Evaluating cloud database migration options using workload models

A key challenge in porting enterprise software systems to the cloud is the migration of their database. Choosing a cloud provider and service option (e.g., a database-as-a-service or a manually configured set of virtual machines) typically requires the estimation of the cost and migration duration for each considered option. Many organisations also require this information for budgeting and planning purposes. Existing cloud migration research focuses on the software components, and therefore does not address this need. We introduce a two-stage approach which accurately estimates the migration cost, migration duration and cloud running costs of relational databases. The first stage of our approach obtains workload and structure models of the database to be migrated from database logs and the database schema. The second stage performs a discrete-event simulation using these models to obtain the cost and duration estimates. We implemented software tools that automate both stages of our approach. An extensive evaluation compares the estimates from our approach against results from real-world cloud database migrations

Workload-Aware Performance Tuning for Autonomous DBMSs

Optimal configuration is vital for a DataBase Management System (DBMS) to achieve high performance. There is no one-size-fits-all configuration that works for different workloads since each workload has varying patterns with different resource requirements. There is a relationship between configuration, workload, and system performance. If a configuration cannot adapt to the dynamic changes of a workload, there could be a significant degradation in the overall performance of DBMS unless a sophisticated administrator is continuously re-configuring the DBMS. In this tutorial, we focus on autonomous workload-aware performance tuning, which is expected to automatically and continuously tune the configuration as the workload changes. We survey three research directions, including 1) workload classification, 2) workload forecasting, and 3) workload-based tuning. While the first two topics address the issue of obtaining accurate workload information, the third one tackles the problem of how to properly use the workload information to optimize performance. We also identify research challenges and open problems, and give real-world examples about leveraging workload information for database tuning in commercial products (e.g., Amazon Redshift). We will demonstrate workload-aware performance tuning in Amazon Redshift in the presentation.Peer reviewe

application cost-aware cloud provisioning

Οι πλατφόρμες νέφους επιτρέπουν στους ιδιοκτήτες εφαρμογών την ενοικίαση πόρων, προκειμένου να επεκτείνουν δυναμικά τη συνολική υπολογιστική ισχύ των υποδομών τους. Τα χαρακτηριστικά και οι τιμές των πόρων αυτών συνήθως ποικίλουν. Οι πάροχοι νέφους διασφαλίζουν την ποιότητα υπηρεσίας μέσω εγγυήσεων (Service Layer Agreements) και πληρώνουν ποινή όταν μια εγγύηση παραβιάζεται. Συνηθως, οι βασισμένες στο νέφος εφαρμογές να προσφέρουν και αυτές τέτοιες εγγυήσεις στους χρήστες. Σε ένα δυναμικό περιβάλλον, όπου ο χρήστης εκτελεί εφαρμογές στο ιδιωτικό νέφος και μπορούν να προσθαφαιρούν κόμβους από πάροχους (δημόσιου) νέφους 2 διαφορετικά είδη SLAs υπάρχουν (i) το SLA που προσφέρεται από την εφαρμογή στους τελικούς χρήστες και (ii) το SLA που προσφέρεται από τους παρόχους νέφους στην εφαρμογή. Έτσι, μια ποινή για παραβίαση SLA από την εφαρμογή στους τελικούς χρήστες μπορεί να είναι χαμηλότερη αν παραβιάζεται και το SLA του παρόχου δημοσίου νέφους. Αυτή η ιδιότητα καθιστά τον υπολογισμό του συνολικού κόστους λειτουργίας περίπλοκο αλλά επεκτείνει το χώρο αναζήτησης των επιλογών με το χαμηλότερο συνολικό κόστος. Σε αυτήν τη διπλωματική εργασία παρουσιάζουμε έναν αλγόριθμο παροχής πόρων NoSQL εφαρμογών, που στοχεύει στην ελαχιστοποίηση του συνολικού κόστους της εφαρμογής λαμβάνοντας υπόψη τις ιδιότητες ελαστικότητας της εφαρμογής αυτής σε ένα ετερογενές περιβάλλον και είναι βασισμένος σε ‘‘look-ahead’’ βελτιστοποίησηCloud computing platforms allow application owners to rent resources in order to expand dynamically the overall computational power of their infrastructure. The resources characteristics and lease prices usually vary. Cloud providers ensure the Quality of Service through Service Layer Agreements (SLAs) and pay a penalty when these agreements are violated. Usually, cloud-based applications also offer SLAs to the users. In a dynamic environment, where a user is running applications on her private cloud and add/remove nodes from (public) cloud providers, 2 types of SLAs exist (i) the SLA offered by the application to the end users and (ii) the SLA offered by the cloud providers to the application. Thus, a penalty for an SLA violation from the application to the end users might be lower if the SLA from the public cloud provider is also violated. This property makes the calculation of the total operational cost complex, but also expands the search space of choices with lower total cost. In this thesis we present an application-cost aware resource provisioning algorithm for NoSQL applications that aims to minimize the application total cost by taking into account the elasticity properties of that application in a heterogeneous environment and is based on look-ahead optimization

Interplaying Cassandra NoSQL Consistency and Performance: A Benchmarking Approach

This experience report analyses performance of the Cassandra NoSQL database and studies the fundamental trade-off between data consistency and delays in distributed data storages. The primary focus is on investigating the interplay between the Cassandra performance (response time) and its consistency settings. The paper reports the results of the read and write performance benchmarking for a replicated Cassandra cluster, deployed in the Amazon EC2 Cloud. We present quantitative results showing how different consistency settings affect the Cassandra performance under different workloads. One of our main findings is that it is possible to minimize Cassandra delays and still guarantee the strong data consistency by optimal coordination of consistency settings for both read and write requests. Our experiments show that (i) strong consistency costs up to 25% of performance and (ii) the best setting for strong consistency depends on the ratio of read and write operations. Finally, we generalize our experience by proposing a benchmarking-based methodology for run-time optimization of consistency settings to achieve the maximum Cassandra performance and still guarantee the strong data consistency under mixed workloads

A methodology for full-system power modeling in heterogeneous data centers

The need for energy-awareness in current data centers has encouraged the use of power modeling to estimate their power consumption. However, existing models present noticeable limitations, which make them application-dependent, platform-dependent, inaccurate, or computationally complex. In this paper, we propose a platform-and application-agnostic methodology for full-system power modeling in heterogeneous data centers that overcomes those limitations. It derives a single model per platform, which works with high accuracy for heterogeneous applications with different patterns of resource usage and energy consumption, by systematically selecting a minimum set of resource usage indicators and extracting complex relations among them that capture the impact on energy consumption of all the resources in the system. We demonstrate our methodology by generating power models for heterogeneous platforms with very different power consumption profiles. Our validation experiments with real Cloud applications show that such models provide high accuracy (around 5% of average estimation error).This work is supported by the Spanish Ministry of Economy and Competitiveness under contract TIN2015-65316-P, by the Gener- alitat de Catalunya under contract 2014-SGR-1051, and by the European Commission under FP7-SMARTCITIES-2013 contract 608679 (RenewIT) and FP7-ICT-2013-10 contracts 610874 (AS- CETiC) and 610456 (EuroServer).Peer ReviewedPostprint (author's final draft

Cloud Based IoT Architecture

The Internet of Things (IoT) and cloud computing have grown in popularity over the past decade as the internet becomes faster and more ubiquitous. Cloud platforms are well suited to handle IoT systems as they are accessible and resilient, and they provide a scalable solution to store and analyze large amounts of IoT data. IoT applications are complex software systems and software developers need to have a thorough understanding of the capabilities, limitations, architecture, and design patterns of cloud platforms and cloud-based IoT tools to build an efficient, maintainable, and customizable IoT application. As the IoT landscape is constantly changing, research into cloud-based IoT platforms is either lacking or out of date. The goal of this thesis is to describe the basic components and requirements for a cloud-based IoT platform, to provide useful insights and experiences in implementing a cloud-based IoT solution using Microsoft Azure, and to discuss some of the shortcomings when combining IoT with a cloud platform