Ioannis Giannakopoulos

CELAR: Automated application elasticity platform

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.

PANIC: Modeling Application Performance over Virtualized Resources

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.

Recovering from Cloud Application Deployment Failures Through Re-execution

In this paper we study the problem of automated cloud application deployment and configuration. Transient failures are commonly found in current cloud infrastructures, attributed to the complexity of the software and hardware stacks utilized. These errors affect cloud application deployment, forcing the users to manually check and intervene in the deployment process. To address this challenge, we propose a simple yet powerful deployment methodology with error recovery features that bases its functionality on identifying the script dependencies and re-executing the appropriate configuration scripts. To guarantee the idempotent script execution, we adopt a filesystem snapshot mechanism that enables our approach to revert to a healthy filesystem state in case of failed script executions. Our experimental analysis indicates that our approach can resolve any transient deployment failure appearing during the deployment phase, even in highly unpredictable cloud environments.

An Equitable Solution to the Stable Marriage Problem

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 mens and womens 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.

MoDisSENSE: A distributed platform for social networking services over mobile devices

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.

AURA: Recovering from Transient Failures in Cloud Deployments

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 AURAs ability to overcome transient failures, even in the most unstable environments.

Cloud application deployment with transient failure recovery

Application deployment is a crucial operation for modern cloud providers. The ability to dynamically allocate resources and deploy a new application instance based on a user-provided description in a fully automated manner is of great importance for the cloud users as it facilitates the generation of fully reproducible application environments with minimum effort. However, most modern deployment solutions do not consider the error-prone nature of the cloud: Network glitches, bad synchronization between different services and other software or infrastructure related failures with transient characteristics are frequently encountered. Even if these failures may be tolerable during an application’s lifetime, during the deployment phase they can cause severe errors and lead it to failure. In order to tackle this challenge, in this work we propose AURA, an open source system that enables cloud application deployment with transient failure recovery capabilities. AURA formulates the application deployment as a Directed Acyclic Graph. Whenever a transient failure occurs, it traverses the graph, identifies the parts of it that failed and re-executes the respective scripts, based on the fact that when the transient failure disappears the script execution will succeed. Moreover, in order to guarantee that each script execution is idempotent, AURA adopts a lightweight filesystem snapshot mechanism that aims at canceling the side effects of the failed scripts. Our thorough evaluation indicated that AURA is capable of deploying diverse real-world applications to environments exhibiting high error probabilities, introducing a minimal time overhead, proportional to the failure probability of the deployment scripts.

Exploiting Social Networking and Mobile Data for Crisis Detection and Management

Every day, vast amounts of social networking data is being produced and consumed at a constantly increasing rate. A user’s digital footprint coming from social networks or mobile devices, such as comments, check-ins and GPS traces contains valuable information about her behavior under normal as well as emergency conditions. The collection and analysis of mobile and social networking data before, during and after a disaster opens new perspectives in areas such as real-time event detection, crisis management and personalization and provides valuable insights about the extent of the disaster, its impact on the affected population and the rate of disaster recovery. Traditional storage and processing systems are unable to cope with the size of the collected data and the complexity of the applied analysis, thus distributed approaches are usually employed. In this work, we propose an open-source distributed platform that can serve as a backend for applications and services related to crisis detection and management by combining spatio-textual user generated data. The system focuses on scalability and relies on a combination of state-of-the art Big Data frameworks. It currently supports the most popular social networks, being easily extensible to any social platform. The experimental evaluation of our prototype attests its performance and scalability even under heavy load, using different query types over various cluster sizes.

Isolation in Docker through Layer Encryption

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 containers lifetime, bearing only a marginal performance overhead.

Fair, Fast and Frugal Large-Scale Matchmaking for VM Placement

VM placement, be it in public or private clouds, has a decisive impact on the provider’s interest and the customer’s needs alike, both of which may vary over time and circumstances. However, current resource management practices are either statically bound to specific policies or unilaterally favor the needs of Cloud operators. In this paper we argue for a flexible and democratic mechanism to map virtual to physical resources, trying to balance satisfaction on both sides of the involved stakeholders. To that end, VM placement is expressed as an Equitable Stable Matching Problem (ESMP), where each party’s policy is translated to a preference list. A practical approximation for this NP-hard problem, modified accordingly to ensure efficiency and scalability, is applied to provide equitable matchings within a reasonable time frame. Our experimental evaluation shows that, requiring no more memory than what a high-end desktop PC provides and knowing no more than the top 20% of the agent’s preference lists, our solution can efficiently resolve more than 90% of large-scale ESMP instances within \( N \sqrt{N} \) rounds of matchmaking.