Evagoras Makridis

Evagoras Makridis, Kyriakos Deliparaschos, Evangelia Kalyvianaki et al.

We present robust dynamic resource allocation mechanisms to allocate application resources meeting Service Level Objectives (SLOs) agreed between cloud providers and customers. In fact, two filter-based robust controllers, i.e. H-infinity filter and Maximum Correntropy Criterion Kalman filter (MCC-KF), are proposed. The controllers are self-adaptive, with process noise variances and covariances calculated using previous measurements within a time window. In the allocation process, a bounded client mean response time (mRT) is maintained. Both controllers are deployed and evaluated on an experimental testbed hosting the RUBiS (Rice University Bidding System) auction benchmark web site. The proposed controllers offer improved performance under abrupt workload changes, shown via rigorous comparison with current state-of-the-art. On our experimental setup, the Single-Input-Single-Output (SISO) controllers can operate on the same server where the resource allocation is performed; while Multi-Input-Multi-Output (MIMO) controllers are on a separate server where all the data are collected for decision making. SISO controllers take decisions not dependent to other system states (servers), albeit MIMO controllers are characterized by increased communication overhead and potential delays. While SISO controllers offer improved performance over MIMO ones, the latter enable a more informed decision making framework for resource allocation problem of multi-tier applications.

Evagoras Makridis, Themistoklis Charalambous

Sequential decision-making under uncertainty often involves multiple agents learning which actions (arms) yield the highest rewards through repeated interaction with a stochastic environment. This setting is commonly modeled by cooperative multi-agent multi-armed bandit problems, where agents explore and share information without centralized coordination. In many realistic systems, agents have heterogeneous capabilities that limit their access to subsets of arms and communicate over asymmetric networks represented by directed graphs. In this work, we study multi-agent multi-armed bandit problems with partial arm access, where agents explore and exploit only the arms available to them while exchanging information with neighbors. We propose a distributed consensus-based upper confidence bound (UCB) algorithm that accounts for both the arm accessibility structure and network asymmetry. Our approach employs a mass-preserving information mixing mechanism, ensuring that reward estimates remain unbiased across the network despite accessibility constraints and asymmetric information flow. Under standard stochastic assumptions, we establish logarithmic regret for every agent, with explicit dependence on network mixing properties and arm accessibility constraints. These results quantify how heterogeneous arm access and directed communication shape cooperative learning performance.