Related papers: SLA-Centric Automated Algorithm Selection Framewor…
We present a machine learning framework and a new test bed for data mining from the Slurm Workload Manager for high-performance computing (HPC) clusters. The focus was to find a method for selecting features to support decisions: helping…
Several scientific and industry applications require High Performance Computing (HPC) resources to process and/or simulate complex models. Not long ago, companies, research institutes, and universities used to acquire and maintain…
Efficient load balancing is crucial in cloud computing environments to ensure optimal resource utilization, minimize response times, and prevent server overload. Traditional load balancing algorithms, such as round-robin or least…
A self-learning adaptive system (SLAS) uses machine learning to enable and enhance its adaptability. Such systems are expected to perform well in dynamic situations. For learning high-performance adaptation policy, some assumptions must be…
In this paper, we study the peak-aware energy scheduling problem using the competitive framework with machine learning prediction. With the uncertainty of energy demand as the fundamental challenge, the goal is to schedule the energy output…
The rapid expansion of AI inference services in the cloud necessitates a robust scalability solution to manage dynamic workloads and maintain high performance. This study proposes a comprehensive scalability optimization framework for cloud…
Serverless computing has emerged as a compelling new paradigm of cloud computing models in recent years. It promises the user services at large scale and low cost while eliminating the need for infrastructure management. On cloud provider…
Reinforcement Learning (RL) has demonstrated a great potential for automatically solving decision-making problems in complex uncertain environments. RL proposes a computational approach that allows learning through interaction in an…
In this article we consider the Conditional Super Learner (CSL), an algorithm which selects the best model candidate from a library conditional on the covariates. The CSL expands the idea of using cross-validation to select the best model…
This paper presents a novel application of Genetic Algorithms(GAs) to quantify the performance of Platform as a Service (PaaS), a cloud service model that plays a critical role in both industry and academia. While Cloud benchmarks are not…
Automated Algorithm Selection (AAS) is a popular meta-algorithmic approach and has demonstrated to work well for single-objective optimisation in combination with exploratory landscape features (ELA), i.e., (numerical) descriptive features…
This paper investigates a critical resource allocation problem in the first party cloud: scheduling containers to machines. There are tens of services and each service runs a set of homogeneous containers with dynamic resource usage;…
Cloud Computing emerges from the global economic crisis as an option to use computing resources from a more rational point of view. In other words, a cheaper way to have IT resources. However, issues as security and privacy, SLA (Service…
This paper addresses the challenges of rapid resource variation and highly uncertain task loads in cloud computing environments. It proposes an optimization method for elastic cloud resource scaling based on a multi-agent system. The method…
Cloud-native applications have significantly advanced the development and scalability of online services through the use of microservices and modular architectures. However, achieving adaptability, resilience, and efficient performance…
In this paper, we examine the problem of a single provider offering multiple types of service level agreements, and the implications thereof. In doing so, we propose a simple model for machine-readable service level agreements (SLAs) and…
Modern software systems are often highly configurable to tailor varied requirements from diverse stakeholders. Understanding the mapping between configurations and the desired performance attributes plays a fundamental role in advancing the…
As the cloud infrastructure grows, it becomes more challenging to manage resources in such a massive, diverse, and distributed setting, despite the fact that cloud computing provides computational capabilities on-demand. Due to resource…
A fundamental goal in the design of IaaS service is to enable both user-friendly and cost-effective service access, while attaining high resource efficiency for revenue maximization. QoS differentiation is an important lens to achieve this…
With the development of networking technology, the computing system has evolved towards the multi-tier paradigm gradually. However, challenges, such as multi-resource heterogeneity of devices, resource competition of services, and networked…