Over the recent years, deep learning is widely being used in a variety of different fields and applications. The constant growth of data used to train complex models, has opened research in the distributed learning. In this domain, two main architectures are used to train models in a distribution fashion, all-reduce and parameter server. Both support synchronous learning, while parameter server also supports asynchronous learning. These architectures are adopted by tech companies, which have developed multiple systems for this purpose. Among the most popular and widely used distributed deep learning systems are Google TensorFlow, Facebook PyTorch and Apache MXNet. In this paper, we quantify the performance gap between these systems and present a detailed analysis to discuss the parameters that affect their execution time. Overall, in synchronous learning setups, TensorFlow is slower compared to PyTorch by average 2.65X, while the latter lags MXNet by average 1.38X. Regarding asynchronous learning, MXNet is faster by average 3.22X in respect with TensorFlow.

While there has been a lot of effort in recent years in optimising Big Data systems like Apache Spark and Hadoop, the all-to-all transfer of data between a MapReduce computation step, i.e., the shuffle data mechanism between cluster nodes remains always a serious bottleneck. In this work, we present Cherry, an open-source distributed task-aware Caching sHuffle sErvice for seRveRless analYtics. Our thorough experiments on a cloud testbed using realistic and synthetic workloads showcase that Cherry can achieve an almost 23% to 39% reduction in completion of the reduce stage with small shuffle block sizes, a 10% reduction in execution time on real workloads, while it can efficiently handle Spark execution failures with a constant task time re-computation overhead compared to existing approaches.

Over the last years, deep learning has gained an increase in popularity in various domains introducing complex models to handle the data explosion. However, while such model architectures can support the enormous amount of data, a single computing node cannot train the model using the whole data set in a timely fashion. Thus, specialized distributed architectures have been proposed, most of which follow data parallelism schemes, as the widely used parameter server approach. In this setup, each worker contributes to the training process in an asynchronous manner. While asynchronous training does not suffer from synchronization overheads, it introduces the problem of stale gradients which might cause the model to diverge during the training process. In this paper, we examine different data assignment schemes to workers, which facilitate the asynchronous learning approach. Specifically, we propose two different algorithms to perform the data sharding. Our experimental evaluation indicated that when stratification is taken into account the validation results present up to 6X less variance compared to standard sharding creation. When further data exploration for hidden stratification is performed, validation metrics can be slightly optimized. This method also achieves to reduce the variance of training and validation metrics by up to 8X and 2X respectively.

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.

In an era where big data analytics has become a first-class requirement for both the industrial and the academic community, multiple engines are built to execute distributed domain-specific analytics. SQL-based big data analytics is a very popular but also challenging domain due to its complexity that requires multiple runtime query optimizations. Popular frameworks, such as Presto and SparkSQL, commonly retrieve data from multiple sources and process them locally using domain-specific optimizers. However, recent work indicates that no single engine offers the optimal all-in-one solution for all types of SQL queries. Taking this into account, we envision building an optimizer to facilitate faster distributed SQL analytics over multiple engines, which will perform operator-level optimization using Machine Learning techniques and will exploit the sophisticated data-driven local engine optimizations.

Over the past decade, a plethora of systems have emerged to support data analytics in various domains such as SQL and machine learning, among others. In each of the data analysis domains, there are now many different specialized systems that leverage domain-specific optimizations to efficiently execute their workloads. An alternative approach is to build a general-purpose data analytics system that uses a common execution engine and programming model to support workloads in different domains. In this work, we choose representative systems of each class (Spark, TensorFlow, Presto and Hive) and benchmark their performance on a wide variety of machine learning and SQL workloads. We perform an extensive comparative analysis on the strengths and limitations of each system and highlight major areas for improvement for all systems. We believe that the major insights gained from this study will be useful for developers to improve the performance of these systems.

Mobile Network Operators develop new technologies, as the 5G network, to handle the constantly increasing network traffic, while they put less effort on optimizing their operations. However, more energy efficient approaches are essential for cost reduction and compliance with energy footprint principles. Network usage and IoT data produced in the base stations can be used to develop such approaches. Considering the above, the BigOptiBase platform has been designed. Through this platform we will offer a big data analytics subsystem developed to provide elastic energy efficient solutions for the base stations using data analytics and machine learning technologies.

Nowadays, deep learning is a crucial part of a variety of big data applications. Both the vast amount of data and the high complexity of the state-of-the-art neural networks have led to perform the network training in a distributed manner accross clusters. Since synchronization overheads are usually fatal for the training's performance, asynchronous training is usually preferred in such cases. However, this training mode is sensitive to conflicting updates. Such updates most commonly occur when the workers train on a totally different part of the data. To reduce this phenomenon, in this paper, we propose the use of hashing schemes when distributing training data across workers.

We present the Elton tool, a publicly available cloud resource elasticity management system tailored to NoSQL databases. Elton is integrated in the Ganetimgr web platform, and offers an easy to use web interface, through which monitoring and horizontal scaling of NoSQL databases can be performed and what-if analysis queries are enabled. Elton uses Markov Decision Processes (MDPs) as the underlying modeling framework, and encapsulates state-of-the-art horizontal scaling policies that offer different trade-offs between performance and monetary deployment cost. Its main novelty is that it employs probabilistic model checking to allow for both efficient elasticity decisions and analysis of scaling actions and serves as a case study about the benefits of model checking in online decision making and analysis.

Social networking users have the ability to check into Points of Interest (POIs) and associate location with their posts or tweets, leading to the creation of Geo-Social Networks (GeoSNs). There are many systems that aim to efficiently store and analyze plain and socially enhanced spatio-temporal data. A proper evaluation of these systems should be done using real data from popular GeoSNs, such as Foursquare, Facebook, etc. However, privacy restrictions prohibit the access to such real data in a large scale. Therefore, evaluations are done using real or synthetic data sets that include either only spatio-textual data (e.g. tweets) or plain spatial data (e.g. GPS traces) that are not socially enhanced. In this paper, we present Spaten, an open-source configurable spatio-temporal and textual data set generator, that extracts GPS traces from realistic routes utilizing Google Maps API, combines them with real POIs and relevant user comments crawled from TripAdvisor and makes the data available for further analysis. The injection of social properties extracted by existing Twitter graphs to the generated data along with further parameterization leads to realistic GeoSN data sets. We create and publicly offer GB-size datasets with millions of check-ins and GPS traces. As a proof of concept, we loaded the generated data into a Big Data enabled NoSQL system, and we evaluated its scalability by performing queries typically found in social networking sites. We hope that Spaten can provide the research community with the ability to generate realistic GeoSN data in a large scale, so as to properly evaluate their work.

Modern large-scale computing deployments consist of complex applications running over machine clusters. An important issue in these is the offering of elasticity, i.e., the dynamic allocation of resources to applications to meet fluctuating workload demands. Threshold based approaches are typically employed, yet they are difficult to calibrate and optimize. Approaches based on reinforcement learning (RL) have been proposed, but they require a large number of states in order to model complex application behavior. Methods that adaptively partition the state space have been proposed, but their partitioning criteria and strategies are sub-optimal. In this work we present MDP_DT, a novel full-model based reinforcement learning algorithm for elastic resource management that employs adaptive state space partitioning. We propose two novel statistical criteria and three strategies and we experimentally prove that they correctly decide both where and when to partition, outperforming existing approaches. We experimentally evaluate MDP_DT in a real large scale cluster over variable not-encountered workloads and we show that it takes more informed decisions compared to static, model-free and threshold approaches, while requiring a minimal amount of training data. We experimentally show that this adaptation enabled MDP_DT to optimize the achieved profit while being 40% cheaper than calibrated RL and threshold approaches.

In this paper we present RASP, a system that combines latest distributed stream processing and NoSQL engines to enable the real-time low latency storage and joining of incoming data streams with external datasets of arbitrary sizes through an extensible, SQL compliant manner. We achieve low latency, real time execution by employing the Kafka and Storm frameworks to join incoming tuples as they arrive, while the denormalized result is being stored in HBase, a distributed NoSQL engine with the use of Phoenix, a framework that fully supports SQL. We fine-tune the topology execution to achieve maximum performance and we also apply a set of optimizations both in the HBase storage and the Phoenix SQL execution framework. We use RASP to solve a network analytics problem using real data. RASP performs its computations utilizing an extensible pipeline of Storm bolts that incrementally augment incoming tuples with the execution of different algorithms. We deploy our system over an IaaS cloud and we evaluate its performance for various workloads, cluster sizes and configurations, where we show that in some cases RASP achieves a throughput increase of more than 140% and a latency drop of more than 65% compared to a vanilla setting.

Modern large-scale computing deployments consist of complex applications running over machine clusters. An important issue there is the offering of elasticity, i.e., the dynamic allocation of resources to applications to meet fluctuating workload demands. Threshold based approaches are typically employed, yet they are difficult to configure and optimize. Approaches based on reinforcement learning have been proposed, but they require a large number of states in order to model complex application behavior. Methods that adaptively partition the state space have been proposed, but their partitioning criteria and strategies are sub-optimal. In this work we present MDP DT, a novel fullmodel based reinforcement learning algorithm for elastic resource management that employs adaptive state space partitioning. We propose two novel statistical criteria and three strategies and we experimentally prove that they correctly decide both where and when to partition, outperforming existing approaches. We experimentally evaluate MDP DT in a real large scale cluster over variable not-encountered workloads and we show that it takes more informed decisions compared to static and model-free approaches, while requiring a minimal amount of training data.

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.

The use of cloud computing has gained extreme popularity. Through cloud platforms that provide infrastructure as a service (IaaS), users can elastically provision resources enabling automated application throttling. Usually, scaling is either manually performed or through a service that dynamically consolidates cloud resources based on a predefined policy. However, these policies are simplistic, threshold based and may not be able to capture specific application behaviors according to configuration parameters and applied workload type. In this work, we extend TIRAMOLA, a cloud-enabled framework that allows automated resizing of NoSQL clusters, in order to identify different workload types and apply the most beneficial scaling action according to user defined policies. We perform a thorough analysis of how different query types are handled by modern NoSQL systems and evaluate the performance of a NoSQL cluster of varying size, over mixed workload types and magnitudes. We utilize this knowledge to fine tune the extended TIRAMOLA's policies in order to take accurate scaling decisions. We perform an extensive experimental evaluation of workload aware and unaware versions on an HBase cluster and our analysis confirms that the former can operate successfully in any environment, behaving accordingly to any input load.

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.

The proliferation of data in RDF format has resulted in the emergence of a plethora of specialized management systems. While the ability to adapt to the complexity of a SPARQL query -- given their inherent diversity -- is crucial, current approaches do not scale well when faced with substantially complex, non-selective joins, resulting in exponential growth of execution times. In this demonstration we present H2 RDF+, an RDF store that efficiently performs distributed Merge and Sort-Merge joins using a multiple-index scheme over HBase indexes. Through a greedy planner that incorporates our cost-model, it adaptively commands for either single or multi-machine query execution based on join complexity. In this paper, we present its key scientific contributions and allow participants to interact with an H2RDF+ deployment over a Cloud infrastructure. Using a web-based GUI we allow users to load different datasets (both real and synthetic), apply any query (custom or predefined) and monitor its execution. By allowing real-time inspection of cluster status, response times and committed resources the audience will evaluate the validity of H2RDF+'s claims and perform direct comparisons to two other state-of-the-art RDF stores.

The proliferation of data in RDF format calls for efficient and scalable solutions for their management. While scalability in the era of big data is a hard requirement, modern systems fail to adapt based on the complexity of the query. Current approaches do not scale well when faced with substantially complex, non-selective joins, resulting in exponential growth of execution times. In this work we present H2RDF+, an RDF store that efficiently performs distributed Merge and Sort-Merge joins over a multiple index scheme. H2RDF+ is highly scalable, utilizing distributed MapReduce processing and HBase indexes. Utilizing aggressive byte-level compression and result grouping over fast scans, it can process both complex and selective join queries in a highly efficient manner. Furthermore, it adaptively chooses for either single- or multi-machine execution based on join complexity estimated through index statistics. Our extensive evaluation demonstrates that H2RDF+ efficiently answers non-selective joins an order of magnitude faster than both current state-of-the-art distributed and centralized stores, while being only tenths of a second slower in simple queries, scaling linearly to the amount of available resources.

Recently, a large number of pay-as-you-go data services are offered over cloud infrastructures. Data service providers need appropriate and flexible query charging mechanisms and query optimization that take into consideration cloud operational expenses, pricing strategies and user preferences. Yet, existing solutions are static and non-configurable. We demonstrate COCCUS, a modular system for cost-aware query execution, adaptive query charge and optimization of cloud data services. The audience can set their queries along with their execution preferences and budget constraints, while COCCUS adaptively determines query charge and manages secondary data structures according to various economic policies. We demonstrate COCCUS's operation over centralized and shared nothing CloudDBMS architectures on top of public and private IaaS clouds. The audience is enabled to set economic policies and execute various workloads through a comprehensive GUI. COCCUS's adaptability is showcased using real-time graphs depicting a number of key performance metrics.

Unanticipated load spikes or skewed data access patterns may lead to severe performance degradation in data serving applications, a typical problem of distributed NoSQL data-stores. In these cases, load balancing is a necessary operation. In this demonstration, we present the DBalancer, a generic distributed module that can be installed on top of a typical NoSQL data-store and provide an efficient and highly configurable load balancing mechanism. Balancing is performed by simple message exchanges and typical data movement operations supported by most modern NoSQL data-stores. We present the system's architecture, we describe in detail its modules and their interaction and we implement a suite of different algorithms on top of it. Through a web-based interactive GUI we allow the users to launch NoSQL clusters of various sizes, to apply numerous skewed and dynamic workloads and to compare the implemented load balancing algorithms. Videos and graphs showcasing each algorithm's effect on a number of indicative performance and cost metrics will be created on the fly for every setup. By browsing the results of different executions users will be able to grasp each algorithm's balancing mechanisms and performance impact in a number of representative setups.

NoSQL databases focus on analytical processing of large scale datasets, offering increased scalability over commodity hardware. One of their strongest features is elasticity, which allows for fairly portioned premiums and high-quality performance. Yet, the process of adaptive expansion and contraction of resources usually involves a lot of manual effort, often requiring the definition of the conditions for scaling up or down to be provided by the users. To date, there exists no open-source system for automatic resizing of NoSQL clusters. In this demonstration, we present TIRAMOLA, a modular, cloud-enabled framework for monitoring and adaptively resizing NoSQL clusters. Our system incorporates a decision-making module which allows for optimal cluster resize actions in order to maximize any quantifiable reward function provided together with life-long adaptation to workload or infrastructural changes. The audience will be able to initiate HBase clusters of various sizes and apply varying workloads through multiple YCSB clients. The attendees will be able to watch, in real-time, the system perform automatic VM additions and removals as well as how cluster performance metrics change relative to the optimization parameters of their choice.

NoSQL databases focus on analytical processing of large scale datasets, offering increased scalability over commodity hardware. One of their strongest features is elasticity, which allows for fairly portioned premiums and high-quality performance and directly applies to the philosophy of a cloud-based platform. Yet, the process of adaptive expansion and contraction of resources usually involves a lot of manual effort during cluster configuration. To date, there exists no comparative study to quantify this cost and measure the efficacy of NoSQL engines that offer this feature over a cloud provider. In this work, we present a cloud-enabled framework for adaptive monitoring of NoSQL systems. We perform a study of the elasticity feature on some of the most popular NoSQL databases over an open-source cloud platform. Based on these measurements, we finally present a prototype implementation of a decision making system that enables automatic elastic operations of any NoSQL engine based on administrator or application-specified constraints.