Fog computing has emerged as a promising paradigm for distributed data processing, but managing numerous devices in fog domains is complex due to the scale of the infrastructure. To address this challenge, organizing fog devices into fog colonies allows independent management on a smaller scale. We present a genetic algorithm (GA) approach that utilizes hierarchical clustering to define the fog colony layout. The GA selects a subset of colony candidates from the dendrogram obtained with hierarchical clustering and optimizes the network communication time between users and applications and the execution time of algorithms that manage application placement in each colony. We deployed an NSGA-II, a multi-objective approach for GAs, to evaluate our proposal. Our experimental results demonstrate that combining a GA with hierarchical clustering improves both optimization objectives. We conducted nine experiment scenarios, varying the number of applications and fog devices. Our results show that even in the worst-case scenario, the GA’s results dominated the solutions obtained by two control algorithms after only 137 generations. Additionally, the number of genetic solutions and their homogeneous distribution in the Pareto front were satisfactory.

The increasing complexity of Compute Continuum environments calls for efficient resource optimization techniques. In this paper, we propose and evaluate three distributed designs of a genetic algorithm (GA) for resource optimization, within an increasing degree of distribution. The designs leverage the execution of the GA in the infrastructure devices themselves by dealing with the specific features of this domain: constrained resources and wide geographical distribution of the devices. For their evaluation, we implemented a benchmark case using the NSGA-II for the specific problem of optimizing the application placement, according to the guidelines of our three distributed designs. These three experimental scenarios were compared against a control case, representing a traditional centralized version of this GA algorithm, evaluating solution quality and network overhead. The results show that the design with the lowest distribution degree, which keeps centralized storage of the objective space, achieves comparable solution quality to the traditional approach but incurs a higher network load. The second design, which completely distributes the population between the workers, reduces network overhead but exhibits lower solution diversity while keeping enough good results in terms of optimization objective minimization. The second design demonstrates the highest overall efficiency in optimization performance and network cost. Finally, the proposal with a distributed population that only interchanges solutions between the workers’ neighbors achieves the lowest network load but with compromised solution quality.

Abstract Virtualization and server consolidation are the technologies that govern today’s data centers, allowing both efficient management at the functionality level as well as at the energy and performance levels. There are two main ways to virtualize either using virtual machines or containers. Both have a series of characteristics and applications, sometimes being not compatible with each other. Not to lose the advantages of each of them, there is a trend to load data centers by nesting containers in virtual machines. Although there are good experiences at a functional level, the performance and energy consumption trade-off of these solutions is not completely clear. Therefore, it is necessary to study how this new trend affects both energy consumption and performance. In this work, we present an experimental study aimed to investigate the behavior of nesting containers in virtual machines while executing CPU-intensive workloads. Our objective is to understand what performance and energy nesting configurations are equivalent or not. In this way, administrators will be able to manage their data centers more efficiently.

Abstract Virtualization technologies are extensively utilized in data centers, particularly cloud computing. This facilitates data center management and diminishes the number of physical machines (servers) and, subsequently, their cooling requirements, leading to cost, space, and power consumption reductions. When applications in data centers are executing independent parallel transactions, but with similar performance requirements, the appropriate level of virtual machine consolidation on a server poses a fundamental challenge for capacity planning. This article introduces a method to evaluate the performance speedup achieved through virtualization on any server and the effects of virtualization and consolidation overheads on physical or virtual machine scalability. This research formalizes the speedup and overheads, using classical computer architecture statements. but at the same time proposes a new method to analyze these overhead amounts and types, showing the scalability and efficiency of different consolidations in the same server and its comparison against no consolidation. This work also proposes a new way to determine the optimal number of physical servers and the optimal number of consolidated virtual machines for a given transaction workload. The real experimentation was performed with different workload sizes, types of virtualizations and different servers. The method presented also facilitates the representation of linear scalability against the real degree of parallelism of either physical machines or consolidated virtual machines for a given transaction workload, as well as striking the right balance between speedup and energy in virtual server consolidation.

Several approaches exist to generate synthetic data centre traces for various purposes: from augmenting operating traces for data centre simulators and digital twins to forecasting the incoming workload to improve the data centre behaviour. The evaluation of the quality of synthetically generated multivariate time-series datasets, such as those related to data-centre traces, is not a trivial task, since complex patterns and correlation between variables may be present. This paper proposes a new multivariate time-series evaluation framework that computes a set of metrics and figures that can be used to measure the quality of synthetically generated data-centre traces. We then employ the proposed tool to compare two synthetic data centre traces with the original trace and assess their quality. These synthetic traces have been generated by means of Generative Adversarial Networks (GAN). In this work, we employ TimeGAN, a GAN model focused on the generation of multivariate time series traces. We finally show how the proposed framework provides us with a set of metrics consistent with the observable behaviour and numerical insights on the quality of the generated data centre traces, which are hard to acquire otherwise.

In recent years, the increase in the use of services in cloud, fog, edge, and IoT ecosystems has been very notable. On the one hand, environmental sustainability is affected by this type of ecosystem since it can produce a large amount of energy consumption which translates into CO2 emissions into the atmosphere. On the other hand, due to the COVID-19 pandemic, the use of these ecosystems has increased considerably. Thus, it is necessary to apply policies and techniques to maximize sustainability within these ecosystems. Some of these policies and techniques are those based on artificial intelligence. However, the current processing of these policies and techniques can also consume a lot of resources. From this perspective, this article aims to clarify whether the sustainability of cloud/fog/edge/IoT ecosystems is improved by the application of artificial intelligence. To do this, a systematic literature review is developed in this paper. In addition, a set of classifications of the analyzed works is proposed based on the different aspects related to these ecosystems, their sustainability, and the applicability of artificial intelligence to improve them.

Abstract Server consolidation is one of the most commonly used techniques for reducing energy consumption in datacenters; however, this results in inherent performance degradation due to the coallocation of virtual servers, i.e., virtual machines (VMs) and containers, in physical ones. Given the widespread use of containers and their combination with VMs, it is necessary to quantify the performance degradation in these new consolidation scenarios, as this information will help system administrators make decisions based on server performance management. In this paper, a general method for quantifying performance degradation, that is, server overhead, is proposed for arbitrary consolidation scenarios. To demonstrate the applicability of the method, we develop a set of experiments with varying combinations of VMs, containers, and workload demands. From the results, we can obtain a suitable method for quantifying performance degradation that can be implemented as a recursive algorithm. From the set of experiments addressing the hypothetical consolidation scenarios, we show that the overhead depends not only on the type of hypervisor and the workload distribution but also on the combination of VMs and containers and their nesting, if feasible.

Summary Fog computing architectures distribute computational and storage resources along the continuum from the cloud to things. Therefore, the execution of services or the storage of files can be closer to the users. The main objectives of fog computing domains are to reduce the user latency and the network usage. Availability is also an issue in fog architectures because the topology of the network does not guarantee redundant links between devices. Consequently, the definition of placement polices is a key challenge. We propose a placement policy for data replication to increase data availability that contrasts with other storage policies that only consider a single replica of the files. The system is modeled with complex weighted networks and topological features, such as centrality indices. Graph partition algorithms are evaluated to select the fog devices that store data replicas. Our approach is compared with two other placement policies: one that stores only one replica and FogStore, which also stores file replicas but uses a greedy approach (the shortest path). We analyze 22 experiments with simulations. The results show that our approach obtains the shortest latency times, mainly for writing operations, a smaller network usage increase, and a similar file availability to FogStore.

Fog computing extends the cloud to where things are by placing applications closer to the users and Internet of Things devices. The placement of those applications, or their services, has an important influence on the performance of the fog architecture. Improving the availability and the latency of the applications is a challenging task due to the complexity of this type of distributed system. In this paper, we propose a service placement policy inspired by complex networks. We are able to increase the service availability and the quality of service (QoS) satisfaction rate by first mapping applications to communities of fog devices and then transitively placing the services of the applications on the fog devices of the community. The underlying idea is to place as many interrelated services as possible in the devices closest to the users. We compare our solution with an integer linear programming approach, and the simulation results show that our proposal obtains improved QoS satisfaction and service availability.

Technical debt has successfully captured the interest of practitioners and researchers alike. We argue that the concept of technical debt holds much more currency within the strategic Information Systems literature. Hence, we have developed a research framework for expanding the concept of technical debt into a new concept we dub "technology debt". This expanded concept aims at capturing the path-dependencies reported in literature in regard to digital investments, and to make these both researchable and manageable. Technology debt is defined as the constraining effects of previous governance decisions on future decisions, including technical debt as important factor, but not unique. According to the findings, technology debt is a feasible method for highlighting the constraining aspects of IT investments and including these in the investment evaluation by governing body. This offers support for corporate stakeholders involved in the decision-making surrounding IT related investments, particularly in IT governance and management processes.

Resource management is a key factor in the performance and efficient utilization of cloud systems, and many research works have proposed efficient policies to optimize such systems. However, these policies have traditionally managed the resources individually, neglecting the complexity of cloud systems and the interrelation between their elements. To illustrate this situation, we present an approach focused on virtualized Hadoop for a simultaneous and coordinated management of virtual machines and file replicas. Specifically, we propose determining the virtual machine allocation, virtual machine template selection, and file replica placement with the objective of minimizing the power consumption, physical resource waste, and file unavailability. We implemented our solution using the non-dominated sorting genetic algorithm-II, which is a multi-objective optimization algorithm. Our approach obtained important benefits in terms of file unavailability and resource waste, with overall improvements of approximately 400 and 170 percent compared to three other optimization strategies. The benefits for the power consumption were smaller, with an improvement of approximately 1.9 percent.

Our work proposes an IT governance model for healthcare institutions based on the ISO/IEC 38500 standard. We adapt the standard taking into account our previous work about IT governance and other papers related with the challenges faced by hospitals and other healthcare organizations. The proposed IT governance model may be used to develop particular IT governance frameworks compatible with the standard.

The objective of this paper is to demonstrate that sharing the vocabulary for service description enhances the service discovery mechanism. The proposed solution is a distributed architecture for enhanced context-aware web services. The starting point is a motivation scenario in which university students are trying to share a solution about a specific problem in a campus environment. The proposed solution includes an ontology-based context model for describing service vocabulary. This model is shared among users to facilitate the description of their petitions. The Devices Profile for Web Services (DPWS) was integrated in the architecture as a framework for sending, describing and discovering web services. The adopted validation methodology consisted in comparing scenarios with the context ontology as vocabulary source and others that use synonyms from Word net. A series of discrete-event simulations were set up by specifying performance metrics related to the discovery mechanism, control parameters and user behavior models. The results have shown that using the context ontology enhances the discovery ratio as well as the mean discovered services per request.

Nowadays, in Spain, accessing to emergency services and customer care is done only by traditional telephone calls to the number 061. In order to facilitate interaction between the system and the user emergency system, we are improving and expanding new channels of communication. Given that the use of smart mobile devices is widespread in our society, we are developing a mobile application for emergency management providing the same assistance and brand new features. This paper includes the technological field of emergency management and describes in detail the structure of our new application.

The performance of web caches, in Web Content Management Systems, can be improved by assembling only some of the content elements of a web page in the application server, and finishing the assembling process in the cache proxy. Due to this, the cache is able to manage parts of the web page instead of whole pages, which improves its performance. We propose an algorithm based on decision trees and obtained in a training process to create content fragmentation designs. Data mining is used in the training phase. Inputs of the classification algorithm must be monitored from the system producing small overheads. The paper contribution are the validation of: the use of classification system to self-adapt content fragmentation designs to improve the web performance, the parameters set to be used as inputs of the decision tree and finally, the suitability of using decision trees to represent and implement, in the classification system, the previous extracted knowledge. All these aspects are validated by experimental results extracted from a test-bed.

Ontology Matching (OM) consists of set of processes that decide and establish if ontology elements share lexical or semantic relationships. For a correct alignment, when we establish semantic relationships, it is necessary to find out the meaning of the elements that depends on the context. In this paper, we propose an extension of OWL called OWL-M to improve the representation of multiple types of relationships among ontology elements taking account the context. In function of each OWL-M alignment, a similarity degree is computed. Moreover, to assist in decision making when human participation is required, we define a set of annotations to describe the logical decisions used by OWL-M processes.

Companies and web sites store information about the user behaviour which is not shared with other people. A tool for public and free sharing of this information has been developed. This tool is installed as a Firefox Add-on and it stores, in a central system (Web Mining Service), the gathered information in the browser of the client.

We address two issues in this work: on one hand, we consider and compare five throughput predictors to be used in a Web system in order to estimate its future performance and, on the other hand, we propose an adaptive QoS-aware admission control algorithm that is based on a resource allocation scheme that includes a throughput predictor. In order to obtain a low overhead, the monitoring of the arrival traffic to the Web system is done following an adaptive time slot scheduling based on the burstiness factor that we defined in a previous work. Simulation results of the behaviour of the five throughput predictors and the admission control algorithm are presented and discussed.

This paper presents a load balancing solution for a cluster of Web servers that considers QoS in order to differentiate the service provided to the clients when the Web system is receiving a high volume of traffic. Adaptability is a needed feature to suit the balancing parameters to the fluctuations of the Web traffic. We monitor the incoming workload to the system and tune the parameters more frequently when the bursty arrivals trend increases. Throughput prediction enables to estimate the maximum utilization allowed in the servers for each service, and then to set a maximum number of accepted requests to set a limit in the utilization of the servers and to avoid a possible congestion situation, while guaranteeing the QoS. Classical dispatching policies are introduced and compared to analyse the behaviour of the algorithm. HTTP static and dynamic requests are considered in the workload.

We present a Web system architecture using ontologies to improve the behavior of the system from the performance viewpoint. Since Web system performance indexes depend on state and parameter values on runtime period, the proposed system configuration will change during this period. In order to perform this change, the Web system is monitorized and gathered information stored into a knowledge base. We also model the performance of the different Web system elements intervening in the configuration using the knowledge base expressed by means of ontologies. An example of the use of this ontology in cache tier is also presented. We propose the use of performance reasoners to change the configuration during runtime period based on the information supplied from the knowledge base.

Several techniques and applications have been proposed to aid the decision taking process in the system performance domain. Most of these techniques have depicted the performance model of systems through annotations of performance measurements coming from specific software descriptive syntactical languages. However, the semantic representation of performance information provides the possibility of its ulterior machine-processable logical interpretation and therefore the applicability of inference rules about a particular domain. In this work, we propose a performance ontology together with the system performance analysis technique as an example of framework building for intelligent applications based on semantic Web