Serverless computing has reshaped the cloud computing landscape by offering benefits such as auto-scalability, streamlined operational management, and granular billing. As its adoption grows, challenges related to performance and cost optimization in hybrid architectures combining private servers and public cloud clusters have emerged. Central to these challenges are achieving optimal response latency and balancing performance and cost. To address these challenges, this paper introduces an adaptive routing service specifically designed for hybrid environments, proficient in leveraging real-time function metrics. Our proposed service pivots on three integral components: a monitor that captures performance metrics and raises alarms for predefined anomalies; a forecaster that predicts function latency across clusters, which includes wait and execution times and produces request distributions for each cluster to equalize the overall function latency; and a router then processes incoming requests, taking cues from the forecaster’s predictions. Notably, based on user-defined objectives, the forecaster can be directed to either minimize latency or optimize execution costs through trading off wait or execution time. Comprehensive evaluations on AWS and Azure clusters using the open source FaaS framework Apache OpenWhisk showcase our approach’s effectiveness, yielding a 9% improvement in average latency, a 45% decrease in standard deviation latency and a 17% cost reduction compared to conventional 50-50 routing. The advantages of elevated monitoring frequency are also illuminated, emphasizing quicker convergence times.

Towards enabling the automated and optimized FaaS deployment of applications in a hybrid-cloud setting, the application requirements should be met by comparing them to the capabilities of the available resources of available clusters. To this end, semantic matching between the application characteristics and the individual descriptions of available compute clusters (e.g. from public or private cloud or edge facilities available) is required. In this work, such a system is proposed, namely the Reasoning Framework, which performs semantic matching between application and resource (meta)data and facilitates information sharing among the FaaS platform components leveraging Knowledge Graphs, ontology technologies, and semantic reasoning. The proposed system harvests information from the application function workflow, provided as a graph by the function editor specification (based on Node-RED), including developer-inserted annotations during the design process, and maps them to the dynamic information retrieved from the available clusters. The Reasoning Framework interprets these data as graphs and automatically applies several semantic rules that enable filtering of the available resources and efficient information retrieval through a RESTfull interface. The paper also discusses experimental results to further showcase the advantages of the proposed approach.

Multi-access Edge Computing (MEC) is expected to play an essential role in enabling 5G (and beyond) technologies and services. This has driven numerous micro-datacenter (μDC) deployment studies in the literature, with a common goal of addressing the optimal μDC placement and dimensioning problems. Along this line, this paper aims at clustering subareas with similar network activity dynamics, to find a good hotspots' representation over the urban area. Leveraging common Machine Learning (ML) and statistics principles, the main contribution of this paper is two-fold: (i) the definition and selection of dynamicity features based on real telecommunications datasets; and (ii) the granular cluster evaluation and analysis based on agglomerative hierarchical clustering. Three feature sets (containing 20, 12 and 8 features, respectively) are evaluated at varying precision levels, showing interesting trends on the number of clusters, heatmaps and intra-cluster correlation. These could potentially provide some valuable indications on the placement and dimensioning of the$\mu$DCs.

Addressing increased demand in cloud environments is typically achieved through auto-scaling the use of further resources allocated. However this comes at the trade-off of increased back-end resource stress as well as runtime costs. In this paper a request batch aggregation and management pattern is proposed and implemented, especially for cases of computational load that needs a heavyweight environment setup (such as performance prediction and AI services). The pattern acts as a preprocessing layer and accumulates requests, withholding their forwarding to the backend based on the conditions of execution (frequency of requests), in order to regulate back-end resource stress without adding extra resources. Different controlling logics are tried out for batch request release, ranging from simpler ones (batch size static regulation, time out interval) to more elaborate ones (ANN model predicting the desired batch size) for adapting the pattern configuration during runtime. Results indicate severe reduction of back-end stress through significantly reducing needed containers (therefore costs as well), enhanced response time as well as avoidance of system breakdown under heavy load. The pattern can be applied in both microservices and serverless environments, especially in Edge cases where resources are constrained.

Compliance to regulatory requirements is a critical concern in information system development projects. Managing this aspect is increasingly challenging while failures impose costly consequence on the organizations world-wide. However, how a legislation may or may not affect information system development projects is often not easily identifiable due to lack of clear understanding and guidelines. This paper presents compliant information system development (CISMET) ontology, exploiting concepts from 21 existing ontologies (regarding regulatory compliance and information system development). The key findings are the six parent classes in the CISMET ontology describing the system development goals, services, process, activities, artifacts, and resources. Also, there are 26 sub classes and 21 class properties that describe various concepts and their relationships in regulatory compliant information system development. The General Data Protection Regulation (GDPR) of the European Union has been instantiated in the proposed framework to indicate how regulatory requirements compliance concepts are mapped to system development projects. Thus, involved stakeholders (information system researchers and system developers) may identify dependencies and actions needed with relation to various rules in the regulation and their link to the system elements through a relevant software application. The latter enables users to easily submit queries towards the backend ontology through a specialized front-end application that can aid in formulating and submitting these queries.

Resources shortage during a time of crisis is a typical but also dangerous situation that health systems need to mitigate. When the health crisis reaches a global level, this shortage becomes more critical and spans across all related resource categories and types (materials, equipment, personnel etc). The aim of this work is to present an online flexible platform, based on a combination of REST API and messaging technologies, that targets at offering a mechanism for swift registration of resource types across affected entities, government or private institutions, individual or corporate donors and volunteers. A full range of functionalities is presented, including dynamic submission of resource availability or demand, matchmaking based on criteria, investigation of supply or demand status, asynchronous alerting, general overview of the situation as well as analytics capabilities. Thus either centralized government coordination or individual corporate or volunteer efforts can be redirected and optimize the distribution and usage of resources.

In aiming to create added intelligence for Things interfacing with IoT principles in the form of Services, Smart Heating Management is consistently a field of promising research. In the currently demonstrated work, an approach will be described for an operational software framework of modular components that can create intelligence in Housing Units as those presented in our scenario. The framework is based on the lightweight Case Based Reasoning approach and principles in order to describe the Problem and Solution of heating management, as well as to extract knowledge from generic historical data. Collaboration between houses is included through the sharing of anonymized high level problem-solution data, as an instantiation of the same social learning principles that govern human behavior and enable Knowledge diffusion. The approach is validated through historical data acquired in two time periods from the Camden (London) community residencies and demonstrates an average of 22% savings in boiler active time. Knowledge sharing is also analyzed along with the benefits and pitfalls it might produce.

Deciding and reserving appropriate resources in the Cloud, is a basic initial step for adopters when employing an Infrastructure as a Service to host their application. However, the size and number of Virtual Machines used, along with the expected application workload, will highly influence its operation, in terms of the observed Quality of Service. This paper proposes a machine learning approach, based on Artificial Neural Networks, for mapping Quality of Service required levels and (expected) application workload to concrete resource demands. The presented solution is evaluated through a comercial Customer Relationship Management application, generating a training set of realistic workload and Quality of Service measurements in order to illustrate the effectiveness of the proposed technique in a real-world scenario.

The problem of Denial of Service attacks has siginificant effects for any computing system available through the public domain. In the case of service oriented infrastructures or Clouds, it becomes even more critical since elasticity policies tied with metrics like Key Performance Indicators (KPIs) can lead a Cloud adoptor to significant monetary losses. DoS attacks increase the KPIs, which in turn trigger the elastic increase of resources but without financial benefit for the owner of the cloud-enabled application (Economic Denial of Sustainability). Given the numerous scenarios of DoS attacks and the nature of services computing (in many cases involving legitimate automated traffic requests and bursts), networking mitigation approaches may not be sufficient. In this paper, the concept of Key Completion Indicators (KCIs) is provided and an analysis framework based on a probabilistic approach is proposed that can be applied on the application layer in cloud-deployed applications and elasticity policies, in order to avoid the aforementioned situation. KCIs indicate the level of completeness and provided revenue of the requests made towards a publicly available service and together with the KPIs can lead to a safer result with regard to elasticity. An initial architecture of this DoS Identification as a Service is portrayed.

Cloud services have emerged as an innovative IT provisioning model in the recent years. However, after their usage severe considerations have emerged with regard to their varying performance due to multitenancy and resource sharing issues. These issues make it very difficult to provide any kind of performance estimation during application design or deployment time. The aim of this paper is to present a mechanism and process for measuring the performance of various Cloud services and describing this information in machine understandable format. The framework is responsible for organizing the execution and can support multiple Cloud providers. Furthermore we present approaches for measuring service performance with the usage of specialized metrics for ranking the services according to a weighted combination of cost, performance and workload.

In modern utility computing infrastructures, like grids and clouds, one of the significant actions of a service provider is to predict the resources needed by the services included in its platform in an automated fashion for service provisioning optimization. Furthermore, a variety of software toolkits exist that implement an extended set of algorithms applicable to workload forecasting. However, their automated use as services in the distributed computing paradigm includes a number of design and implementation challenges. In this paper, a decoupled framework is presented, for taking advantage of software like GNU Octave in the process of creating and using prediction models during the service life cycle of a SOI. A performance analysis of the framework is also conducted. In this context, a methodology for creating parametric or gearbox services with multiple modes of operations based on the execution conditions is portrayed and is applied to transform the aforementioned service framework to optimize service performance. A new estimation algorithm is introduced, that creates performance rules of applications as black boxes, through the creation and usage of genetically optimized artificial neural networks. Through this combination, the critical parameters of the networks are decided through an evolutionary iterative process.

In the recent years, cloud computing has emerged as the new IT paradigm that promises elastic resources on a pay-per-use basis. The challenges of cloud computing are focused around massive data storage and efficient large scale distributed computation. Hadoop, a community driven Apache project has provided an efficient and cost effective platform for large scale computation using the map-reduce methodology, pioneered by Google. In this paper, the design of a Hadoop-based data management system as the front-end service for Cloud data management is investigated. This framework is enriched with Restful APIs in front of Hadoop and a series of components that aim to extend Hadoop's functionality beyond its well known back-end, heavy data processing scope. These components are used to enrich security, logging and data analysis features and also data access compatibility between different but interconnected Cloud providers (federated Clouds). Hadoop capabilities are also extended in a quest for intelligent decision making regarding the choice of the fittest services for federation in a federated cloud scenario, in addition to legally compliant behaviour regarding the geographical location of data storage.

The emergence of new environments such as Cloud computing highlighted new challenges in traditional fields like performance estimation. Most of the current cloud environments follow the Software, Platform, Infrastructure service model in order to map discrete roles / providers according to the offering in each “layer”. However, the limited amount of information passed from one layer to the other has raised the level of difficulty in translating user-understandable application terms from the Software layer to resource specific attributes, which can be used to manage resources in the Platform and Infrastructure layers. In this paper, a generic black box approach, based on Artificial Neural Networks is used in order to perform the aforementioned translation. The efficiency of the approach is presented and validated through different application scenarios (namely FFMPEG encoding and real-time interactive e-Learning) that highlight its applicability even in cases where accurate performance estimation is critical, as in cloud environments aiming to facilitate real-time and interactivity.

The advancements in distributed computing have driven the emergence of service-based infrastructures that allow for on-demand provision of ICT assets. Taking into consideration the complexity of distributed environments, significant challenges exist in providing and managing the offered on-demand resources with the required level of Quality of Service (QoS), especially for real-time interactive multimedia applications. Common to these applications is the service-oriented approach being followed, thus applications consist of application service components that interact in order to provide the corresponding application functionality. In this paper we present an architectural design of a complete Workflow Management System (WfMS) for enacting application service components that have been deployed in a Virtualized Environment. The WfMS aims at synchronizing the application components, monitoring their execution and handling faults while adhering to the QoS requirements of the application.

A constantly increasing number of applications from various scientific sectors are finding their way towards adopting grid technologies in order to take advantage of their capabilities: the advent of grid environments made feasible the solution of computational intensive problems in a reliable and cost-effective way. In this paper we present a grid-based approach for aggregation of data that are obtained from various sources (e.g. cameras, sensors) and their analysis with the use of genetic algorithms. By also taking into consideration general historical data and patient-specific medical information, we present the realization of the proposed approach with an application scenario for personalized healthcare and medicine.