Cloud Database Services Research Articles

Tao: Improving Resource Utilization while Guaranteeing SLO in Multi-tenant Relational Database-as-a-Service

It is an open challenge for cloud database service providers to guarantee tenants' service-level objectives (SLOs) and enjoy high resource utilization simultaneously. In this work, we propose a novel system Tao to overcome it. Tao consists of three key components: (i) tasklet-based DAG generator, (ii) tasklet-based DAG executor, and (iii) SLO-guaranteed scheduler. The core concept in Tao is tasklet, a coroutine-based lightweight execution unit of the physical execution plan. In particular, we first convert each SQL operator in the traditional physical execution plan into a set of fine-grained tasklets by the tasklet-based DAG generator. Then, we abstract the tasklet-based DAG execution procedure and implement the tasklet-based DAG executor using C++20 coroutines. Finally, we introduce the SLO-guaranteed scheduler for scheduling tenants' tasklets across CPU cores. This scheduler guarantees tenants' SLOs with a token bucket model and improves resource utilization with an on-demand core adjustment strategy. We build Tao on an open-sourced relational database, Hyrise, and conduct extensive experimental studies to demonstrate its superiority over existing solutions.

A Flexible Forecasting Stack

Forecasting extrapolates the values of a time series into the future, and is crucial to optimize core operations for many businesses and organizations. Building machine learning (ML)-based forecasting applications presents a challenge though, due to non-stationary data and large numbers of time series. As there is no single dominating approach to forecasting, forecasting systems have to support a wide variety of approaches, ranging from deep learning-based methods to classical methods built on probabilistic modelling. We revisit our earlier work on a monolithic platform for forecasting from VLDB 2017, and describe how we evolved it into a modern forecasting stack consisting of several layers that support a wide range of forecasting needs and automate common tasks like model selection. This stack leverages our open source forecasting libraries GluonTS and AutoGluon-TimeSeries , the scalable ML platform SageMaker , and forms the basis of the no-code forecasting solutions ( SageMaker Canvas and Amazon Forecast ), available in the Amazon Web Services cloud. We give insights into the predictive performance of our stack and discuss learnings from using it to provision resources for the cloud database services DynamoDB, Redshift and Athena.

Flexible Resource Allocation for Relational Database-as-a-Service

Oversubscription is an essential cost management strategy for cloud database providers, and its importance is magnified by the emerging paradigm of serverless databases. In contrast to general purpose techniques used for oversubscription in hypervisors, operating systems and cluster managers, we develop techniques that leverage our understanding of how DBMSs use resources and how resource allocations impact database performance. Our techniques are designed to flexibly redistribute resources across database tenants at the node and cluster levels with low overhead. We have implemented our techniques in a commercial cloud database service: Azure SQL Database. Experiments using microbenchmarks, industry-standard benchmarks and real-world resource usage traces show that using our approach, it is possible to tightly control the impact on database performance even with a relatively high degree of oversubscription.

Eigen: End-to-End Resource Optimization for Large-Scale Databases on the Cloud

Increasingly, cloud database vendors host large-scale geographically distributed clusters to provide cloud database services. When managing the clusters, we observe that it is challenging to simultaneously maximizing the resource allocation ratio and resource availability. This problem becomes more severe in modern cloud database clusters, where resource allocations occur more frequently and on a greater scale. To improve the resource allocation ratio without hurting resource availability, we introduce Eigen, a large-scale cloud-native cluster management system for large-scale databases on the cloud. Based on a resource flow model, we propose a hierarchical resource management system and three resource optimization algorithms that enable end-to-end resource optimization. Furthermore, we demonstrate the system optimization that promotes user experience by reducing scheduling latencies and improving scheduling throughput. Eigen has been launched in a large-scale public-cloud production environment for 30+ months and served more than 30+ regions (100+ available zones) globally. Based on the evaluation of real-world clusters and simulated experiments, Eigen can improve the allocation ratio by over 27% (from 60% to 87.0%) on average, while the ratio of delayed resource provisions is under 0.1%.

Augmented reality participatory platform: A novel digital participatory planning tool to engage under-resourced communities in improving neighborhood walkability

Engaging communities and utilizing local knowledge can alleviate the disparities faced by under-resourced neighborhoods with regard to providing sufficient opportunities for walkability. Digital technologies, and in particular smartphones, have created new opportunities to improve community engagement by expanding the inclusion of diverse stakeholders in decision-making. This paper introduces a novel digital participatory planning tool called the Augmented Reality Participatory Platform (ARPP). The platform uses mobile augmented reality (mobile AR) to engage residents, particularly in under-resourced communities, in identifying the design improvements necessary to enhance neighborhood walkability. ARPP consists of a mobile AR app, cloud database, and real-time mapping service. It provides a platform for two-way communication between residents of these communities and decision-makers, with potential benefits for both groups. ARPP enables residents to interact with street features and walkable design interventions in situ, generate walking-related spatial data, and share them and possible design solutions with decision-makers. The platform also assists decision-makers with presenting walkable street design alternatives developed based on community feedback to residents. To determine if ARPP is useful and user-friendly, experts and a group of residents from an under-resourced community evaluated the usability of the mobile AR app. We found that the app largely met the usability goals, and after we addressed must-solve issues identified by the experts, community participants faced very few issues when interacting with it. With recent technological advances in mobile AR and its potential for planning and design, integrating mobile AR into participatory planning is timely.

Efficient Bi-objective SQL Optimization for Enclaved Cloud Databases with Differentially Private Padding

Hardware-enabled enclaves have been applied to efficiently enforce data security and privacy protection in cloud database services. Such enclaved systems, however, are reported to suffer from I/O-size (also referred to as communication-volume)-based side-channel attacks. Albeit differentially private padding has been exploited to defend against these attacks as a principle method, it introduces a challenging bi-objective parametric query optimization (BPQO) problem and current solutions are still not satisfactory. Concretely, the goal in BPQO is to find a Pareto-optimal plan that makes a tradeoff between query performance and privacy loss; existing solutions are subjected to poor computational efficiency and high cloud resource waste. In this article, we propose a two-phase optimization algorithm called TPOA to solve the BPQO problem. TPOA incorporates two novel ideas: divide-and-conquer to separately handle parameters according to their types in optimization for dimensionality reduction; on-demand-optimization to progressively build a set of necessary Pareto-optimal plans instead of seeking a complete set for saving resources. Besides, we introduce an acceleration mechanism in TPOA to improve its efficiency, which prunes the non-optimal candidate plans in advance. We theoretically prove the correctness of TPOA, numerically analyze its complexity, and formally give an end-to-end privacy analysis. Through a comprehensive evaluation on its efficiency by running baseline algorithms over synthetic and test-bed benchmarks, we can conclude that TPOA outperforms all benchmarked methods with an overall efficiency improvement of roughly two orders of magnitude; moreover, the acceleration mechanism speeds up TPOA by 10-200×.

A Multi Cluster Secure System for Out Sourced Encrypted Data Bases

Recently, database users have begun to use cloud database services to outsource theirdatabases. The reason for this is the high computation speed and the huge storage capacity thatcloud owners provide at low prices. However, despite the attractiveness of the cloud computingenvironment to database users, privacy issues remain a cause for concern for database ownerssince data access is out of their control. Encryption is the only way of assuaging users' fearssurroundingdataprivacy,butexecutingStructuredQueryLanguage (SQL)queriesoverencrypteddatais achallengingtask,especiallyif the data a re-encryptedby arandomizedencryptionalgorithm. Theexistingsystem, whichisanindexingschemetoencodetheoriginaltable’stuplesinto bit vectors (BVs) prior to the encryption. The resulting index is then usedto narrow therange of retrieved encrypted records from the cloud to a small set of records that are candidatesfor the user's query. Based on the indexing scheme, the designed system is used to execute SQLqueries over the encrypted data. The data are encrypted by a single randomized encryptionalgorithm,namely theAdvanced Encryption Standard-Cipher-Block Chaining (AES-CBC). Inthe existing scheme, theindex values (BVs) are stored atuser's side, and which is supportedmostof relational algebra operators, such as select, join,etc. In the existing system,the clouddata server is able to extract the ratio of two underlying data records, and key management servercandecryptthe ciphertextsofowners’data records,whichbreakthe privacysecurity.

Selecting cloud database services provider through multi-attribute group decision making: a probabilistic uncertainty linguistics TODIM model

Cloud database services platforms (CDSP) have aided in both the generation of new ideas and the reduction of costs. Choosing the finest CDSP is a critical part of enterprise management for helping businesses respond to increasing market pressure and client demand. This study tried to solve this problem and proposed the integration concept of the TODIM (TOmada de Decisão Interativa e Multicritério) and the probability uncertain linguistic (PUL), which is a typical decision-making technique focused on the theory of prospects (PT-PUL-TODIM). The concept of probability uncertainty linguistic term sets (PULTS) is discussed. The cumulative weight of probabilistic double hierarchy linguistic knowledge is then determined by using the following cumulative prospect theory. Based on the experts’ opinions, complex expressions with experience in the Vietnam market were collected and further transformed into a PULTS matrix. However, the CRiteria Importance Through Inter-Criteria Correlation (CRITIC) technique is implemented to calculate the objective criteria weights. The PT-PUL-TODIM optimization approach is developed and adopted for the CDSP. Finally, we compared other methods and conducted a sensitivity analysis to compare the PT-PUL-TODIM approach with other ways to assess the validity, practicality, and usefulness of the technique, and the comparison revealed the merits and limitations of the model.

Corpus-Based Japanese Reading Teaching Database Cloud Service Model

With the widespread application of mobile Internet technology, big data, and artificial intelligence, the demand for computing resources in the whole society is growing rapidly. Database cloud service is an online managed high-availability database service built on a cloud computing platform. It can be used as a basic service of the cloud computing platform and has the characteristics of high service availability and high data reliability. The purpose of this paper is to design a corpus-based Japanese reading teaching database cloud service system and integrate a machine learning-based Structure Query Language (SQL) injection detection function for the system. The proposed work is as follows: (1) Design the basic framework of database cloud service. The module of cloud database service is introduced, and the realization logic of main system functions is analyzed. (2) A SQL injection classifier based on feature engineering and machine learning is designed for database cloud services, which can determine whether the input SQL command is an injection statement. The classifier is deployed between the database instance and the front-end server to integrate the SQL injection detection function into the cloud database instance. Then use the support vector machine algorithm to perform simulation experiments on the classifier model to compare the classification performance of the support vector machine algorithm when using different kernel functions. It is found that when the classification decision threshold is 0.5, the classifier using linear kernel function or radial basis kernel function has better classification performance for SQL sentences.

Perceived Effectiveness of Developing a Mobile System of Formative Test with Handwriting Revision to Devise an Instruction Design Based on Cognitive Apprenticeship Theory

Education helps increase socioeconomic mobility and is an important way of leaving poverty according to the United Nations, especially since COVID-19 hit the world hard in early 2020. A Mobile System of Formative Tests with Handwriting Revision is proposed in the paper, called the MSFT system. The MSFT is developed from the cognitive apprenticeship theory (CAT) in instructional design. The instruction model can be utilized for higher education mathematics teaching/studying for quiz-oriented instruction inside traditional classrooms as well as for distance-learning modes. The MSFT platform provides college undergraduates and graduates an app for a handheld device, which is used to upload their answer sheets with captured photos to the cloud database server. Moreover, instructors can use the platform to revise or assess answer sheets with instructors’ handwriting through web interfaces or apps. Important features of the integrated platform for teachers are (1) grading answer sheets by handwriting, (2) correcting mistakes in the answer sheets by handwriting, (3) writing down instructors’ comments on students’ answer sheets directly, and (4) choosing examples to demonstrate during class presentation, in a single window through web applications. To evaluate MSFT performance and service level for students, a questionnaire survey was conducted for 51 students and separated into an experimental group and a control group. Results from the experiment show that learning attitudes and learning satisfaction were significantly increased with the MSFT system in the experimental group compared to the control group.

A Survey of Comparison Different Cloud Database Performance: SQL and NoSQL

The scales in databases have recently increased, and more expansion is expected in the future. The storage costs gradually have slowed down and quickly expanded the storage capacity. Cloud intrusion has modified the comparisons in recent years. Database performance plays an essential part in the market. Cloud Database focuses on new, conventional databases, specific applications for scalability, dynamic devices, and ease of use. Cloud databases are primarily used for data storage, retrieval, modification, and analysis by tools like business intelligence. These tools can build new business strategies and demonstrate scalability and elasticity while managing vast amounts of data with reliable, customized, and cost-effective services in various applications. This paper provides an overview of cloud computing, Cloud database architecture and kinds, and database as a service. It also highlights the characteristics, deployment, and service model of cloud computing and the performance and functionality of the various SQL and NoSQL cloud database applications and services required to evaluate them. It focuses on the different parameters to assess their performance, such as ease of software portability, transaction capabilities, and the maximum amount of data stored. The primary purpose of this paper is to assist businesses and individuals in understanding how cloud computing may provide them with dependable, personalized, and cost-effective services in many applications.

Design and Implementation of intelligent Home monitoring System based on mobile robot

With the aid of Alibaba Cloud platform, this system builds a cloud database server, and uses short-distance wireless communication ZigBee one-to-many networking technology, RFID technology and Android development and application technology to realize mobile phone clients’ remote real-time acquisition of indoor and outdoor parameters transmitted by mobile robots, and a monitoring system that can be automatically adjusted. The mobile robot car walks indoors according to a predetermined track, and detects the greenhouse degree, light intensity, combustible gas concentration and other environmental parameters of each indoor location in real time and transmits them to the mobile APP terminal for real-time display. By comparing the indoor terminal parameters and set values, the home lighting, fan curtains, etc. can be automatically adjusted by mobile robot or manually adjusted by mobile APP. Finally, the system has been tested as a whole, and the test results show that the system has successfully achieved the expected goal, and the operation is convenient, with a certain market practical value.

An Efficient Secure System for Fetching Data From the Outsourced Encrypted Databases

Recently, database users have begun to use cloud database services to outsource their databases. The reason for this is the high computation speed and the huge storage capacity that cloud owners provide at low prices. However, despite the attractiveness of the cloud computing environment to database users, privacy issues remain a cause for concern for database owners since data access is out of their control. Encryption is the only way of assuaging users’ fears surrounding data privacy, but executing Structured Query Language (SQL) queries over encrypted data is a challenging task, especially if the data are encrypted by a randomized encryption algorithm. Many researchers have addressed the privacy issues by encrypting the data using deterministic, onion layer, or homomorphic encryption. Nevertheless, even with these systems, the encrypted data can still be subjected to attack. In this research, we first propose an indexing scheme to encode the original table’s tuples into bit vectors (BVs) prior to the encryption. The resulting index is then used to narrow the range of retrieved encrypted records from the cloud to a small set of records that are candidates for the user’s query. Based on the indexing scheme, we then design a system to execute SQL queries over the encrypted data. The data are encrypted by a single randomized encryption algorithm, namely the Advanced Encryption Standard-Cipher-Block Chaining (AES-CBC). In the proposed scheme, we store the index values (BVs) at user’s side, and we extend the system to support most of relational algebra operators, such as select, join, etc. Implementation and evaluation of the proposed system reveals that it is practical and efficient at reducing both the computation and space overhead when compared with state-of-the-art systems like CryptDB.

On the Performance of Cloud Services and Databases for Industrial IoT Scalable Applications

In the Industry 4.0 the communication infrastructure is derived from the Internet of Things (IoT), and it is called Industrial IoT or IIoT. Smart objects deployed on the field collect a large amount of data which is stored and processed in the Cloud to create innovative services. However, differently from most of the consumer applications, the industrial scenario is generally constrained by time-related requirements and its needs for real-time behavior (i.e., bounded and possibly short delays). Unfortunately, timeliness is generally ignored by traditional service provider, and the Cloud is treated as a black box. For instance, Cloud databases (generally seen as “Database as a service”—DBaaS) have unknown or hard-to-compare impact on applications. The novelty of this work is to provide an experimental measurement methodology based on an abstract view of IIoT applications, in order to define some easy-to-evaluate metrics focused on DBaaS latency (no matter the actual implementation details are). In particular, the focus is on the impact of DBaaS on the overall communication delays in a typical IIoT scalable context (i.e., from the field to the Cloud and the way back). In order to show the effectiveness of the proposed approach, a real use case is discussed (it is a predictive maintenance application with a Siemens S7 industrial controller transmitting system health status information to a Cloudant DB inside the IBM Bluemix platform). Experiments carried on in this use case provide useful insights about the DBaaS performance: evaluation of delays, effects of involved number of devices (scalability and complexity), constraints of the architecture, and clear information for comparing with other implementations and for optimizing configuration. In other words, the proposed evaluation strategy helps in finding out the peculiarities of Cloud Database service implementations.

Secured Access and Security Issues of Maintenance for Cloud Database

Users are using the cloud database services for storing there important data in order to utilized cloud services. Data confidentiality remains one of the main concerns and the major barrier to the development of cloud services. But at the same time users also think about “whether there data is in secured hands and how it is protected from the outside world?” In order to make sure users that there data is in safe hands, cloud database are using more secured mechanism for accessing the cloud database. In order to achieve the goal of security we are using AES and honey encryption (HE) algorithm for strong authorization. Before authorization we need to have authentication of the users. We are using keystroke dynamics as a biometrics authentication and second one as color code authentication. Cloud databases should also be properly maintained from security point of view. For the different levels of the cloud database we have find out the security issues which provide help in maintenance of cloud database. By implementing these issues the security of the cloud database will be more increased.

Automated Pollution Detection System using IoT and AWS Cloud

Smart cities are one of the upcoming trends in the world. These smart cities include smart traffic light system, smart cars, smart homes, smart traffic monitoring system. As environmental pollution has become the major cause of various problems like climatic changes, improper irrigation methods, depletion of the ozone layer etc. “Automated Pollution Detection System using IoT and AWS Cloud” provides an architecture for integrating IoT and Cloud Computing and an application which is used to detect air pollution by fitting in arduino devices at public places like traffic lights, industrial areas, construction areas etc., and transferring the data using GSM modem to a cloud database server AWS RDS. The cloud server is linked with the EC2 instance (Ubuntu server) in order to publish the web application using EC2. Web Application which is created using Word press and a Mobile application using Android Studio. The Web application shows the value of pollutant at a particular place along with the map facility by using GPS in the Arduino. This is also linked to a mobile application which sends a push notification service (SNS) to our mobile application.

Constant time recovery in Azure SQL database

Azure SQL Database and the upcoming release of SQL Server introduce a novel database recovery mechanism that combines traditional ARIES recovery with multi-version concurrency control to achieve database recovery in constant time, regardless of the size of user transactions. Additionally, our algorithm enables continuous transaction log truncation, even in the presence of long running transactions, thereby allowing large data modifications using only a small, constant amount of log space. These capabilities are particularly important for any Cloud database service given a) the constantly increasing database sizes, b) the frequent failures of commodity hardware, c) the strict availability requirements of modern, global applications and d) the fact that software upgrades and other maintenance tasks are managed by the Cloud platform, introducing unexpected failures for the users. This paper describes the design of our recovery algorithm and demonstrates how it allowed us to improve the availability of Azure SQL Database by guaranteeing consistent recovery times of under 3 minutes for 99.999% of recovery cases in production.

Boundary Value Analysis Testing on Augmented Reality of Indonesian Fruit Recognition at Mekarsari Tourist Park using Cloud Method on Android Mobile Devices

Software testing is a process of implementing a program with the aim of finding an error. A good test case is if the test has the possibility of finding an uncovered error. A successful test is if the test finds an error that was not initially found. One of the testing types available is black box testing. This paper proposes testing using black box testing technique. The black box testing method consists of several ways including equivalence partitioning, boundary value analysis, comparison testing, sample testing, robustness testing, and others. Among the many methods of testing, Boundary Value Analysis was chosen in this study. Boundary Value Analysis is a method of testing by determining the value of the lower limit and upper limit of the data that will be tested. This test is performed on the functions of Augmented Reality prototype of Indonesia fruit recognition by using the cloud method on Android mobile devices. From testing the distance of marker objects to Android mobile devices on cloud recognition using an Android camera shows that the higher the augmentable rating of the target image and the more the number of markers features detected, the easier the image will be tracked by AR. If the distance between the camera and the real object gets farther away, then the virtual object cannot be displayed. Testing with mobile devices using HSDPA, 3G and WIFI networks connecting a cloud database server to display virtual objects shows the average results of devices that use WIFI networks provide the fastest performance.

PLI $$^+$$ + : efficient clustering of cloud databases

Commercial cloud database services increase availability of data and provide reliable access to data. Routine database maintenance tasks such as clustering, however, increase the costs of hosting data on commercial cloud instances. Clustering causes an I/O burst; clustering in one-shot depletes I/O credit accumulated by an instance and increases the cost of hosting data. An unclustered database decreases query performance by scanning large amounts of data, gradually depleting I/O credits. In this paper, we introduce Physical Location Index Plus ( $${PLI}^{\small {{+}}}$$ ), an indexing method for databases hosted on commercial cloud. $${PLI}^{\small {{+}}}$$ relies on internal knowledge of data layout, building a physical location index, which maps a range of physical co-locations with a range of attribute values to create approximately sorted buckets. As new data is inserted, writes are partitioned in memory based on incoming data distribution. The data is written to physical locations on disk in block-based partitions to favor large granularity I/O. Incoming SQL queries on indexed attribute values are rewritten in terms of the physical location ranges. As a result, $${PLI}^{\small {{+}}}$$ does not decrease query performance on an unclustered cloud database instance, DBAs may choose to cluster the instance when they have sufficiently large I/O credit available for clustering thus delaying the need for clustering. We evaluate query performance over $${PLI}^{\small {{+}}}$$ by comparing it with clustered, unclustered (secondary) indexes, and log-structured merge trees on real datasets. Experiments show that $${PLI}^{\small {{+}}}$$ significantly delays clustering, and yet does not degrade query performance—thus achieving higher level of sortedness than unclustered indexes and log-structured merge trees. We also evaluate the quality of clustering by introducing a measure of interval sortedness, and the size of index.

An Internet of Things (IoT)-based risk monitoring system for managing cold supply chain risks

PurposeSince the handling of environmentally sensitive products requires close monitoring under prescribed conditions throughout the supply chain, it is essential to manage specific supply chain risks, i.e. maintaining good environmental conditions, and ensuring occupational safety in the cold environment. The purpose of this paper is to propose an Internet of Things (IoT)-based risk monitoring system (IoTRMS) for controlling product quality and occupational safety risks in cold chains. Real-time product monitoring and risk assessment in personal occupational safety can be then effectively established throughout the entire cold chain.Design/methodology/approachIn the design of IoTRMS, there are three major components for risk monitoring in cold chains, namely: wireless sensor network; cloud database services; and fuzzy logic approach. The wireless sensor network is deployed to collect ambient environmental conditions automatically, and the collected information is then managed and applied to a product quality degradation model in the cloud database. The fuzzy logic approach is applied in evaluating the cold-associated occupational safety risk of the different cold chain parties considering specific personal health status. To examine the performance of the proposed system, a cold chain service provider is selected for conducting a comparative analysis before and after applying the IoTRMS.FindingsThe real-time environmental monitoring ensures that the products handled within the desired conditions, namely temperature, humidity and lighting intensity so that any violation of the handling requirements is visible among all cold chain parties. In addition, for cold warehouses and rooms in different cold chain facilities, the personal occupational safety risk assessment is established by considering the surrounding environment and the operators’ personal health status. The frequency of occupational safety risks occurring, including cold-related accidents and injuries, can be greatly reduced. In addition, worker satisfaction and operational efficiency are improved. Therefore, it provides a solid foundation for assessing and identifying product quality and occupational safety risks in cold chain activities.Originality/valueThe cold chain is developed for managing environmentally sensitive products in the right conditions. Most studies found that the risks in cold chain are related to the fluctuation of environmental conditions, resulting in poor product quality and negative influences on consumer health. In addition, there is a lack of occupational safety risk consideration for those who work in cold environments. Therefore, this paper proposes IoTRMS to contribute the area of risk monitoring by means of the IoT application and artificial intelligence techniques. The risk assessment and identification can be effectively established, resulting in secure product quality and appropriate occupational safety management.