A bi-objective cost model for optimizing database queries in a multi-cloud environment

AbstractCost models are broadly used in query processing to drive the query optimization process, accurately predict the query execution time, schedule database query tasks, apply admission control and derive resource requirements to name a few applications. The main role of cost models is to estimate the time needed to run the query on a specific machine. In a multi-cloud environment, cost models should be easily calibrated for a wide range of different physical machines, and time estimates need to be complemented with monetary cost information, since both the economic cost and the performance are of primary importance. This work aims to serve as the first proposal for a bi-objective query cost model suitable for queries executed over resources provided by potentially multiple cloud providers. We leverage existing calibrating modeling techniques for time estimates and we couple such estimates with monetary cost information covering the main charging options for using cloud resources. Moreover, we explain how the cost model can become part of an optimizer. Our approach is applicable to more generic data flow graphs, the execution plans of which do not necessarily comprise relational operators. Finally, we give a concrete example about the usage of our proposal and we validate its accuracy through real case studies

Elasticity management of cloud noSQL databases

Cloud computing has arisen as one of the most attractive alternatives for providing computational infrastructures for high-demand applications. One of the main advantages of cloud computing is that it renders the procurement of expensive computing resources unnecessary, thus lifting the burden of high upfront investments in proprietary platforms from system developers and owners. This characteristic is complemented by the capacity for on-demand resource provisioning based on the actual current requirements; this feature is commonly referred to as elasticity, and it can be manifested in different forms referring to the number, the size or the location of virtual machines (VMs) employed. Examples of these three elasticity types are the increase of the number of VMs (horizontal scaling), the allocation of more memory to a VM (vertical scaling) and moving a VM to a less loaded physical machine (migration), respectively. This PHD thesis exclusively focuses on automated elasticity approaches, and especially targets elasticity in the form of horizontally scaling the number of application VMs, where a probabilistic model checking approach is used to verify system properties and solve models, that represent the elastic behavior of the system. The model checking results allow the analysis of the elastic behavior of the system and combined with decision making policies handle the elasticity of the occupied resources for a NoSQL database application. Experimental results show the efficiency of the proposed approach, achieving dependable and configurable trade-offs between conflicting objectives, like: (i) maintaining the performance of the system, avoiding over-provisioning, (ii) maintaining the performance, reassuring the integrity and security of the stored data and (iii) maintaining the performance, minimizing the operation cost of the NoSQL application.Οι υποδομές νέφους έχουν συγκεντρώσει το ενδιαφέρον τόσο της ερευνητικής όσο και της επιχειρηματικής κοινότητας, καθώς προσφέρουν μοναδικά χαρακτηριστικά, τα οποία αν αξιοποιηθούν με αποδοτικό τρόπο μπορούν να οδηγήσουν στη βέλτιστη επίτευξη στόχων όπως η εγγύηση της απόδοσης, η διασφάλιση της ακεραιότητας των δεδομένων, η μείωση του λειτουργικού κόστους, κ.α.. Ένα δυνατό χαρακτηριστικό των υποδομών νέφους είναι η δυνατότητα ελαστικής διαχείρισης των διαθέσιμων πόρων (ελαστικότητα). Αυτό το χαρακτηριστικό αναφέρεται είτε στην οριζόντια κλιμάκωση (προσθήκη/αφαίρεση Εικονικών Μηχανών (ΕΜ)), είτε στην κάθετη κλιμάκωση (π.χ. προσθήκη αφαίρεση κύριας μνήμης RAM), είτε στη μετανάστευση των ΕΜ σε διαφορετικά φυσικά μηχανήματα. Η παρούσα διατριβή προτείνει μία καινοτόμο μέθοδο διαχείρισης της οριζόντιας κλιμάκωσης που βασίζεται στον πιθανοκρατικό έλεγχο μοντέλων, επαληθεύοντας ιδιότητες συστήματος, για την επίλυση μοντέλων που αναπαριστούν την ελαστική συμπεριφορά του συστήματος. Τα αποτελέσματα της επίλυσης επιτρέπουν την ανάλυση της δυναμικής συμπεριφοράς του συστήματος και συνδυάζονται με πολιτικές λήψης αποφάσεων ελαστικότητας πόρων, για τη βέλτιστη αυτοματοποιημένη διαχείριση της οριζόντιας κλιμάκωσης των ΕΜ, που εξυπηρετούν μη-σχεσιακές βάσεις δεδομένων. Μέσω της πειραματικής αξιολόγησης αναδεικνύεται η αποδοτικότητα της προτεινόμενης μεθοδολογίας, επιτυγχάνοντας αξιόπιστες και ρυθμιζόμενες ισορροπίες μεταξύ αντικρουόμενων στόχων, όπως (i) η απόδοση του συστήματος, αποφεύγοντας την υπέρ-δέσμευση πόρων, (ii) η απόδοση του συστήματος και η διασφάλιση της ακεραιότητας των αποθηκευμένων δεδομένων και (iii) η διατήρηση της απόδοσης του συστήματος, ελαχιστοποιώντας το λειτουργικό κόστος της υποδομής

Binary Theta-Joins using MapReduce: Efficiency Analysis and Improvements

We deal with binary theta-joins in a MapReduce environment, and we make two contributions. First, we show that the best known algorithm to date for this problem can reach the optimal trade-o ↵ between the size of the input a reducer can receive and the incurred communication cost when the join selectivity is high. Second, when the join selectivity is low, we present improvements upon the state-of-the-art with a view to decreasing the communication cost and the maximum load a reducer can receive, taking also into account the load imbalance across the reducers. 1

Probabilistic model checking of perturbed MDPs with applications to cloud computing

2017 Association for Computing Machinery. Probabilistic model checking is a formal verification technique that has been applied successfully in a variety of domains, providing identification of system errors through quantitative verification of stochastic system models. One domain that can benefit from probabilistic model checking is cloud computing, which must provide highly reliable and secure computational and storage services to large numbers of mission-critical software systems. For real-world domains like cloud computing, external system factors and environmental changes must be estimated accurately in the form of probabilities in system models; inaccurate estimates for the model probabilities can lead to invalid verification results. To address the effects of uncertainty in probability estimates, in previous work we have developed a variety of techniques for perturbation analysis of discrete- and continuous-time Markov chains (DTMCs and CTMCs). These techniques determine the consequences of the uncertainty on verification of system properties. In this paper, we present the first approach for perturbation analysis of Markov decision processes (MDPs), a stochastic formalism that is especially popular due to the significant expressive power it provides through the combination of both probabilistic and nondeterministic choice. Our primary contribution is a novel technique for efficiently analyzing the effects of perturbations of model probabilities on verification of reachability properties of MDPs. The technique heuristically explores the space of adversaries of an MDP, which encode the different ways of resolving the MDP\u27s nondeterministic choices.We demonstrate the practical effectiveness of our approach by applying it to two case studies of cloud systems