effective strategies to minimize expenses without compromising performance. While cloud platforms like AWS offer transient pricing options, such as Spot Pricing, to reduce operational costs, the unpredictable demand and abrupt termination of spot VMs introduce considerable challenges. By leveraging containerization alongside intelligent orchestration, it is possible to optimize micro services deploy-ment costs while maintaining performance requirements. We present SpotKube, an open-source, Kubernetes-based so-lution that employs a genetic algorithm for cost optimization. Designed to dynamically scale clusters for micro service applications on public clouds using spot pricing, SpotKube analyzes application characteristics to recommend optimal resource al-locations, ensuring deployments remain cost -effective without sacrificing performance. Its elastic cluster autoscaler adapts to changing demands, gracefully managing node terminations to minimize disruptions in system availability. Evaluations conducted using real-world public cloud setups demonstrate SpotKube's superior performance and cost efficiency compared to alternative optimization strategies. GitHub: https://github.com/SvotKube/SvotKube

Recent advancements in big data processing and machine learning, among others, increase the resource demand for running applications with larger problem sizes. Elastic cloud computing resources with pay-per-use pricing offers new opportunities where large application execution is constrained only by the cost budget. Given a cost budget and a time deadline, this paper introduces a measurement-driven analytical modeling approach to determine the largest Pareto-optimal problem size and its corresponding cloud configuration for execution. We evaluate our approach with a set of representative applications that exhibit a range of resource demand growth patterns on Amazon AWS cloud. We show the existence of cost-time-size Pareto-frontier with multiple sweet spots meeting user constraints. To characterize the cost-performance of cloud resources, we use Performance Cost Ratio (PCR) metric. We extend Gustafson's fixed-time scaling in the context of cloud, and, investigate fixed-cost-time scaling of applications and show that using resources with higher PCR yields better cost-time performance. We discuss a number of useful insights on the trade-off between the execution time and the largest Pareto-optimal problem size, and, show that time deadline could be tightened for a proportionately much smaller reduction of problem size.

Clouds offer great flexibility for scaling applications due to the wide spectrum of resources with different cost-performance, inherent resource elasticity and pay-peruse charging. However, determining cost-time-efficient cloud configurations to execute a given application in the large resource configuration space remains a key challenge. The growing importance of elastic applications for which the accuracy is a function of resource consumption introduces new opportunities to exploit resource elasticity on clouds. In this paper, we introduce CELIA, a measurement-driven analytical modeling approach to determine cost-time-optimal cloud resource configurations to execute a given elastic application with a time deadline and a cost budget. We evaluate CELIA with three representative elastic applications on more than ten million configurations consisting of Amazon EC2 resource types with different cost-performance. Using CELIA, we show that multiple cost-time Pareto-optimal configurations exist among feasible cloud configurations that execute an elastic application within a time deadline and cost budget. These Pareto-optimal configurations exhibit up to 30% cost savings for an elastic application representing n-body simulation. We investigate the impact of fixed-time scaling on the cost of executing elastic applications on cloud. We show that cost gradient with respect to resource demand is smaller when cloud resources with better cost-performance are used. Furthermore, we show that the relative increase in cost is always smaller compared to the relative reduction of execution time deadline. For example, tightening the execution time deadline by two-thirds incurs only 40% increase in cost for the n-body simulation application.

False sharing is a major class of performance bugs in parallel applications. Detecting false sharing is difficult as it does not change the program semantics. We introduce an efficient and effective approach for detecting false sharing based on machine learning.