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Service-based architectures provide substantial benefits, yet service orchestration remains a challenge, particularly for newcomers. While various resources on orchestration techniques exist, they often lack clarity and standardization,…
Optimizing resource allocation for analytical workloads is vital for reducing costs of cloud-data services. At the same time, it is incredibly hard for users to allocate resources per query in serverless processing systems, and they…
This paper addresses the poor finite-horizon performance of existing online \emph{restless bandit} (RB) algorithms, which stems from the prohibitive sample complexity of learning a full \emph{Markov decision process} (MDP) for each agent.…
To ensure uninterrupted services to the cloud clients from federated cloud providers, it is important to guarantee an efficient allocation of the cloud resources to users to improve the rate of client satisfaction and the quality of the…
Cloud computing environments demand dynamic and efficient resource management to ensure optimal performance, reduced energy consumption, and adherence to Service Level Agreements (SLAs). This paper presents a Genetic Algorithm (GA)-based…
In the past decade, we have witnessed a dramatically increasing volume of data collected from varied sources. The explosion of data has transformed the world as more information is available for collection and analysis than ever before. To…
In this paper, we address the contextual dueling bandit problem by proposing variance-aware algorithms that leverage neural networks to approximate nonlinear utility functions. Our approach employs a \textit{variance-aware exploration…
We propose the first contextual bandit algorithm that is parameter-free, efficient, and optimal in terms of dynamic regret. Specifically, our algorithm achieves dynamic regret $\mathcal{O}(\min\{\sqrt{ST},…
We consider the problem of multiple agents or robots searching for a target in the plane. This is motivated by Search and Rescue operations (SAR) in the high seas which in the past were often performed with several vessels, and more…
Dynamic resource allocation for machine learning workloads in cloud environments remains challenging due to competing objectives of minimizing training time and operational costs while meeting Service Level Agreement (SLA) constraints.…
Distributed applications employ Kubernetes for scalable, fault-tolerant deployments over computer clusters, where application components run in groups of containers called pods. The scheduler, at the heart of Kubernetes' architecture,…
We consider the contextual bandit problem where at each time, the agent only has access to a noisy version of the context and the error variance (or an estimator of this variance). This setting is motivated by a wide range of applications…
Edge computing environments increasingly rely on lightweight container orchestration platforms to manage resource-constrained devices. This paper provides an empirical analysis of five lightweight kubernetes distributions (KD)(k0s, k3s,…
This paper addresses the challenges of high resource dynamism and scheduling complexity in cloud-native database systems. It proposes an adaptive resource orchestration method based on multi-agent reinforcement learning. The method…
We study the problem of resource provisioning under stringent reliability or service level requirements, which arise in applications such as power distribution, emergency response, cloud server provisioning, and regulatory risk management.…
A robotic network is a system with multiple robots connected by a communication network. Certain tasks that cannot be accomplished with available robotic resources are candidates for the use of cloud robotics, which overcomes the…
Contextual bandits (CB) are online sequential decision-making problems under partial feedback that underpin many adaptive services. There is a growing demand to deploy CB agents directly on-device, under strict constraints on memory,…
The increasing demand for diverse, mobile applications with various degrees of Quality of Service requirements meets the increasing elasticity of on-demand resource provisioning in virtualized cloud computing infrastructures. This paper…
Modern applications increasingly span across cloud, fog, and edge environments, demanding orchestration systems that can adapt to diverse deployment contexts while meeting Quality-of-Service (QoS) requirements. Standard Kubernetes…
Today's cloud infrastructure landscape offers a broad range of services to build and operate software applications. The myriad of options, however, has also brought along a new layer of complexity. When it comes to procuring cloud computing…