Declarative orchestration systems are fundamental to scaling MLOps, yet they predominantly rely on execution-time validation, creating a significant configuration-deployment feedback gap. In MLOps, this gap is particularly costly because machine learning pipelines often include compute-intensive training, data processing, and deployment stages where deterministic errors—such as missing artifacts or resource mismatches—may be detected only after remote execution has started. We introduce a four-layer static validation framework that intercepts these failures before any compute is provisioned. The framework integrates syntax parsing, schema compliance, dependency resolution, and execution graph analysis to provide a generalized validation approach for declarative systems. We instantiate the framework for GitHub Actions and evaluate it against 843 workflow configurations: 161 curated templates, 598 production workflows from top public repositories, and 84 MLOps workflows from 18 repositories. The approach achieves 88.5% recall on representative error patterns. While semantic validation achieves a lower 35%–37% recall due to the inherent opacity of custom scripts and runtime-dependent expressions, the framework maintains 100% precision on curated workflows, ensuring developer trust by minimizing false alarms. On a labeled 100-workflow real-world benchmark, head-to-head comparison with actionlint confirms distinctive strengths in registry-layer detection. Validation completes within seconds, offering an 89 × reduction in feedback latency. Under an explicit scenario model, our results indicate that this “fail-fast” approach can prevent 1.1% of total CI/CD executions, yielding an estimated $760,000 in annual infrastructure savings and a 4.2-ton reduction in C O 2 emissions for a mid-sized organization. This framework establishes a scalable, resource-efficient foundation for sustainable MLOps engineering.

A database system is one of the most common cloud applications where elasticity allows to dynamically allocate resources in response to changing workload demands. In such systems, users usually configure resources based on empirical decisions, for example by boosting computational resources with the expectation of improving database throughput. The latter also suggests that users manually scale resources and tune the database configurations offline to meet the application demands. However, this approach could rapidly increase infrastructure expenditures while this is also time consuming. In this paper, we propose SONA, a framework to support constrained performance optimization of cloud applications using a hybrid approach of artificial neural networks and genetic algorithms. The proposed framework monitors the source system to identify the optimal configurations that maximize application performance based on genuine workload executions. As a result, the experimental analysis presents a novel dataset collected from TPC-C runs on MySQL server. The optimization process is being constrained to satisfy user and application requirements including the cloud infrastructure expenditures, the cost-performance ratio, the baseline performance and the average percentage of idle CPU resources. Furthermore, SONA uses a cloned containerized environment that replicates the main application to avoid system overhead during the optimization process. Our results demonstrate the effectiveness of SONA framework to optimize application performance of OLTP applications deployed on cloud.

Cloud computing emerged as a technology that offers scalable access to computing resources in conjunction with low maintenance costs. In this domain, cloud users utilize virtualized resources to benefit from on-demand and long-term pricing strategies. Although the latter consists of a more cost-efficient solution, it requires accurate estimations of future workload demands, which is a challenging task. Furthermore, clouds offer threshold-based auto-scaling rules that need to be manually controlled by the users according to application requirements. Still, tuning scaling parameters is not trivial, since it is mainly based on static scaling rules that may lead to unreasonable costs and quality of service violations. In this work we introduce ADA-RP, an adaptive auto-scaling framework for reliable resource provisioning in the cloud. ADA-RP uses historical time series data for training K-means and convolutional neural networks (CNN) to categorize future workload demands as High, Medium or Low based on CPU utilization. We auto-scale cloud resources in real-time based on the predicted workload demand to reduce costs and improve application performance. The experimental analysis is based on TPC-C runs on MySQL containers deployed on the Google Cloud Platform. Experimental results are prosperous, demonstrating the ability of ADA-RP (i) to reduce MySQL deployment costs by 48% in a single-tenant environment, and (ii) to double the executed queries per second in a multi-tenant environment considering user’s budget requirements.

Today, Internet users expect Web applications to be fast, performant, and always available. With the emergence of Internet of Things (IoT), data collection and the analysis of streams have become more and more challenging. Behind the scenes, application owners and cloud service providers work to meet these expectations, yet, the problem of how to most effectively and efficiently auto-scale a Web application to optimize for performance while reducing costs and energy usage is still a challenge. In particular, this problem has new relevance due to the continued rise of IoT and microservice-based architectures. A key concern, that is often not addressed by current auto-scaling systems, is the decision on which microservice to scale in order to increase performance. Our aim is to design a prototype auto-scaling system for microservice-based Web applications that can learn from the past service experience. The contributions of the work can be divided into two parts: 1) developing a pipeline for microservice auto-scaling and 2) evaluating a hybrid sequence and supervised learning model for recommending scaling actions. The pipeline has proven to be an effective platform for exploring auto-scaling solutions, as we will demonstrate through the evaluation of our proposed hybrid model. The results of the hybrid model show the merit of using a supervised model to identify which microservices should be scaled up more.

Today, the emergence of the industry revolution systems such as Industry 4.0, Internet of Things, and big data frameworks poses new challenges in terms of storage and processing of real-time data. As systems scale in humongous sizes, a crucial task is to administer the variety of different subsystems and applications to ensure high performance. This is directly related with the identification and elimination of system failures and errors, while the system runs. In particular, database systems may experience abnormalities related with decreased throughput or increased resource usage, that in turn affects system performance. In this article, we focus on not only SQL (NoSQL) database systems that are ideal for storing sensor data in the concept of Industry 4.0. This typically includes a variety of applications and workloads that are difficult to online monitor, thus making anomaly detection a challenging task. Creating a robust platform to serve such infrastructures with minimum hardware or software failures is a key challenge. In this article, we propose RADAR, an anomaly detection system that works on real time. RADAR is a data-driven decision-making system for NoSQL systems, by providing process information extraction during resource monitoring and by associating resource usage with the top processes, to identify anomalous cases. In this article, we focus on anomalies such as hardware failures or software bugs that could lead to abnormal application runs, without necessarily stopping system functionality, e.g., due to a system crash, but by affecting its performance, e.g., decreased database system throughput. Although different patterns may occur through time, we focus on periodic running workloads (e.g., monitoring daily usage) that are very common for NoSQL systems, and Internet of Things scenarios where data streams are forwarded to the Cloud for storage and processing. We apply various machine learning algorithms such as autoregressive integrated moving average (ARIMA), seasonal ARIMA, and long-short-term memory recurrent neural networks. We experimentally analyze our solution to demonstrate the benefits of supporting online erroneous state identification and characterization for modern applications.

Cloud computing emerges as the key platform for IoT data storage, processing and analytics due to its simplicity, scalability and affordability (i.e. no up-front investment, low operation costs). Remote patient monitoring in particular can benefit from for this technology in many ways: (a) the new solution is acceptable by many user categories and provides invaluable assistance to chronic patients and the elderly, (b) it is expected to increase users autonomy and confidence and enable self-managing of their condition with the help of caregivers remotely, (c) it reduces the need for face-to-face appointments with doctors and days in hospital. This work reviews key challenges for reliable and secure remote health monitoring based on experience and lessons learned from applying the above technology to the problem of real-time data collection using both wide-range and short-rage wireless protocols and health sensors.

Today, cloud computing applications are rapidly constructed by services belonging to different cloud providers and service owners. This work presents the inter-cloud elasticity framework, which focuses on cloud load balancing based on dynamic virtual machine reconfiguration when variations on load or on user requests volume are observed. We design a dynamic reconfiguration system, called inter-cloud load balancer (ICLB), that allows scaling up or down the virtual resources (thus providing automatized elasticity), by eliminating service downtimes and communication failures. It includes an inter-cloud load balancer for distributing incoming user HTTP traffic across multiple instances of inter-cloud applications and services and we perform dynamic reconfiguration of resources according to the real time requirements. The experimental analysis includes different topologies by showing how real-time traffic variation (using real world workloads) affects resource utilization and by achieving better resource usage in inter-cloud.

Recent technological advances in Cloud Computing technology paved the way for developing and offering advanced services for remote monitoring in many industry areas; including the agricultural sector. In this work, we developed a Cloud-based farm management information system, referred as CloudFarm. CloudFarm offers a variety of services that helps farmers to manage and keep control of their farms and crops, by supporting all the basic agricultural activities according to set of good agricultural practices. CloudFarm is accessible to farmers, as a mobile application running on a smartphone or tablet that connects with a Cloud service for data analysis and orchestration. The "on the go" system means that farmers can manage and capture scheduled or performed activities directly from fields, while using the smartphone application that is synchronized with data stored permanently on the cloud so that farmers can have always access to up-date and real-time information. Furthermore, CloudFarm offers a Web application where farmers can monitor their data and certified advisors can monitor the farmers' agricultural activities and advise the m about their farm status and best planning of farm and crops works.

In recent years, different kinds of natural hazards or man-made disasters happened that were diversified and difficult to control with heavy casualties. In this paper, we focus on the rapid and systematic evacuation of large-scale densities of people after disasters to reduce loss in an effective manner. The optimal evacuation planning is a key challenge and becomes a hotspot of research and development. We design our system based on an Internet of Things (IoT) scenario that utilizes a mobile cloud computing platform in order to develop the crowd lives oriented track and help optimization system (CLOTHO). CLOTHO is an evacuation planning system for large-scale densities of people in disasters. It includes the mobile terminal (IoT side) for data collection and the cloud backend system for storage and analytics. We build our solution upon a typical IoT/fog disaster management scenario and we propose an IoT application based on an evacuation planning algorithm that uses the artificial potential field (APF), which is the core of CLOTHO. APF is conceptualized as an IoT service, and can determine the direction of evacuation automatically according to the gradient direction of the potential field, suitable for rapid evacuation of large population. Based on APF, we propose an evacuation planning algorithm names as APF with relationship attraction (APF-RA). APF-RA guides the evacuees with relationship to move to the same shelter as much as possible, to calm evacuees and realize a more humanitarian evacuation. The experimental results show that CLOTHO (using APF and APF-RA) can effectively improve convergence rate, shorten the evacuation route length and evacuation time, and make the remaining capacity of the surrounding shelters well balanced.

Inter-cloud is an approach that facilitates scalable resource provisioning across multiple cloud infrastructures. In this paper, we focus on the performance optimization of Infrastructure as a Service (IaaS) using the meta-scheduling paradigm to achieve an improved job scheduling across multiple clouds. We propose a novel inter-cloud job scheduling framework and implement policies to optimize performance of participating clouds. The framework, named as Inter-Cloud Meta-Scheduling (ICMS), is based on a novel message exchange mechanism to allow optimization of job scheduling metrics. The resulting system offers improved flexibility, robustness and decentralization. We implemented a toolkit named “Simulating the Inter-Cloud” (SimIC) to perform the design and implementation of different inter-cloud entities and policies in the ICMS framework. An experimental analysis is produced for job executions in inter-cloud and a performance is presented for a number of parameters such as job execution, makespan, and turnaround times. The results highlight that the overall performance of individual clouds for selected parameters and configuration is improved when these are brought together under the proposed ICMS framework.

Cloud computing and the future Internet concept highlight new requirements for the software engineering phases including testing and validation of modular web services. A major reason is because cloud applications are developed by services belonging to different providers, thus making software testing a really challenging issue. In this work, we propose a testing methodology that includes two fold testing actions; a unit testing of cloud service APIs following white and black box techniques and an integration testing strategy by identifying services that could interface with each other. In addition, we present the Elvior TestCast T3 (TTCN-3) testing tool for automation of use case testing. We demonstrate the results of the methodology when applied to different cloud services and we present a discussion of our conclusions for a real world use case, in which we applied this methodology.

In this paper, we introduce a new methodology for automatic phase detection and characterization for applications running on the cloud. In contrast to existing approaches, our approach is novel in the fact that it is non-intrusive, more general (supports multiple programming languages), lightweight and can detect phase changes online as the application runs. We evaluate our approach for a number of C, C++ and Java application servers that are widely used in the cloud. Our method achieves a phase change detection accuracy upto 98.2% with an average detection delay of less than 0.01 seconds after the start or end of a phase. We also show a sample use case of our phase detection and characterization method for anomaly detection in the cloud.

In latest years, there is a corresponding growth for cloud computing services, especially for those that can be offered as modular components in order to construct Future Internet (FI) applications. In addition, the emergence of inter-clouds as the mean to achieve utilization of various services available from different providers highlights new research directions. Following the business benefits of the cloud services into more advanced and modular systems the requirements for effective accounting and billing with regards to the service usage and the accurate billing of consumers of services becomes challenging. This work focuses on cloud platforms, their platform segregations and services, and presents an accounting and billing service that can be used to rate, charge and bill consumers of within a cloud or an inter-cloud platforms. The solution is developed based on FIWARE platform, which promotes the usage of Cloud Computing in Europe by designing general purpose cloud services namely as Generic Enablers (GEs). Here we design and implement an Accounting and Billing GE that is built upon OpenStack platforms and hosted as an easily to deploy and use cloud service.

Today, IoT systems are designed and implemented to address specific challenges based on domain specific requirements, thus not taking into consideration issues of openness, scalability, interoperability and use-case independence. As a result, they are less principled, lacking standards, vendor oriented and hardly replicable since the same IoT architecture cannot be used in more than one use-cases. To address the fragmentation of existing IoT solutions, the IoT-A project proposes an architecture reference model that defines the principles and standards for generating IoT architectures and promoting the interoperation of IoT solutions. However, IoT-A addresses the architecture design problem, and does not focus on whether existing cloud platforms can offer the tools and services to support the implementation of IoT-A compliant IoT systems. In this work we propose an architecture based on IoT-A that focuses on the FIWARE open cloud platform that in turn provides the building blocks of future Internet applications and services. We further correlate FIWARE and IoT-A projects to identify the key features for FIWARE to support IoT-A compliant system implementations.

As cloud based platforms become more popular, it becomes an essential task for the cloud administrator to efficiently manage the costly hardware resources in the cloud environment. Prompt action should be taken whenever hardware resources are faulty, or configured and utilized in a way that causes application performance degradation, hence poor quality of service. In this paper, we propose a semantic aware technique based on neural network learning and pattern recognition in order to provide automated, real-time support for resource anomaly detection. We incorporate application semantics to narrow down the scope of the learning and detection phase, thus enabling our machine learning technique to work at a very low overhead when executed online. As our method runs "life-long" on monitored resource usage on the cloud, in case of wrong prediction, we can leverage administrator feedback to improve prediction on future runs. This feedback directed scheme with the attached context helps us to achieve an anomaly detection accuracy of as high as 98.3% in our experimental evaluation, and can be easily used in conjunction with other anomaly detection techniques for the cloud.

Gesture recognition technology enables new means of user communication and interaction with machines. This work focuses on gesture recognition by analyzing data, obtained by motion sensors, in the cloud. We present Interact, a cloud based gesture recognition system that uses FIWARE. To promote the development of Future Internet (FI) applications, all functionalities of Interact are offered as cloud services, enabling elasticity, lower cost of maintenance and off-site efficient data storage. To promote productivity, Interact offers a REST API for recognizing, storing and managing customized gesture collections in the cloud as well as for subscribing to sensors. The system is sensor independent and is designed to be compliant with the most popular motion sensors (i.e., Leap Motion or Kinect). To demonstrate the functionalities of Interact, a system prototype has been developed utilizing the LEAP Motion sensor, offering all the previously described functionalities. The prototype is hosted on FIWARE Lab and is available for testing.

Summary The evolution of communication protocols, sensory hardware, mobile and pervasive devices, alongside social and cyber‐physical networks, has made the Internet of things (IoT) an interesting concept with inherent complexities as it is realised. Such complexities range from addressing mechanisms to information management and from communication protocols to presentation and interaction within the IoT. Although existing Internet and communication models can be extended to provide the basis for realising IoT, they may not be sufficiently capable to handle the new paradigms that IoT introduces, such as social communities, smart spaces, privacy and personalisation of devices and information, modelling and reasoning. With interaction models in IoT moving from the orthodox service consumption model, towards an interactive conversational model, nature‐inspired computational models appear to be candidate representations. Specifically, this research contests that the reactive and interactive nature of IoT makes chemical reaction‐inspired approaches particularly well suited to such requirements. This paper presents a chemical reaction‐inspired computational model using the concepts of graphs and reflection, which attempts to address the complexities associated with the visualisation, modelling, interaction, analysis and abstraction of information in the IoT. Copyright © 2013 John Wiley & Sons, Ltd.

Over the recent years, the rapid development of Cloud Computing has driven to a large market of cloud services that offer infrastructure, platforms and software to everyday users. Yet, due to the lack of common accepted standards, cloud service providers use different technologies and offer their clients services that are operated by a variety of proprietary APIs. The lack of standardization results in numerous heterogeneities (e.g., heterogeneous service descriptions, message level naming conflicts, data representation conflicts etc.) making the interoperation, collaboration and portability of services a very complex task. In this work we focus on the problems of interoperability and portability in Cloud Computing, we address their differences and we discuss some of the latest research work in this area. We evaluate and point out relationships between the identified solutions. Finally we present a use case scenario of the FI-STAR project, that aims to bridge the gap of healthcare provision in cloud computing. We demonstrate the architecture of the project and we discuss possible interoperability and portability issues.

Ant Colony Optimization (ACO) and other similar nature inspired mechanisms like artificial neural networks, swarm intelligence and evolutionary algorithms are based on naturally existing Complex Adaptive Systems (CAS). Human immune system, sand dune ripples, and ant foraging are some examples of the naturally existing CAS. Participating agents in these systems interact according to simple local rules which result in complex behavior and self-organization at system level. Adhoc grids are dynamic in nature and participating nodes show intermittent and volatile participation. Resource availability fluctuates over time inadhoc grids and results in a new adhoc grid state. These changes require adoption of the adhoc grid to anew state by applying some self organizing mechanism. In this paper, we present nature-inspired (ACO), micro-economic based mechanisms for infrastructure level self-organization in adhoc grids. These mechanisms help in achieving a scalable, dynamic and a self-organizing adhoc grid infrastructure. These mechanisms are evaluated with varying workloads in different network conditions. Study of these mechanisms helped in understanding the effect of ACO based self-organization mechanism on the infrastructural spectrum, ranging from completely centralized to fully decentralized.

'Simulating the Inter-Cloud' (SimIC) is a discrete event simulation toolkit based on the process oriented simulation package of SimJava. The SimIC aims of replicating an inter-cloud facility wherein multiple clouds collaborate with each other for distributing service requests with regards to the desired simulation setup. The package encompasses the fundamental entities of the inter-cloud meta-scheduling algorithm such as users, meta-brokers, local-brokers, datacenters, hosts, hyper visors and virtual machines (VMs). Additionally, resource discovery and scheduling policies together with VMs allocation, re-scheduling and VM migration strategies are included as well. Using the SimIC a modeler can design a fully dynamic inter-cloud setting wherein collaboration is founded on meta-scheduling inspired characteristics of distributed resource managers that exchange user requirements as driven events in real-time simulations. The SimIC aims of achieving interoperability, flexibility and service elasticity while at the same time introducing the notion of heterogeneity of multiple clouds' configurations. In addition it accepts an optimization of a variety of selected performance criteria for a diversity of entities. The crucial factor of dynamics consideration has implemented by allowing reactive orchestration based on current workload of already executed heterogeneous user specifications. These are in the form of text files that the modeler can load in the toolkit and occurs in real-time at different simulation intervals. Finally, a unique request is scheduled for execution to an internal cloud datacenter host VM that is capable of performing the service contract. This is formally designed in Service Level Agreements (SLAs) based upon user profiling.

This work covers the inter-cloud meta-scheduling system that encompasses the essential components of the interoperable cloud setting for wide service dissemination. The study herein illustrates a set of distributed and decentralized operations by highlighting meta-computing characteristics. This is achieved by using meta-brokers that determine a middle-standing component for orchestrating the decision making process in order to select the most appropriate datacenter resource among collaborated clouds. The selection is based on heuristic performance criteria (e.g. the service execution time, latency, energy efficiency etc.). Our solution is more advanced when compared to conventional centralized schemes, as it offers robust real-time scalable, elastic and flexible service scheduling in a fully decentralized and dynamic manner. Similarly, issues related with bottleneck on multiple service requests, heterogeneity, information exposition and consideration of variation of workloads are of prime focus. In view of that, the whole process is based upon random service requests from users that are clients of a sub-cloud of an inter-cloud datacenter and access is done via a meta-broker. The inter-cloud facility distributes the request for service by enclosing each personalized service into a host virtual machine. The study presents a detailed discussion of the algorithmic model for demonstrating the whole service dissemination, allocation, execution and monitoring process along with the preliminary implementation and configuration on a proposed SimIC simulation framework.

The emergency of cloud computing and Generic Enablers (GEs) as the building blocks of Future Internet (FI) applications highlights new requirements in the area of cloud services. Though, due to the current restrictions of various certification standards related with privacy and safety of health related data, the utilization of cloud computing in such area has been in many instances unlawful. Here, we focus on demonstrating a “software to data” provisioning solution to propose a mapping of FI application use case requirements to software specifications (using GEs). The aim is to establish a provider to consumer cloud setting wherein no sensitive data will be exchanged but it will reside at the back-end site. We propose a prototype architecture that covers the cloud management layer and the operational features that manage data and Internet of Things devices. To show a real life scenario, we present the use case of the diabetes care and a FI application that includes various GEs.

Highly dynamic environments like clouds by nature cause a high degree of unpredictability of resource utilization and performance. Failures, latencies and heterogeneity should always be the main concern for affecting the scheduling decisions in distributed infrastructures. As a result, the scheduling efficiency of jobs before their submission is very difficult to be achieved or either forecasted. Even in the cases of the most complex schedulers a comprehensive dynamic view cannot always be predicted. Thus, the rescheduling concept takes advantage of the current scheduling status and performs a dynamic scheduling decision. In this paper we present a discussion of the virtual machine migration strategies that are currently available in distributed systems based on the need of migrating virtualized resources in order to achieve better resource utilization and performance such as improve load balancing, makespan and higher throughput of jobs. We conclude our study with a critical discussion of vital requirements for virtual machine migration.

Over the last decades, the cooperation amongst different resources that belong to various environments has been arisen as one of the most important research topic. This is mainly because of the different requirements, in terms of jobs' preferences that have been posed by different resource providers as the most efficient way to coordinate large scale settings like grids and clouds. However, the commonality of the complexity of the architectures (e.g. in heterogeneity issues) and the targets that each paradigm aims to achieve (e.g. flexibility) remains the same. This is to efficiently orchestrate resources and user demands in a distributed computing fashion by bridging the gap among local and remote participants. At a first glance, this is directly related with the scheduling concept, which is one of the most important issues for designing a cooperative resource management system, especially in large scale settings. In addition, the term meta-computing, hence meta-scheduling, offers additional functionalities in the area of interoperable resource management because of its great proficiency to handle sudden variations and dynamic situations in user demands by bridging the gap among local and remote participants. This work presents a review on scheduling in high performance, grid and cloud computing infrastructures. We conclude by analysing most important characteristics towards inter-cooperated infrastructures.

Monitoring process offer a quantitative and qualitative measurement of performance by collecting information relevant to environment and applications. Monitoring allows the obtaining of valuable parameters about performance, resource usage and availability, the efficiency of scheduling and used algorithms and represents a mechanism for analyzing and adapting an application's behavior, particularly useful for optimization of complex applications. Self-* properties of different applications are the answer to the complexity and large scale of distributed systems. The purpose of this paper is to analyze the requirements and to build such a tool destined for computational grids using the Grid Gain middleware platform (an Enterprise middleware for Grids, dedicated both to researcher environments and to industry). The optimization process is very important for QoS assurance, so multi-criteria approach could be adopted. The self-* behavior consider bio-inspired techniques for optimization (genetic algorithms, immune algorithms, swarm intelligence).

Cloud computing provides an efficient and flexible means for various services to meet the diverse and escalating needs of IT end-users. It offers novel functionalities including the utilization of remote services in addition to the virtualization technology. The latter feature offers an efficient method to harness the cloud power by fragmenting a cloud physical host in small manageable virtual portions. As a norm, the virtualized parts are generated by the cloud provider administrator through the hyper visor software based on a generic need for various services. However, several obstacles arise from this generalized and static approach. In this paper, we study and propose a model for instantiating dynamically virtual machines in relation to the current job characteristics. Following, we simulate a virtualized cloud environment in order to evaluate the model's dynamic-ness by measuring the correlation of virtual machines to hosts for certain job variations. This will allow us to compute the expected average execution time of various virtual machines instantiations per job length.

Resource provision within critical friend environment take place on a demand fashion and it is based on the aptitude of members to look across multiple locations for resource discovery and allocation. A major concern of such large scale and uncertain topology setting is the capability of members (nodes) to efficiently search and locate neighbouring participants. Here we adopt a Peer to Peer (P2P) approach in which every node in the network acts alike and discovers resources in a distributed coordination. More specifically, we propose the use of Chord algorithms, as a distributed peer to peer lookup protocol, put forward a new solution by assigning keys to different nodes. By specifying the aspect in which keys (data) are assigned to nodes, and how a node can determine the value for a given key, the algorithm locates the node responsible for that key. In the work herein we discuss a notable case, namely how Chord algorithms as a distributed P2P lookup protocol may determine provable solutions to the problem of efficient large scale Grid and Cloud resource localization. More specifically, the aforementioned proposal deals with the load balancing, scalability and availability of Grid and Cloud resources in a decentralized manner.

In this paper we are focusing on the minimum requirements to be addressed in order to demonstrate a inter-node communication within a Virtual Organisation (VO) using the method of Self-led Critical Friends (SCF). The method is able to decide paths that a node can choose in order to locate neighbouring nodes by aiming at realizing the overhead of each communication. The weight of each path will be measured by the analysis of prerequisites in order to achieve the interaction between nodes. We define requirements as the least fundamentals that a node needs to achieve in order to determine its accessibility factor. The information gathered from an interaction is then stored in a snapshot, a profile that is made available during the discovery stage.