Secure and Efficient Query Processing over Hybrid Clouds

Nowadays, big media healthcare data processing in cloud has become an effective solution for satisfying QoS demands of medical users. It can support various healthcare services such as pre-processing, storing, sharing, and analysis of monitored data as well as acquiring context-awareness. However, to support energy and cost savings, the union of cloud data centers termed as cloud confederation can be an promising approach, which helps a cloud provider to overcome the limitation of physical resources. However, the key challenge in it is to achieve multiple contradictory objectives, e.g., meeting the required level of services defined in service level agreement, maintaining medial users'application QoS, etc. while maximizing profit of a cloud provider. In this paper, for executing heterogeneous big healthcare data processing requests from users, we develop a local and global cloud confederation model, namely FnF, that makes an optimal selection decision for target cloud data center(s) by exploiting Fuzzy logic. The FnF trades off in between profit of cloud provider and user application QoS in selecting federated data center(s). In addition, FnF enhances its decision accuracy by precisely estimating the resource requirements for the big data processing tasks using multiple linear regression. The proposed FnF model is validated through numerical as well as experimental evaluations. Simulation results depict the effectiveness and efficiency of the FnF model compared to state-of-the-art approaches.

Query Processing is a key determinant in the overall performance of distributed databases. It requires processing of data at their respective sites and transmission of the same between them. These together constitute a distributed query processing strategy (DQP). DQP aims to arrive at an efficient query processing strategy for a given query. This strategy involves generation of efficient query plans for a distributed query. In case of distributed relational queries, the number of possible query plans grows exponentially with an increase in the number of relations accessed by the query. This number increases further when the relations, accessed by the query, have replicas at different sites. Such a large search space renders it infeasible to find optimal query plans. This paper presents a query plan generation algorithm that attempts to generate optimal query plans, for a given query, using genetic algorithm. The query plans so generated involve fewer sites, thus leading to efficient query processing. Further, experimental results show that the proposed algorithm converges quickly towards optimal query plans for an observed crossover and mutation probability.

Secure Data Sharing and Processing in Heterogeneous Clouds

Cloud computing paved the way to many technical facilities for companies to develop their business needs in more effective and efficient manner. Combining private and public cloud into so called Hybrid cloud has made a positive leap in business by allowing applications and data to be shared among enterprises. However, many challenges and issues have arisen when adopting the hybrid cloud to manage, store and process data. The most critical one of these challenges is the security of the adopted Hybrid cloud. This research presented a comprehensive study about the security challenges in the Hybrid cloud computing as well as the suggested solutions. The study used a Systematic Literature Review (SLR) process to collect, review and summarize published articles from IEEE and Springer Nature databases and between 2020 and 2021. As a result, there were 7 eligible articles selected according to the search criteria and fully reviewed. The findings have revealed that there are four main challenges which are Data Security, Access Control, Privacy, Data Leakage and Cy- ber Attacks. Future studies should be conducted using different databases to have further investigation regarding the security in Hybrid clouds.

The increasing adoption of hybrid cloud computing introduces strategic and operational challenges, highlighting the critical role of top management in decision-making. While prior studies largely focus on technological and security aspects, this study addresses a growing need to explore leadership-driven factors influencing hybrid cloud adoption. Using a systematic literature review (SLR) guided by the PRISMA framework, 24 peer-reviewed articles were selected from reputable databases based on defined inclusion and exclusion criteria. This review identifies key decision-making factors shaping top management's adoption of hybrid cloud solutions across industries, framed within the Technology–Organization–Environment (TOE) framework. Technological factors included complexity, data security, relative advantage, and compatibility; organizational considerations centered on cost, resource availability, and leadership readiness; while competitive pressure emerged as the dominant environmental factor. Industry-specific findings revealed that healthcare, IT, and local government emphasized relative advantage and compatibility, whereas telecommunications prioritized innovation and operational efficiency. Small and medium-sized enterprises (SMEs) focused on scalability, cost-effectiveness, and security. Common adoption barriers included security concerns, integration complexity, and organizational readiness. The study also examined decision-making models and proposed a conceptual framework highlighting leadership’s influence on hybrid cloud strategies. By shifting focus from technical to managerial perspectives, this research fills a gap in current literature and offers actionable insights for business leaders, IT executives, and policymakers. The findings support more strategic, leadership-informed approaches to hybrid cloud integration. Future studies should empirically validate these factors across varied contexts to strengthen the understanding of hybrid cloud adoption dynamics.

Virtual computing resources are provided via a cloud system that is both clever and intelligent. Based on the user's request, computing resources are made available. A hybrid cloud is the best option for storing and accessing user data for cloud deployments. Maintaining security in a hybrid cloud environment is time-consuming. This study provides a novel strategy for securing data in the hybrid cloud by ensuring the user's data is protected. Users' data in a hybrid cloud is protected using cryptographic approaches provided in this approach. Using this strategy, users' data may be protected in public and private clouds using various encryption methods. The suggested data security paradigm offers various advantages to both consumers and providers in terms of data security. Three symmetric encryption methods are offered as a service in the cloud. The concept is implemented as a cloud-based application hosted in the cloud, and the effectiveness of three strategies is assessed. They are evaluated in terms of performance and security. Using the recommended encryption methods in a hybrid cloud environment is more efficient than using other methods. The proposed technique can be used for relational data. It can be modified and enhanced to process multimedia data.

NoSQL and relational databases have been integrated in response to the increasing demand for scalable and efficient data management in hybrid cloud environments. The differences in data structures and query processing methods between these databases present both challenges and opportunities when designing an optimized hybrid system. This study explores the integration of NoSQL and relational databases to maximize data storage, retrieval, and processing efficiency across multiple cloud systems. NoSQL databases excel in handling unstructured and semi-structured data, offering flexibility and scalability, whereas relational databases provide robust consistency and structured query capabilities. Despite the wide availability of relational databases, certain applications require the dynamic adaptability of NoSQL systems, making integration a viable solution. The research evaluates key performance parameters, including query execution speed, scalability, data consistency, and resource utilization. Cloudsim is used for simulation, allowing for an in-depth comparison between standalone and hybrid database models. Experimental results indicate that the proposed hybrid model improves query performance by 30%, reduces latency by 20%, and enhances scalability by 40% compared to using relational or NoSQL databases alone. The novelty of this approach lies in its ability to overcome the limitations of each database type by their strengths in a dynamic cloud environment. The results highlight the effectiveness of hybrid database integration in optimizing cloud-based data management while ensuring seamless operation, adaptability, and resource efficiency.

The application scheduling is an important requirement in the cloud computing context. It allows to define the required resources to execute applications tasks following predefined criteria, for instance, maximum execution time, number of virtual machines, volume of data, among others. Selecting process to choose the most appropriate execution structure is driven by scheduling algorithms. This paper proposes a scheduling mechanism for data processing in cloud computing environments. Such mechanism analyzes some specific variables in the business context of a software house specialized in software for lawyers and law offices. The main goal of this mechanism is to fulfill the seasonal company's demand using IaaS services and considering two policies: (i) the maximum execution time allowed by the application may not be exceeded and (ii) the data have to be processed considering the lowest possible monetary cost. The proposed solution generates strategies to select the best set of virtual machines to process the current bunch of data considering the amount of data, the estimated execution time for each specific strategy and the monetary cost of the virtual machines sets. In the context of this work, the strategy concept means the schedule of a set of virtual machines to process a specific amount of data, load balancing decisions and the parallelism of application's execution flow. The proposed solution has resulted in great impact for that company since it allowed the vertiginous increase of the amount of clients served.

To avoid making data equivalent to big price it is significant to investigate how to deploy the VMs so as to lower both communication cost and deployment cost for streaming data processing (SBDP). The authors motivate such a bicriteria problem by considering task scheduling and resource allocation for SBDP in geo-distributed datacenters. They introduce some representative data processing frameworks in Clouds, with a special emphasize on SBDP. To support efficient data processing and analysis, service providers need to characterize the features of the different data services and propose new processing models. The geo-distributed datacenters are running rich and complex tasks to support a variety of user requirements. To improve the service performance and guarantee the quality of service at the same time, task scheduling and resource allocation algorithm in a Cloud platform is a fundamental issue in achieving efficient Cloud services.

The increasing volumes of relational data let us find an alternative to cope with them. Recently, several hybrid approaches (e.g., HadoopDB and Hive) between parallel databases and Hadoop have been introduced to the database community. Although these hybrid approaches have gained wide popularity, they cannot avoid the choice of suboptimal execution strategies. We believe that this problem is caused by the inherent limits of their architectures. In this demo, we present Tajo, a relational, distributed data warehouse system on shared-nothing clusters. It uses Hadoop Distributed File System (HDFS) as the storage layer and has its own query execution engine that we have developed instead of the MapReduce framework. A Tajo cluster consists of one master node and a number of workers across cluster nodes. The master is mainly responsible for query planning and the coordinator for workers. The master divides a query into small tasks and disseminates them to workers. Each worker has a local query engine that executes a directed acyclic graph of physical operators. A DAG of operators can take two or more input sources and be pipelined within the local query engine. In addition, Tajo can control distributed data flow more flexible than that of MapReduce and supports indexing techniques. By combining these features, Tajo can employ more optimized and efficient query processing, including the existing methods that have been studied in the traditional database research areas. To give a deep understanding of the Tajo architecture and behavior during query processing, the demonstration will allow users to submit TPC-H queries to 32 Tajo cluster nodes. The web-based user interface will show (1) how the submitted queries are planned, (2) how the query are distributed across nodes, (3) the cluster and node status, and (4) the detail of relations and their physical information. Also, we provide the performance evaluation of Tajo compared with Hive.

Homomorphic cryptosystem-based secure data processing model for edge-assisted IoT healthcare systems

Over the last few years, the context of big data has gained a significant traction due to many factors. While the public cloud model had been deeply studied to face the increasing demand for large-scale data processing capabilities, many organizations are now focusing on the hybrid cloud model, where the classic scenario is enriched with a private (company owned) cloud -- e.g., for the management of sensible data. In this work, we propose HyMR, a policy to enable autonomic cloud bursting for clusters of virtual machines operating MapReduce jobs over a hybrid cloud. This policy -- together with an infrastructure level system for resource provisioning in hybrid clouds -- can be used to face the temporary (or permanent) lack of computational resources on the private cloud, allowing cloud bursting in the context of big data applications. By means of an empirical evaluation of the system scale-up/-down performance, we show that HyMR policy allows the user to significantly reduce the data-processing time.

Coping with uncertainty is a challenging and complex problem particularly in hybrid cloud environments-private cloud plus public cloud. Conflicting goals of minimizing the cost and performance, unknown prior knowledge about task running times, and a lack of estimation tools are just a few of the challenges that resource management systems in those environments encounter. The aim in this paper is to find Pareto-optimal schedules for large-scale Bag-of-Tasks (BoT) applications that meet user defined constraints, such as deadline or budget or some tradeoff between them. BoT applications are common in science and engineering and consist of many independent tasks. To achieve the user's chosen Pareto-optimal schedule, we develop a dynamic resource allocation process for hybrid clouds. We also present a hybrid approach to estimating task running times that incorporates several estimators with a feedback control system to cope with the inherent uncertainty in such estimation. Through extensive experiments on a test bed hybrid cloud, using Amazon EC2 as a public cloud, we show that the proposed approach can achieve near optimality with little overhead, and consistently achieves a solution within 2% of the user's chosen Pareto-optimal schedule. Further, we demonstrate that our approach performs better than an extended List scheduling approach by reducing both the total cost and time needed to run the application by almost 20% and 5% on average, respectively.

In the environment of wide-area networks such as the Internet, distributed query processing is becoming problematic due to changes of the underlying networks and user requirements. In this new context, conventional query processing strategies with the homogeneous assumption will not work well, because they are unable to adapt to unexpected changes in the performance of the communication networks. In this paper, we address the issue of how to make distributed query processing aware of these changes. We introduce the idea of integrating distributed query processing with quality of service (QoS) management and accordingly illustrate our view of QoS-aware distributed query processing.

Fault-tolerance has long been a feature of database systems, with transactions supporting the structuring of applications so as to ensure continuation of updating applications in spite of machine failures. For read-only queries the perceived wisdom has been that support for fault-tolerance is too expensive to be worthwhile. Distributed query processing is coming to be seen as a promising way of implementing applications that combine structured data and analysis operations in dynamic distributed settings such as computational grids. Such a query may be long-running and having to redo the whole query after a failure may cause problems (e.g. if the result may trigger business or safety critical activities). This work describes and evaluates a new scheme for adding fault-tolerance to distributed query processing through a rollback-recovery mechanism. The high level expression of user requests in a physical algebra offers opportunities for tuning the fault-tolerance provision so as to reduce the cost, and give better performance than employment of generic fault-tolerance mechanisms at the lowest level of query processing. This paper outlines how the publicly-available OGSA-DQP computational grid-based distributed query processing system can be modified to include support for fault-tolerance and presents a performance evaluation which includes measurements of the cost of both protocol overheads and rollback-recovery, for a set of example distributed queries.