Georgios Skourletopoulos

Resource usage prediction for optimal and balanced provision of multimedia services

This paper proposes a novel network architecture for optimal and balanced provision of multimedia services, exploiting a resource prediction system. This architecture enables for the long-term prediction of multimedia services future demands, based on the history of previous network resources usage. The proposed research approach provides the opportunity for the optimal distribution of streaming data, among Content Delivery Networks, cloud-based providers and Home Media Gateways. The short-term prediction that is performed, enables for making the proper decisions by the system, according to specific network metrics, towards achieving higher Quality of Service and Quality of Experience for the end users. The validity of the proposed system is verified through several sets of extended experimental simulation tests, carried out under controlled simulation conditions.

A novel methodology for efficient throughput evaluation in virtualized routers

This paper analyzes a novel methodology for calculating the throughput in a device, which hosts multiple virtualized network interconnect devices (i.e. virtual routers). The proposed methodology, which extends the well-known procedure (for non-virtualized IP routers) adopted from RFC 2544, considers the impact of heterogeneity of the offered load at the level of virtual routers. The utility of this methodology is demonstrated, analyzing the throughput of virtualized routers by four different virtualization platforms that use two different techniques, which are the paravirtualization (Xen and Citrix Xen) and the OS-level virtualization (Linux Containers and Jails). The results indicate that the virtualization platforms behave differently to distribution of traffic load among virtual routers. Finally, the need for the proposed methodology is motivated by performing extensive throughput tests on the aforementioned platforms at different work points of the network device (i.e. different offered traffic load distribution between virtual routers).

An evaluation of cloud-based mobile services with limited capacity: a linear approach

Mobile computing is pervading networks at an increasing speed as mobile devices are used with diverse forms of wireless technologies to access data. This paper evaluates different cloud-supported mobile services subject to limited capacity, as the selection of a service may introduce additional costs, such as those that derive from the additional amount of memory required for processing. In this context, a novel linear model and algorithm in the mobile cloud computing environment are proposed from the service capacity perspective, considering the cost that derives from the unused capacity. The probability of overutilization or underutilization of the selected service is also researched, once a linear growth in the number of users occurs. To further make effective and strategic investment decisions when selecting the appropriate cloud-based mobile service to lease off, the model formulation is based on a cost–benefit appraisal. The proposed quantification approach is evaluated with respect to four different case scenarios, exploiting a web tool that has been developed as a proof of concept and implementing the algorithm to calculate and compare the benefits and costs in the mobile cloud-based service level.

Predicting and quantifying the technical debt in cloud software engineering

Identifying and managing effectively the Technical Debt has become an issue of great importance over recent years. In cloud marketplaces, where the cloud services can be leased, the difficulty to promptly predict and manage the Technical Debt has a significant impact. In this paper, we examine the Technical Debt, which stems from budget constraints during the software development process as well as the capacity of a cloud service. In this context, the budget and the cloud service selection decisions may introduce Technical Debt. Towards reaching a conclusion, two approaches are taken into consideration. Initially, a cost estimation approach is researched, which is related to implementing Software as a Service (SaaS) in the cloud for three scenarios aiming to predict the incurrence of the Technical Debt in the future. The Constructive Cost Model (COCOMO) is exploited, in order to estimate the implementation cost and define a range of secureness. In addition, a Technical Debt quantification approach is adopted, which is associated with leasing a cloud Software as a Service (SaaS), towards indicating the most appropriate cloud service to be selected.

Performance analysis of reactive routing protocols in Mobile Ad hoc Networks

Mobile Ad hoc Networks (MANETs) are self-organized infrastructure-less networks, including wireless mobile systems, which have been thoroughly studied and widely used in a variety of applications. Routing mechanisms in MANETs are considered as a major research challenge due to the nature of the network topology, where the nodes are able to move in any direction in the network showing unpredictable behavior. Nodes failures and absence of centralized coordinators are also important challenges in such network configurations. In addition, the routing protocols that are usually exploited in MANETs are characterized as proactive (Table-driven) and reactive (On-demand). In this context, this paper elaborates on a preliminary comparison study among different reactive protocols, such as the Ad hoc On-demand Distance Vector (AODV) and the Dynamic Source Routing (DSR). This comparison was conducted towards investigating the optimum exploitation of such routing protocols in MANETs, in terms of their performance against the number of the nodes in the network.

A Fluctuation-Based Modelling Approach to Quantification of the Technical Debt on Mobile Cloud-Based Service Level

Enterprise mobility has become a top technology priority for companies over recent years and many organizations are accelerating the adoption of mobile cloud application models. The mobile cloud can be considered as a marketplace, where the mobile services of the mobile cloud-based system architectures can be leased off via the cloud. In this context, this paper elaborates on a novel fluctuation-based quantification model, which is based on a cost-benefit appraisal, adopting a non- linear and asymmetric approach. The proposed model aims to predict the incurrence and the risk of entering into a new technical debt (TD) in the future and provide insights to inform effective investment decision making. The lease of a cloud- based mobile service was considered, when developing the formula, and the research approach is investigated with respect to the cost that derives from the unused capacity. The probability of overutilization or underutilization of the selected service is examined, as fluctuations in the number of users are forecasted. A quantification tool has been also developed as a proof of concept, implementing the proposed model and intending to quantify and evaluate the technical debt on mobile cloud-based service level, when fluctuations in the demand occur.

Virtual Network Functions exploitation through a prototype resource management framework

This paper elaborates on the design of a prototype framework for the exploitation of virtual Network Functions (NFs) and virtual Network Resources (NRs) in a cloud-based network architecture. The proposed framework is based on a Marketplace approach, utilizing a centralized network architecture, where the exploitation of the available NFs/NRs is managed by a Brokerage Module. This Brokerage Module applies trading policies based on a resource predictive entity that monitors the resources, towards matching the end-user demands and providing the acceptable bandwidth required.

Quantifying and evaluating the technical debt on mobile cloud-based service level

As network bandwidth and coverage continue to increase, the adoption rates of mobile devices are growing over time and the mobile technology is becoming increasingly industrialized. In mobile cloud marketplaces, the cloud-supported mobile services can be leased off. However, the mobile service selection may introduce technical debt (TD), which is essential to be predicted and quantified. In this context, this paper examines the incurrence of technical debt in the future when leasing cloud-based mobile services by proposing a novel quantitative model, which adopts a linear and symmetric approach as a linear growth in the number of users is predicted. The formulation of the problem is based on a cost-benefit analysis, elaborating on the potential profit that could be obtained if the number of users would be equal to the maximum value. The probability of overutilization of the selected service in the long run is also researched. Finally, a quantification tool has been developed as a proof of concept (PoC), which initiates the technical debt analysis and optimization on mobile cloud-based service level and aims to provide insights into the overutilization or underutilization of a web service when a linear increase in the number of users occurs.

Big Data and Cloud Computing: A Survey of the State-of-the-Art and Research Challenges

The proliferation of data warehouses and the rise of multimedia, social media and the Internet of Things (IoT) generate an increasing volume of structured, semi-structured and unstructured data. Towards the investigation of these large volumes of data, big data and data analytics have become emerging research fields, attracting the attention of the academia, industry and governments. Researchers, entrepreneurs, decision makers and problem solvers view ‘big data’ as the tool to revolutionize various industries and sectors, such as business, healthcare, retail, research, education and public administration. In this context, this survey chapter presents a review of the current big data research, exploring applications, opportunities and challenges, as well as the state-of-the-art techniques and underlying models that exploit cloud computing technologies, such as the big data-as-a-service (BDaaS) or analytics-as-a-service (AaaS).

Towards the evaluation of a big data-as-a-service model: A decision theoretic approach

The rise of large data centers has created new business models, where businesses can lease storage and computing capacity and pay only for the storage they actually use, rather than making the large capital investments needed to construct and provision large-scale computer installations. In this context, investments in big-data computing are rapidly gaining ground, having extraordinary near-term and long-term benefits. The mobile cloud can be considered as a marketplace, where the storage and computing capabilities of the mobile cloud-based system architectures can be leased off. However, cloud storage is not less expensive, only that it incurs operating rather than capital expenses. This paper elaborates on a novel cost analysis model, adopting a non-linear and asymmetric approach. The proposed modelling aims to evaluate the adoption of a big data-as-a-service business model against the traditional high-performance data warehouse appliances that exist in the market in order to inform effective and strategic decision making. The lease of cloud storage is investigated, when developing the mathematical formulas, and the research approach is examined with respect to the cost that derives from the unused storage. Possible upgradation of the storage and the risk of entering into new and accumulated costs in the future are also considered in this study. A quantification tool has been also developed as a proof of concept (PoC), implementing the proposed quantitative model and intending to shed light on the adoption of big data-as-a-service business models.