This paper introduces a novel Balanced Binary Whale Optimization Algorithm (BB-WOA) designed specifically for dynamic feature selection in Green Cloud Computing (GCC). Traditional feature selection methods used in cloud resource forecasting often suffer from either suboptimal predictive performance or excessive computational complexity. To address this, we propose significant algorithmic enhancements over the standard Binary Whale Optimization Algorithm (B-WOA), including dynamic binary transition functions, progressive scaling, diversified population initialization via Sobol sequences, balanced exploration-exploitation strategies, and an activation-based recovery mechanism. Our comprehensive experimental evaluation using real-world cloud resource data demonstrates that BB-WOA outperforms existing methods. Specifically, BB-WOA reduces predictive error (RMSE) by up to 7.45% compared to B-WOA and 1.55% compared to Genetic Algorithm (GA). Moreover, BB-WOA achieves computational improvements, reducing execution time by approximately 38.30%, 64.13%, and 78.53% over B-WOA, Random Search (RS), and GA, respectively. Environmentally, the proposed method reduces energy consumption by 38.48% relative to B-WOA, 64.18% compared to RS, and 52.55% relative to GA, while simultaneously selecting significantly fewer features (a reduction of up to 70.77%). These results underscore the effectiveness and sustainability of BB-WOA, positioning it as a highly competitive and environmentally friendly solution.

Abstract One of the challenges in managing cloud computing clusters is assigning resources based on the customers’ needs. For this mechanism to work efficiently, it is imperative that there are sufficient resources reserved to maintain continuous operation, but not too much to avoid overhead costs. Additionally, to avoid the overhead of acquisition time, it is important to reserve resources sufficiently in advance. This paper presents a novel reliable general-purpose mechanism for prediction-based resource usage reservation. The proposed solution should be capable of operating for long periods of time without drift-related problems, and dynamically adapt to changes in system usage. To achieve this, a novel signature-based ensemble prediction method is presented, which utilizes multiple distinct prediction algorithms suited for various use-cases, as well as an anomaly detection mechanism used to improve prediction accuracy. This ensures that the mechanism can operate efficiently in different real-life scenarios. Thanks to a novel signature-based selection algorithm, it is possible to use the best available prediction algorithm for each use-case, even over long periods of time, which would typically lead to drifts. The proposed approach has been evaluated using real-life historical data from various production servers, which include traces from more than 1,500 machines collected over more than a year. Experimental results have demonstrated an increase in prediction accuracy of up to 21.4 percent over the neural network approach. The evaluation of the proposed approach highlights the importance of choosing the appropriate prediction method, especially in diverse scenarios where the load changes frequently.

Due to its flexibility, cloud computing has become essential in modern operational schemes. However, the effective management of cloud resources to ensure cost-effectiveness and maintain high performance presents significant challenges. The pay-as-you-go pricing model, while convenient, can lead to escalated expenses and hinder long-term planning. Consequently, FinOps advocates proactive management strategies, with resource usage prediction emerging as a crucial optimization category. In this research, we introduce the multi-time series forecasting system (MSFS), a novel approach for data-driven resource optimization alongside the hybrid ensemble anomaly detection algorithm (HEADA). Our method prioritizes the concept-centric approach, focusing on factors such as prediction uncertainty, interpretability and domain-specific measures. Furthermore, we introduce the similarity-based time-series grouping (STG) method as a core component of MSFS for optimizing multi-time series forecasting, ensuring its scalability with the rapid growth of the cloud environment. The experiments performed demonstrate that our group-specific forecasting model (GSFM) approach enabled MSFS to achieve a significant cost reduction of up to 44%. • A novel multi-time series forecasting system for cloud resource reservation planning. • A context-aware multi-time series forecasting optimization method. • A novel hybrid ensemble anomaly detection algorithm. • A multifaceted evaluation of the forecasting system using a real-life dataset. • A FinOps-driven qualitative and quantitative assessment of dynamic reservation plans.

Abstract Predicting computing resource usage in any system allows optimized management of resources. As cloud computing is gaining popularity, the urgency of accurate prediction is reduced as resources can be scaled on demand. However, this may result in excessive costs, and therefore there is a considerable body of work devoted to cloud resource optimization which can significantly reduce the costs of cloud computing. The most promising methods employ load prediction and resource scaling based on forecast values. However, prediction quality depends on prediction method selection, as different load characteristics require different forecasting mechanisms. This paper presents a novel approach that incorporates data-driven adaptation of prediction algorithms to generate short- and long-term cloud resource usage predictions and enables the proposed solution to readjust to different load characteristics as well as both temporary and permanent usage changes. First, preliminary tests were performed that yielded promising results – up to 36% better prediction quality. Subsequently, a fully autonomous, multi-stage optimization solution was proposed. The proposed approach was evaluated using real-life historical data from various production servers. Experiment results demonstrate 9.28% to 80.68% better prediction quality when compared to static algorithm selection.

Computing services are increasingly located in computing clouds, which allows for on-demand scalability but may also increase operating costs. It is believed that cloud expenses constitute a significant budget item in companies of all sizes. There is a considerable body of work dedicated to reducing the costs of cloud computing, which is mainly focused on optimizing the use of cloud resources. Such optimization, however, tends to result in the deterioration of computing service responsiveness and, as a result, quality of service parameters, especially when applied to real-world, noisy data which include anomalies. This article presents a novel approach which involves a six-stage optimization process incorporating load prediction supported by machine learning, the discovery of computing service characteristics and long-term planning of resource usage alongside anomaly detection and continuous monitoring with a self-adapting ability. The solution proposed works autonomously, builds knowledge about the optimized system and its load patterns, calculates cost-optimal resource provisioning plans and adapts to rapid environmental changes. Our evaluation using Microsoft’s Azure cloud environment demonstrates savings ranging from 31% to 89% depending on the test scenario; cost reductions for other cloud computing providers were estimated as well.

As mobile device usage is increasing and users are storing large amounts of important and private data on these devices, security is becoming an increasingly important systemic aspect. If private information is leaked, this may have many negative consequences for the owner of a mobile device. Modern mobile devices are generally using only static, one-shot authentication, which does not provide a sufficient level of security throughout the entire usage session. Potential attackers who manage to unlock a mobile device (such as a smartphone) can use it and steal important data. To prevent such situations, a continuous biometrics system can be used which tries to identify the user and grant access to the right person throughout the entire session, thus improving security considerably. In this paper, we present an unobtrusive continuous authentication system which operates on behavioral biometrics and will identify users based on their hand movements when using the smartphone using built-in sensors and public APIs. We will also compare model-based and template-based approaches in terms of adding new users to the system, and test them on an actual device. Because this type of authentication does not attain an accuracy comparable to that of the best static authentication methods, it should not be treated as an alternative security mechanism but rather as another layer of security added to the existing static authentication system.

Abstract This paper describes a new approach to automatic long-term cloud resource usage planning with a novel hybrid anomaly detection mechanism. It analyzes existing anomaly detection solutions, possible improvements and the impact on the accuracy of resource usage planning. The proposed anomaly detection solution is an important part of the research, since it allows greater accuracy to be achieved in the long term. The proposed approach dynamically adjusts reservation plans in order to reduce the unnecessary load on resources and prevent the cloud from running out of them. The predictions are based on cloud analysis conducted using machine learning algorithms, which made it possible to reduce costs by about 50%. The solution was evaluated on real-life data from over 1700 virtual machines.

Cloud computing is gaining popularity among small and medium-sized enterprises. The cost of cloud resources plays a significant role for these companies and this is why cloud resource optimization has become a very important issue. Numerous methods have been proposed to optimize cloud computing resources according to actual demand and to reduce the cost of cloud services. Such approaches mostly focus on a single factor (i.e., compute power) optimization, but this can yield unsatisfactory results in real-world cloud workloads which are multi-factor, dynamic and irregular. This article presents a novel approach which uses anomaly detection, machine learning and particle swarm optimization to achieve a cost-optimal cloud resource configuration. It is a complete solution which works in a closed loop without the need for external supervision or initialization, builds knowledge about the usage patterns of the system being optimized and filters out anomalous situations on the fly. Our solution can adapt to changes in both system load and the cloud provider’s pricing plan. It was tested in Microsoft’s cloud environment Azure using data collected from a real-life system. Experiments demonstrate that over a period of 10 months, a cost reduction of 85 percent was achieved.

The ultimate goal of Mobile Cloud Computing is to allow users of mobile devices to execute their applications and complex numerical tasks on a broad range of cloud services and resources. One of the most challenging problems in the flow of mobile tasks related to remote cloud services is the security of all aspects of communication, service security and the reliability of cloud resources. In this paper, we developed a new security-aware job flow model for mobile computational clouds. In our model, we defined dedicated algorithm models such as the Filtration Algorithm and Prediction Module to generate an optimal secure system architecture for task and data processing and to ensure optimal cloud resource and service utilization. The robust performance of our model has been demonstrated by experimental analysis. Results of the experiments performed show that our flow model significantly enhances the security level of computations compared to a configuration in which computation time is the major criterion for job processing optimization.

Summary In this article, we present an original agent‐based adaptive task scheduling system which optimizes the performance of services in the mobile cloud computing environment using machine learning mechanisms and context information. The system learns how to allocate resources appropriately: how to schedule services/tasks optimally between the mobile device and the cloud. Decisions are made taking into account the context (e.g., network connection type, location, security level). In this study, a supervised learning agent architecture and service selection algorithm are proposed to solve this problem. Adaptation is performed online on a mobile device. To verify the solution proposed, appropriate software has been developed and a series of experiments has been conducted. Results demonstrate that owing to the experience gathered and the learning process performed, the decision module becomes more efficient in assigning the task to either the mobile device or cloud resources. In the face of presented improvements, the security issues inherent in the context of mobile services/applications and cloud computing are further discussed. As threats associated with mobile data offloading are a serious concern, often ruling out the utilization of cloud services, we propose a more security focused approach for our solution, preferably without hindering performance.

Abstract Predicting demand for computing resources in any system is a vital task since it allows the optimized management of resources. To some degree, cloud computing reduces the urgency of accurate prediction as resources can be scaled on demand, which may, however, result in excessive costs. Numerous methods of optimizing cloud computing resources have been proposed, but such optimization commonly degrades system responsiveness which results in quality of service deterioration. This paper presents a novel approach, using anomaly detection and machine learning to achieve cost-optimized and QoS-constrained cloud resource configuration. The utilization of these techniques enables our solution to adapt to different system characteristics and different QoS constraints. Our solution was evaluated using a system located in Microsoft’s Azure cloud environment, and its efficiency in other providers’ computing clouds was estimated as well. Experiment results demonstrate a cost reduction ranging from 51% to 85% (for PaaS/IaaS) over the tested period.

In this paper we present an original adaptive task scheduling system, which optimizes the energy consumption of mobile devices using machine learning mechanisms and context information. The system learns how to allocate resources appropriately: how to schedule services/tasks optimally between the device and the cloud, which is especially important in mobile systems. Decisions are made taking the context into account (e.g. network connection type, location, potential time and cost of executing the application or service). In this study, a supervised learning agent architecture and service selection algorithm are proposed to solve this problem. Adaptation is performed online, on a mobile device. Information about the context, task description, the decision made and its results such as power consumption are stored and constitute training data for a supervised learning algorithm, which updates the knowledge used to determine the optimal location for the execution of a given type of task. To verify the solution proposed, appropriate software has been developed and a series of experiments have been conducted. Results show that due to the experience gathered and the learning process performed, the decision module has consequently become more efficient in assigning the task to either the mobile device or cloud resources. In face of presented improvements, the security issues inherent within the context of mobile application and cloud computing are further discussed. As threats associated with mobile data offloading are a serious concern, often preventing the utilization of cloud services, we propose a more security focused approach for our solution, preferably without hindering the performance.

In this paper we propose a novel reservation plan adaptation system based on machine learning. In the context of cloud auto-scaling, an important issue is the ability to define and use a resource reservation plan, which enables efficient resource scheduling. If necessary, the plan may allocate new resources upon reservation where a sufficient amount of resources is available. Our solution allows the updating of a reservation plan initially prepared by an administrator. It makes it possible to adapt reservation plans one or more weeks ahead. Hence, it allows time for the administrator to analyze the plan and discover potential problems with resource under-provisioning or over-provisioning, which may prevent server overload in the former case and unnecessary expenses in the latter. It also makes it possible to extract and analyze the knowledge learned, which may provide useful information about resource usage characteristics. The proposed solution is tested on OpenStack using real Wikipedia server traffic data. Experimental results demonstrate that machine learning enables an improvement in resource usage.

The majority of Mobile Cloud Computing architectures do not currently take into account resource usage or the costs of running the system in the Cloud, and an assumption is usually made that there is only one virtual machine per user. This approach blocks the large-scale adoption of Mobile Cloud Computing. We apply architectural patterns that are common in traditional Cloud Computing in order to decrease resource demand in Mobile Cloud Computing. We present and compare three architectures: the reference one, which represents the current common approach, and two architectures that leverage the multi-tenancy and asynchrony concepts in order to reduce cloud resource usage. The evaluation conducted has demonstrated that architectures using common Cloud Computing patterns can reduce the resources used by the current approach by 23 while serving 23 times more tasks. This proves that applying common Cloud Computing patterns to the Mobile Cloud Computing environment is a good way of constraining resource demand.

As the concept of merging the capabilities of mobile devices and cloud computing is becoming increasingly popular, an important question arises: how to optimally schedule services/tasks between the device and the cloud. The main objective of this paper is to investigate the possibilities for using a decision module on mobile devices in order to autonomously optimize the execution of services within the framework of Mobile Cloud Computing while taking context into account. A novel model of the decision module with learning capabilities, service-oriented architecture, and service selection optimization algorithm are proposed to solve this problem. To achieve autonomous, online learning on mobile devices, we apply supervised learning. Information about the context, task description, the decision made and its results such as calculation time or power consumption are stored and form training data for a supervised learning algorithm, which updates the knowledge used by the decision module to determine the optimal place for the execution of a given type of task. To verify the solution proposed, service-oriented mobile processing systems for multimedia file conversion have been developed and series of experiments have been executed. Results show that the decision module has become more efficient in assigning the task to either the mobile device or cloud resources.

Recently, there has been an increasing need for secure, efficient and simple notification methods for mobile systems.Such systems are meant to provide users with precise tools best suited for work or leisure environments and a lot of effort has been put into creating a multitude of mobile applications.However, not much research has been put at the same time into determining which of the available protocols are best suited for individual tasks.Here a number of basic notification methods are presented and tests are performed for the most promising ones.An attempt is made to determine which methods have the best throughput, latency, security and other characteristics.A comprehensive comparison is provided, which can be used to select the right method for a specific project.Finally, conclusions are provided and the results of all the tests conducted are discussed.

The paper proposes the concept of adaptive message aggregation for telemetry applications that use GPRS connectivity. The method optimizes the power consumed during data transmission, what is useful in the modern telemetry devices powered from renewable energy sources. The concept has been verified in the levee monitoring scenario.