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In cloud computing environments, virtualization is used to share physical machine (PM) resources among multiple users by creating virtual machines (VMs). Running the PM consumes a large amount of energy. Additionally, the PM will be overloaded when the demand for resources exceeds the PM capacity. This overload on the PM leads to violations of Service Level Agreements (SLAs). Dynamic VM consolidation techniques use live migration of VMs to optimize resource utilization and minimize energy consumption. However, excessive migration of VMs impacts negatively the application performance due to the incurred overhead at the runtime. This paper presents a modified genetic-based VM consolidation (MGVMC) strategy that aims to replace VMs in an online manner taking into account energy consumption, SLA violations, and the number of VM migrations. The MGVMC strategy utilizes the genetic algorithm to migrate VMs to the appropriate PM in a way that minimizes the number of over-utilized and under-utilized physical machines (PMs) as low as possible. The performance of the MGVMC strategy was evaluated using the CloudSim Plus framework with a large number of VMs and workload traces from the PlanetLab platform. The experimental results revealed that the MGVMC strategy achieved a significant improvement in energy consumption, SLA violations, and the number of VM migrations compared to other recent approaches. These results demonstrate the effectiveness of the MGVMC strategy in optimizing VM consolidation in the cloud environment.

From a set of existing tuples, a skyline operator retrieves only a subset, superior tuples that are of a person’s interest and are non-dominant. Processing of queries using the skyline operator is an expensive and exhaustive task. It gets more complicated when skyline queries are applied on partially complete data and databases are distributed over different data centers. Incompleteness in data raises many issues such as compromise on transitivity property and the threat of cyclic dominance to occur within database. To overcome such issues this paper proposes an efficient algorithm called Cloud-based Incomplete Data Skyline algorithm (CIDS) for processing skyline queries over partially complete databases in cloud environment. The algorithm retrieves superior tuples with the aim of reducing domination tests between the tuples, decreasing processing time and reducing the huge amount of data flow from one data center to another. Several experiments have been conducted over different types of datasets, and results have proven that the proposed algorithm outplays the existing algorithms in terms of processing time, domination tests as well as the amount of data flow.

Skyline queries have been widely used as an effective query tool in many contemporary database applications. The main concept of skyline queries relies on retrieving the non-dominated tuples in the database which are known skylines. In most database applications, the contents of the databases are dynamic due to the continuous changes made towards the database. Typically, the changes in the contents of the database occur through data manipulation operations (INSERT and/or UPDATE). Performing these operations on the database results in invalidating the most recent skylines before changes are made on the database. Furthermore, the presence of incomplete data in databases becomes frequent phenomena in recent database applications. Data incompleteness causes several challenges on skyline queries such as losing the transitivity property of the skyline technique and the test dominance process between tuples being cyclic. Reapplying skyline technique on the entire updated incomplete database to determine the new skylines is unwise due to the exhaustive pairwise comparisons. Thus, this paper proposes an approach, named Incomplete Dynamic Skyline Algorithm (IDSA) which attempts to determine the skylines on dynamic and incomplete databases. Two optimization techniques have been incorporated in IDSA, namely: pruning and selecting superior local skylines. The pruning process attempts to exploit the derived skylines before the INSERT/UPDATE operation made on the database to identify the new skylines. Moreover, selecting superior local skylines process assists in further eliminating the remaining non-skylines from further processing. These two optimization techniques lead to a large reduction in the number of domination tests due to avoiding re-computing of skylines over the entire updated database to derive the new skylines. Extensive experiments have been accomplished on both real and synthetic datasets, and the results demonstrate that IDSA outperforms the existing solutions in terms of the number of domination tests and the processing time of the skyline operation.

Data incompleteness becomes a frequent phenomenon in a large number of contemporary database applications such as web autonomous databases, big data, and crowd-sourced databases. Processing skyline queries over incomplete databases impose a number of challenges that negatively influence processing the skyline queries. Most importantly, the skylines derived from incomplete databases are also incomplete in which some values are missing. Retrieving skylines with missing values is undesirable, particularly, for recommendation and decision-making systems. Furthermore, running skyline queries on a database with incomplete data raises a number of issues influence processing skyline queries such as losing the transitivity property of the skyline technique and cyclic dominance between the tuples. The issue of estimating the missing values of skylines has been discussed and examined in the database literature. Most recently, several studies have suggested exploiting the crowd-sourced databases in order to estimate the missing values by generating plausible values using the crowd. Crowd-sourced databases have proved to be a powerful solution to perform user-given tasks by integrating human intelligence and experience to process the tasks. However, task processing using crowd-sourced incurs additional monetary cost and increases the time latency. Also, it is not always possible to produce a satisfactory result that meets the user's preferences. This paper proposes an approach for estimating the missing values of the skylines by first exploiting the available data and utilizes the implicit relationships between the attributes in order to impute the missing values of the skylines. This process aims at reducing the number of values to be estimated using the crowd when local estimation is inappropriate. Intensive experiments on both synthetic and real datasets have been accomplished. The experimental results have proven that the proposed approach for estimating the missing values of the skylines over crowd-sourced enabled incomplete databases is scalable and outperforms the other existing approaches.

Skyline queries rely on the notion of Pareto dominance, filter the data items by keeping only those data items that are the best, most preferred, also known as skylines, from a database to meet the user's preferences. Skyline query has been studied extensively and a significant number of skyline algorithms have been proposed, mostly attempt to resolve the optimisation problem that is mainly associated with a reduction in the processing time of skyline computations. In today's era, the presence of incomplete data in a database is inevitable. Furthermore, databases are dynamic in nature in which their states change throughout the time to reflect the current and latest information of the applications. The skylines derived before changes are made towards the initial database are no longer valid in the new state of the database. Blindly examining the entire database to identify the new set of skylines is unwise as not all data items are affected by the changes made towards the database. Hence, in this paper we propose a solution, named DyIn-Skyline, which is capable of deriving skylines over a dynamic and incomplete database, by exploiting only those data items that are affected by the changes. Several experiments have been conducted and the results show that our proposed solution outperforms the previous works with regard to the number of pairwise comparisons and processing time.

Given the significance of skyline queries, they are incorporated in various modern applications including personalized recommendation systems as well as decision-making and decision-support systems. Skyline queries are used to identify superior data items in the database. Most of the previously proposed skyline algorithms work on a complete database where the data are always present (non-missing). However, in many contemporary real-world databases, particularly those databases with large cardinality and high dimensionality, such assumption is not necessarily valid. Hence, missing data pose new challenges if the processing skyline queries cannot easily apply those methods that are designed for complete data. This is due to the fact that imperfect data cause the loss of the transitivity property of the skyline method and cyclic dominance. This paper presents a framework called Optimized Incomplete Skyline (OIS) which utilizes a technique that simplifies the skyline process on a database with missing data and helps prune the data items before performing the skyline process. The proposed strategy assures that the number of the domination tests is significantly reduced. A set of experiments has been accomplished using both real and synthetic datasets aimed at validating the performance of the framework. The experiment results confirm that the OIS framework is indeed superior and steadily outperforms the current approaches in terms of the number of domination tests required to retrieve the skylines.