arXiv subjects

Vana Kalogeraki

Publications and source records attributed to Vana Kalogeraki.

11 recordsLinked to original sources

arXiv2026

FCx: An algorithm for finding Feasible Counterfactual Explanations

Counterfactual (CF) explanations identify changes that alter an input's classification. While existing methods produce realistic and low-cost CFs, they often fail to ensure feasibility, by suggesting non-constructive modifications or incompatible with future changes (e.g., changing an individual's race to secure a job offer). We introduce a refinement of CF explanations that explicitly enforces feasibility. Our approach is the first to efficiently generate CFs that are realistic, low-cost and feasible. We accommodate both hard feasible constraints, specified by domain knowledge users, and soft feasible constraints, inferred automatically via causal inference from the dataset. Our method, Feasible Counterfactual Explanations (FCx), is based on a modified Variational Autoencoder (VAE) optimized with a multi-factor loss function. We measure the cost of a change based on the absolute change in values (proximity) as well as the number of features changed (sparsity) while realism is measured based on the LOF for density estimation, guaranteeing that CFs reside in densely populated regions. Extensive experiments on four public datasets show that our approach matches state-of-the-art performance across multiple metrics while guaranteeing feasibility.

cs.LG↗

arXiv2026

Trustworthy Scheduling for Big Data Applications

Recent advances in modern containerized execution environments have resulted in substantial benefits in terms of elasticity and more efficient utilization of computing resources. Although existing schedulers strive to optimize performance metrics like task execution times and resource utilization, they provide limited transparency into their decision-making processes or the specific actions developers must take to meet Service Level Objectives (SLOs). In this work, we propose X-Sched, a middleware that uses explainability techniques to generate actionable guidance on resource configurations that makes task execution in containerized environments feasible, under resource and time constraints. X-Sched addresses this gap by integrating counterfactual explanations with advanced machine learning models, such as Random Forests, to efficiently identify optimal configurations. This approach not only ensures that tasks are executed in line with performance goals but also gives users clear, actionable insights into the rationale behind scheduling decisions. Our experimental results validated with data from real-world execution environments, illustrate the efficiency, benefits and practicality of our approach.

cs.DC↗

arXiv2024

Energy Efficient Scheduling for Serverless Systems

Serverless computing, also referred to as Function-as-a-Service (FaaS), is a cloud computing model that has attracted significant attention and has been widely adopted in recent years. The serverless computing model offers an intuitive, event-based interface that makes the development and deployment of scalable cloud-based applications easier and cost-effective. An important aspect that has not been examined in these systems is their energy consumption during the application execution. One way to deal with this issue is to schedule the function invocations in an energy-efficient way. However, efficient scheduling of applications in a multi-tenant environment, like FaaS systems, poses significant challenges. The trade-off between the server's energy usage and the hosted functions' performance requirements needs to be taken into consideration. In this work, we propose an Energy Efficient Scheduler for orchestrating the execution of serverless functions so that it minimizes energy consumption while it satisfies the applications' performance demands. Our approach considers real-time performance measurements and historical data and applies a novel DVFS technique to minimize energy consumption. Our detailed experimental evaluation using realistic workloads on our local cluster illustrates the working and benefits of our approach.

cs.DC↗

arXiv2024

Orchestrating the Execution of Serverless Functions in Hybrid Clouds

In recent years, serverless computing, especially Function as a Service (FaaS), is rapidly growing in popularity as a cloud programming model. The serverless computing model provides an intuitive interface for developing cloud-based applications, where the development and deployment of scalable microservices has become easier and cost-effective. An increasing number of batch-processing applications are deployed as pipelines that comprise a sequence of functions that must meet their deadline targets to be practical. In this paper, we present our Hybrid Cloud Scheduler (HCS) for orchestrating the execution of serverless batch-processing pipelines deployed over heterogeneous infrastructures. Our framework enables developers to (i) automatically schedule and execute batch-processing applications in heterogeneous environments such as the private edge and public cloud serverless infrastructures, (ii) benefit from cost reduction through the utilization of their own resources in a private cluster, and (iii) significantly improves the probability of meeting the deadline requirements of their applications. Our experimental evaluation demonstrates the efficiency and benefits of our approach.

cs.DC↗

arXiv2024

Prediction-driven resource provisioning for serverless container runtimes

In recent years Serverless Computing has emerged as a compelling cloud based model for the development of a wide range of data-intensive applications. However, rapid container provisioning introduces non-trivial challenges for FaaS cloud providers, as (i) real-world FaaS workloads may exhibit highly dynamic request patterns, (ii) applications have service-level objectives (SLOs) that must be met, and (iii) container provisioning can be a costly process. In this paper, we present SLOPE, a prediction framework for serverless FaaS platforms to address the aforementioned challenges. Specifically, it trains a neural network model that utilizes knowledge from past runs in order to estimate the number of instances required to satisfy the invocation rate requirements of the serverless applications. In cases that a priori knowledge is not available, SLOPE makes predictions using a graph edit distance approach to capture the similarities among serverless applications. Our experimental results illustrate the efficiency and benefits of our approach, which can reduce the operating costs by 66.25% on average.

cs.DC↗

arXiv2024

TIMBER: On supporting data pipelines in Mobile Cloud Environments

The radical advances in mobile computing, the IoT technological evolution along with cyberphysical components (e.g., sensors, actuators, control centers) have led to the development of smart city applications that generate raw or pre-processed data, enabling workflows involving the city to better sense the urban environment and support citizens' everyday lives. Recently, a new era of Mobile Edge Cloud (MEC) infrastructures has emerged to support smart city applications that aim to address the challenges raised due to the spatio-temporal dynamics of the urban crowd as well as bring scalability and on-demand computing capacity to urban system applications for timely response. In these, resource capabilities are distributed at the edge of the network and in close proximity to end-users, making it possible to perform computation and data processing at the network edge. However, there are important challenges related to real-time execution, not only due to the highly dynamic and transient crowd, the bursty and highly unpredictable amount of requests but also due to the resource constraints imposed by the Mobile Edge Cloud environment. In this paper, we present TIMBER, our framework for efficiently supporting mobile daTa processing pIpelines in MoBile cloud EnviRonments that effectively addresses the aforementioned challenges. Our detailed experimental results illustrate that our approach can reduce the operating costs by 66.245% on average and achieve up to 96.4% similar throughput performance for agnostic workloads.

cs.DC↗

arXiv2024

A Framework for Feasible Counterfactual Exploration incorporating Causality, Sparsity and Density

The imminent need to interpret the output of a Machine Learning model with counterfactual (CF) explanations - via small perturbations to the input - has been notable in the research community. Although the variety of CF examples is important, the aspect of them being feasible at the same time, does not necessarily apply in their entirety. This work uses different benchmark datasets to examine through the preservation of the logical causal relations of their attributes, whether CF examples can be generated after a small amount of changes to the original input, be feasible and actually useful to the end-user in a real-world case. To achieve this, we used a black box model as a classifier, to distinguish the desired from the input class and a Variational Autoencoder (VAE) to generate feasible CF examples. As an extension, we also extracted two-dimensional manifolds (one for each dataset) that located the majority of the feasible examples, a representation that adequately distinguished them from infeasible ones. For our experimentation we used three commonly used datasets and we managed to generate feasible and at the same time sparse, CF examples that satisfy all possible predefined causal constraints, by confirming their importance with the attributes in a dataset.

cs.LG↗

arXiv2023

Practical Privacy Preservation in a Mobile Cloud Environment

The proliferation of smartphone devices has led to the emergence of powerful user services from enabling interactions with friends and business associates to mapping, finding nearby businesses and alerting users in real-time. Moreover, users do not realize that continuously sharing their trajectory data with online systems may end up revealing a great amount of information in terms of their behavior, mobility patterns and social relationships. Thus, addressing these privacy risks is a fundamental challenge. In this work, we present $TP^3$, a Privacy Protection system for Trajectory analytics. Our contributions are the following: (1) we model a new type of attack, namely 'social link exploitation attack', (2) we utilize the coresets theory, a fast and accurate technique which approximates well the original data using a small data set, and running queries on the coreset produces similar results to the original data, and (3) we employ the Serverless computing paradigm to accommodate a set of privacy operations for achieving high system performance with minimized provisioning costs, while preserving the users' privacy. We have developed these techniques in our $TP^3$ system that works with state-of-the-art trajectory analytics apps and applies different types of privacy operations. Our detailed experimental evaluation illustrates that our approach is both efficient and practical.

cs.CR↗

arXiv2021

Low-Rank Methods in Event Detection and Subsampled Point-to-Subspace Proximity Tests

Monitoring of streamed data to detect abnormal behaviour (variously known as event detection, anomaly detection, change detection, or outlier detection) underlies many applications of the Internet of Things. There, one often collects data from a variety of sources, with asynchronous sampling, and missing data. In this setting, one can predict abnormal behavior using low-rank techniques. In particular, we assume that normal observations come from a low-rank subspace, prior to being corrupted by a uniformly distributed noise. Correspondingly, we aim to recover a representation of the subspace, and perform event detection by running point-to-subspace distance query for incoming data. In particular, we use a variant of low-rank factorisation, which considers interval uncertainty sets around "known entries", on a suitable flattening of the input data to obtain a low-rank model. On-line, we compute the distance of incoming data to the low-rank normal subspace and update the subspace to keep it consistent with the seasonal changes present. For the distance computation, we suggest to consider subsampling. We bound the one-sided error as a function of the number of coordinates employed using techniques from learning theory and computational geometry. In our experimental evaluation, we have tested the ability of the proposed algorithm to identify samples of abnormal behavior in induction-loop data from Dublin, Ireland.

cs.DS↗

arXiv2018

Attendance Maximization for Successful Social Event Planning

Social event planning has received a great deal of attention in recent years where various entities, such as event planners and marketing companies, organizations, venues, or users in Event-based Social Networks, organize numerous social events (e.g., festivals, conferences, promotion parties). Recent studies show that "attendance" is the most common metric used to capture the success of social events, since the number of attendees has great impact on the event's expected gains (e.g., revenue, artist/brand publicity). In this work, we study the Social Event Scheduling (SES) problem which aims at identifying and assigning social events to appropriate time slots, so that the number of events attendees is maximized. We show that, even in highly restricted instances, the SES problem is NP-hard to be approximated over a factor. To solve the SES problem, we design three efficient and scalable algorithms. These algorithms exploit several novel schemes that we design. We conduct extensive experiments using several real and synthetic datasets, and demonstrate that the proposed algorithms perform on average half the computations compared to the existing solution and, in several cases, are 3-5 times faster.

cs.DS↗

arXiv2018

Social Event Scheduling

A major challenge for social event organizers (e.g., event planning and marketing companies, venues) is attracting the maximum number of participants, since it has great impact on the success of the event, and, consequently, the expected gains (e.g., revenue, artist/brand publicity). In this paper, we introduce the Social Event Scheduling (SES) problem, which schedules a set of social events considering user preferences and behavior, events' spatiotemporal conflicts, and competing vents, in order to maximize the overall number of attendees. We show that SES is strongly NP-hard, even in highly restricted instances. To cope with the hardness of the SES problem we design a greedy approximation algorithm. Finally, we evaluate our method experimentally using a dataset from the Meetup event-based social network.

cs.DS↗