Given a two-sided market where each agent ranks those on the other side by preference, the stable marriage problem calls for finding a perfect matching such that no pair of agents prefer each other to their matches. Recent studies show that the number of stable solutions can be large in practice. Yet the classical solution to the problem, the Gale-Shapley (GS) algorithm, assigns an optimal match to each agent on one side, and a pessimal one to each on the other side; such a solution may fare well in terms of equity only in highly asymmetric markets. Finding a stable matching that minimizes the sex equality cost, an equity measure expressing the discrepancy of mean happiness among the two sides, is strongly NP-hard. Extant heuristics either (a) oblige some agents to involuntarily abandon their matches, or (b) bias the outcome in favor of some agents, or (c) need high-polynomial or unbounded time.We provide the first procedurally fair algorithms that output equitable stable marriages and are guaranteed to terminate in at most cubic time; the key to this breakthrough is the monitoring of a monotonic state function and the use of a selective criterion for accepting proposals. Our experiments with diverse simulated markets show that: (a) extant heuristics fail to yield high equity; (b) the best solution found by the GS algorithm can be very far from optimal equity; and (c) our procedures stand out in both efficiency and equity, even when compared to a non-procedurally fair approximation scheme.

In this paper we present the SELIS Big Data Analytics and Machine Learning System (BDA), an open-source cloud-enabled elastic system that has been designed and implemented in order to address data related issues from the logistics domain. By taking into consideration real-life data analytics needs from more than 40 EU logistics providers we present the detailed SELIS BDA architecture along with the generic data and execution model devised to accommodate their diverse needs. We describe the main technologies we have utilized to realize the respective offering and justify our choices from the wider open-source Big Data systems community. We experimentally test our offering under various workloads where we prove that it can scale to serve a large number of concurrent requests while its abstraction/orchestration poses a very small overhead compared to the stand-alone Big Data systems. We believe that the SELIS BDA can be an easy-to-use entry point for the big data analytics world for any logistics company especially from the SME domain.

Can a stable matching that achieves high equity among the two sides of a market be reached in quadratic time? The Deferred Acceptance (DA) algorithm finds a stable matching that is biased in favor of one side; optimizing apt equity measures is strongly NP-hard. A proposed approximation algorithm offers a guarantee only with respect to the DA solutions. Recent work introduced Deferred Acceptance with Compensation Chains (DACC), a class of algorithms that can reach any stable matching in O(n^4) time, but did not propose a way to achieve good equity. In this paper, we propose an alternative that is computationally simpler and achieves high equity too. We introduce Monotonic Deferred Acceptance (MDA), a class of algorithms that progresses monotonically towards a stable matching; we couple MDA with a mechanism we call Strongly Deferred Acceptance (SDA), to build an algorithm that reaches an equitable stable matching in quadratic time; we amend this algorithm with a few low-cost local search steps to what we call Deferred Local Search (DLS), and demonstrate experimentally that it outperforms previous solutions in terms of equity measures and matches the most efficient ones in runtime.

The rapidly increasing amount of available data has created invaluable business opportunities but also new challenges. The focus on content-driven analytics is shifting attention from optimizing operators and systems to handle massive data sizes, to intelligent selection of those datasets that maximize the business competitive advantage. To date, there exists no efficient method to quantify the impact of numerous available datasets over different analytics tasks - a thorough execution over every input would be prohibitively expensive. In this demonstration, we present Apollo, a data profiling and operator modeling system that tackles this challenge. Our system quantifies dataset similarities and projects them into a low-dimensional space. Operator outputs are then estimated over the entire dataset, utilizing similarity information with Machine Learning and a small sample of actual executions. During the demo, attendees will be able to model and visualize multiple analytics operators over datasets from the domains of machine learning and graph analytics.

The plethora of publicly available data sources has given birth to a wealth of new needs and opportunities. The ever increasing amount of data has shifted the analysts' attention from optimizing the operators for specific business cases, to focusing on datasets per se, selecting the ones that are most suitable for specific operators, i.e., they make an operator produce a specific output. Yet, predicting the output of a given operator executed for different input datasets is not an easy task: It entails executing the operator for all of them, something that requires excessive computational power and time. To tackle this challenge, we propose a novel dataset profiling methodology that infers an operator's outcome based on examining the similarity of the available input datasets in specific attributes. Our methodology quantifies dataset similarities and projects them into a low-dimensional space. The operator is then executed for a mere subset of the available datasets and its output for the rest of them is approximated using Neural Networks trained using this space as input. Our experimental evaluation thoroughly examines the performance of our scheme using both synthetic and real-world datasets, indicating that the suggested approach is capable of predicting an operator's output with high accuracy. Moreover, it massively accelerates operator profiling in comparison to approaches that require an exhaustive operator execution, rendering our work ideal for cases where a multitude of operators need to be executed to a set of given datasets.

The advent of the Cloud computing era along with the wide adoption of the distributed paradigm has enabled applications to increase their performance standards and greatly extend their scalability limits. Nevertheless, the ability of modern applications to be deployed in numerous different ways has complicated their structure and enormously increased the difficulty of extracting accurate performance models for them. This capability is crucial for many operations, such as the identification of the most appropriate execution setups for an anticipated workload, finding bottlenecks, etc. In this work, we propose a fully automated performance modeling methodology that aims at the creation of highly accurate performance models for a given maximum number of deployments. The main idea of the proposed methodology lies on the "smart" exploration of the application configuration space, the selection and deployment of a set of representative application configurations and the construction of the performance model through the adoption of Machine Learning techniques. Moreover, taking into consideration the often unstable and error-prone nature of the cloud, the proposed system attempts to overcome transient cloud failures that occur during the application deployment phase through the re-execution of the parts that failed. Our evaluation, conducted for a set of real-world applications frequently deployed over cloud infrastructures, indicates that our system is capable of both constructing performance models of high accuracy and doing so in an efficient manner, fixing application deployments that present errors without requiring human intervention.

The popularity of containers is constantly rising in the virtualization landscape, since they incur significantly less overhead than Virtual Machines, the traditional hypervisor-based counterparts, while enjoying better performance. However, containers pose significant security challenges due to their direct communication with the host kernel, allowing attackers to break into the host system and co-located containers more easily than Virtual Machines. Existing security hardening mechanisms are based on the enforcement of Mandatory Access Control rules, which exclusively allow specified, desired operations. However, these mechanisms entail explicit knowledge of the container functionality and behavior and require manual intervention and setup. To overcome these limitations, we present Docker-sec, a user-friendly mechanism for the protection of Docker containers throughout their lifetime via the enforcement of access policies that correspond to the anticipated (and legitimate) activity of the applications they enclose. Docker-sec employs two mechanisms: (a) Upon container creation, it constructs an initial, static set of access rules based on container configuration parameters; (b) During container runtime, the initial set is enhanced with additional rules that further restrict the container's capabilities, reflecting the actual application operations. Through a rich interaction with our system the audience will experience firsthand how Docker-sec can successfully protect containers from zero-day vulnerabilities in an automatic manner, with minimal overhead on the application performance.

The advent of the Big Data era has given birth to a variety of new architectures aiming at applications with increased scalability, robustness and fault tolerance. At the same time these architectures have complicated application structure, leading to an exponential growth of their configuration space and increased difficulty in predicting their performance. In this work, we describe a novel, automated profiling methodology that makes no assumptions on application structure. Our approach utilizes oblique Decision Trees in order to recursively partition an application's configuration space in disjoint regions, choose a set of representative samples from each subregion according to a defined policy and return a model for the entire space as a composition of linear models over each subregion. An extensive evaluation over real-life applications and synthetic performance functions showcases that our scheme outperforms other state-of-the-art profiling methodologies. It particularly excels at reflecting abnormalities and discontinuities of the performance function, as well as identifying the parameters with the highest impact on the application's behavior.

In this work, we propose AURA, a cloud deployment tool used to deploy applications over providers that tend to present transient failures. The complexity of modern cloud environments imparts an error-prone behavior during the deployment phase of an application, something that hinders automation and magnifies costs both in terms of time and money. To overcome this challenge, we propose AURA, a framework that formulates an application deployment as a Directed Acyclic Graph traversal and re-executes the parts of the graph that failed. AURA achieves to execute any deployment script that updates filesystem related resources in an idempotent manner through the adoption of a layered filesystem technique. In our demonstration, we allow users to describe, deploy and monitor applications through a comprehensive UI and showcase AURA's ability to overcome transient failures, even in the most unstable environments.

Containers are constantly gaining ground in the virtualization landscape as a lightweight and efficient alternative to hypervisor-based Virtual Machines, with Docker being the most successful representative. Docker relies on union-capable file systems, where any action performed to a base image is captured as a new file system layer. This strategy allows developers to easily pack applications into Docker image layers and distribute them via public registries. However, this image creation and distribution strategy does not protect sensitive data from malicious privileged users (e.g., registry administrator, cloud provider), since encryption is not natively supported. We propose and demonstrate a mechanism for secure Docker image manipulation throughout its life cycle: The creation, storage and usage of a Docker image is backed by a data-at-rest mechanism, which maintains sensitive data encrypted on disk and encrypts/decrypts them on-the-fly in order to preserve their confidentiality at all times, while the distribution and migration of images is enhanced with a mechanism that encrypts only specific layers of the file system that need to remain confidential and ensures that only legitimate key holders can decrypt them and reconstruct the original image. Through a rich interaction with our system the audience will experience first-hand how sensitive image data can be safely distributed and remain encrypted at the storage device throughout the container's lifetime, bearing only a marginal performance overhead.

In this work we address the problem of predicting the performance of a complex application deployed over virtualized resources. Cloud computing has enabled numerous companies to develop and deploy their applications over cloud infrastructures for a wealth of reasons including (but not limited to) decrease costs, avoid administrative effort, rapidly allocate new resources, etc. Virtualization however, adds an extra layer in the software stack, hardening the prediction of the relation between the resources and the application performance, which is a key factor for every industry. To address this challenge we propose PANIC, a system which obtains knowledge for the application by actually deploying it over a cloud infrastructure and then, approximating the performance of the application for the all possible deployment configurations. The user of PANIC defines a set of resources along with their respective ranges and then the system samples the deployment space formed by all the combinations of the resources, deploys the application in some representative points and utilizes a wealth of approximation techniques to predict the behavior of the application in the remainder space. The experimental evaluation has indicated that a small portion of the possible deployment configurations is enough to create profiles with high accuracy for three real world applications.

A stable marriage problem (SMP) of size n involves n men and n women, each of whom has ordered members of the opposite gender by descending preferability. A solution is a perfect matching among men and women, such that there exists no pair who prefer each other to their current spouses. The problem was formulated in 1962 by Gale and Shapley, who showed that any instance can be solved in polynomial time, and has attracted interest due to its application to any two-sided market. Still, the solution obtained by the Gale-Shapley algorithm is favorable to one side. Gusfield and Irving introduced the equitable stable marriage problem (ESMP), which calls for finding a stable matching that minimizes the distance between men's and women's sum-of-rankings of their spouses. Unfortunately, ESMP is strongly NP-hard, approximation algorithms therefor are impractical, while even proposed heuristics may run for an unpredictable number of iterations. We propose a novel, deterministic approach that treats both genders equally, while eschewing an exhaustive exploration of the space of all stable matchings. Our thorough experimental study shows that, in contrast to previous proposals, our method not only achieves high-quality solutions, but also terminates efficiently and repeatably on all tested large problem instances.

The amount of social networking data that is being produced and consumed daily is huge and it is constantly increasing. A user's digital footprint coming from social networks or mobile devices, such as comments and check-ins contains valuable information about his preferences. The collection and analysis of such footprints using also information about the users' friends and their footprints offers many opportunities in areas such as personalized search, recommendations, etc. When the size of the collected data or the complexity of the applied methods increases, traditional storage and processing systems are not enough and distributed approaches are employed. In this work, we present MoDisSENSE, an open-source distributed platform that provides personalized search for points of interest and trending events based on the user's social graph by combining spatio-textual user generated data. The system is designed with scalability in mind, it is built using a combination of latest state-of-the art big data frameworks and its functionality is offered through easy to use mobile and web clients which support the most popular social networks. We give an overview of its architectural components and technologies and we evaluate its performance and scalability using different query types over various cluster sizes. Using the web or mobile clients, users are allowed to register themselves with their own social network credentials, perform socially enhanced queries for POIs, browse the results and explore the automatic blog creation functionality that is extracted by analyzing already collected GPS traces.

One of the main promises of the cloud computing paradigm is the ability to scale resources on-demand. This feature characterizes the cloud era, where the overhead of early expenditure for infrastructure is eliminated. Innovative services are thus able to enter the market quicker and adopt faster to new challenges and user demand. One of the main aspects of this on-demand nature is the concept of elasticity, i.e., the ability of autonomously provision and de-provision resources by reacting to changes in the incoming load. An elastic service is able to operate with an optimal cost by expanding and contracting its used resources at runtime and according to demand. This does not only minimizes running cost, but also avoids disruptive outages due to spikes in service usage. While the various layers comprising a cloud service can be scaled, this does not happen in a unified manner. The vision of CELAR is to provide a fully integrated software stack that manages resource allocation for cloud applications in an autonomous, efficient and generic manner. In order to achieve that, CELAR incorporates novel methodologies for describing cloud applications, monitoring the use of various resources, evaluating cost, taking informed decisions and interacting with the underlying cloud infrastructure. Our goal is two-fold. On the one hand is developing the methodologies for achieving multi-grained, automatic elasticity control on both application and infrastructure level. On the other hand is developing the open-source tools that implement those methods in an integrated manner. Hereby we present an overview of the CELAR platform, explaining its architectural components and some basic workflows that show how they interact in order to achieve the core functionalities.

In this work we present MoDisSENSE, a distributed analytics platform for social networking services over mobile devices. MoDisSENSE collects and stores various types of data from heterogeneous sources, such as GPS traces from cell phones, user profile information and comments from social networks connected to the platform. These are combined through spatio-temporal and textual analysis, performed in a distributed fashion, in order to extract knowledge, make smart suggestions and leverage user experience. The datastore follows a hybrid approach to handle both raw and processed data, simultaneously covering the need for scalability and fast query processing. Thus, the platform is able to resolve complex, multi-parameter, socially charged queries over Points of Interest in the order of milliseconds even under heavy load.