Resource Prediction Engine

This paper proposes a network architecture that utilizes novel resource prediction models for optimal selection of multimedia content provision methods. The proposed research approach is based on a prototype system, which exploits a resource prediction engine, utilizing time series and epidemic spread models, for optimal and balanced distribution of the streaming data among content delivery networks, cloud-based providers and home media gateways. The proposed epidemic diseases models adopt the characteristics of the multimedia content delivery over the network architecture. In this context, the paper aims to present the advantages of using such models, by presenting and analysing an epidemic spread scheme for Video on Demand (VoD) delivery, to predict future epidemic spread behavior. In addition, the paper presents two algorithms, adopted in the prototype network architecture, for optimal selection of multimedia content delivery methods, as well as balanced delivery load, by exploiting the resource prediction engine. Both algorithms and models are evaluated to establish their efficiency, towards effectively predicting future network traffic demands. The simulation results verify the validity of the proposed approach, identifying fields for future research and experimentation.

This paper proposes a network architecture that goes a step beyond the current state-of-the-art, by elaborating on a novel resource reservation and provision scheme, through a Media Distribution Middleware (MDM), that is based on a Resource Prediction Engine (RPE) leading to both high resource utilization and quality guarantees. The architecture defines management planes and software components that provide the mechanisms for collecting monitoring data, predicting possible future values of the network metrics and resources usage, and applying management decisions to keep the provision optimal. The proposed research approach is based on novel time series and epidemic spread models, and the outcome is used for the optimal distribution of streaming data, among Content Delivery Networks, cloud-based providers and Home Media Gateways. The proposed epidemic diseases model adopts the characteristics of the multimedia content delivery over the network architecture. In this context, the paper aims to present the advantages of using such models, by presenting and analyzing an epidemic spread scheme for Video on Demand (VoD) delivery, to predict future epidemic spread behavior. The validity of the proposed system is verified through several sets of extended experimental simulation tests, carried out under controlled simulation conditions.

This paper proposes two algorithms adopted in a prototype network architecture, for optimal selection of multimedia content delivery methods, as well as balanced delivery load, by exploiting a novel resource prediction engine. The proposed architecture exploits both algorithms for the prediction of future multimedia services demands, by providing the ability to keep optimal the distribution of the streaming data, among Content Delivery Networks, cloud-based providers and Home Media Gateways. In addition, the prediction of the upcoming fluctuations of the network, provides the ability to the proposed network architecture, achieving optimized Quality of Service (QoS) and Quality of Experience (QoE) for the end users. Both algorithms were evaluated to establish their efficiency, towards effectively predicting future network traffic demands. The experimental results validated their performance and indicated fields for further research and experimentation.

Centralized e-Learning technology has dominated the learning ecosystem that brings a lot of potential usage on media rich learning materials. However, the centralized architecture has their own constraint to support large number of users for accessing large size of learning contents. On the other hand, Content Delivery Network (CDN) solution which relies on distributed architecture provides an alternative solution to eliminate bottleneck access. Although CDN is an effective solution, the implementation of technology is expensive and has less impact for student who lives in limited or non-existence internet access in geographical area. In this paper, we introduce an IoT smart device to provide e-Learning access for content sharing on hybrid cloud environment with distributed peer-topeer communication solution for data synchronization and updates. The IoT smart device acts as an intermediate device between user and cloud services, and provides content sharing solution without fully depending on the cloud server.

Motivated by increased concern over energy consumption in modern data centers, we propose a new, distributed computing platform called Nano Data Centers (NaDa). NaDa uses ISP-controlled home gateways to provide computing and storage services and adopts a managed peer-to-peer model to form a distributed data center infrastructure. To evaluate the potential for energy savings in NaDa platform we pick Video-on-Demand (VoD) services. We develop an energy consumption model for VoD in traditional and in NaDa data centers and evaluate this model using a large set of empirical VoD access data. We find that even under the most pessimistic scenarios, NaDa saves at least 20% to 30% of the energy compared to traditional data centers. These savings stem from energypreserving properties inherent to NaDa such as the reuse of already committed baseline power on underutilized gateways, the avoidance of cooling costs, and the reduction of network energy consumption as a result of demand and service co-localization in NaDa.

—This paper proposes a network architecture that utilizes novel resource prediction models for optimal selection of multimedia content provision methods. The proposed research approach is based on a prototype system, which exploits a resource prediction engine (RPE), utilizing time series and epidemic spread models, for optimal and balanced distribution of the streaming data among content delivery networks, cloud-based providers and home media gateways. The proposed epidemic diseases models adopt the characteristics of the multimedia content delivery over the network architecture. In this context, this paper aims to present the advantages of using such models, by presenting and analyzing an epidemic spread scheme for video-on-demand (VoD) delivery, to predict future epidemic spread behavior. In addition, this paper presents two algorithms, adopted in the prototype network architecture, for optimal selection of multimedia content delivery methods, as well as balanced delivery load, by exploiting the RPE. Both algorithms and models are evaluated to establish their efficiency, toward effectively predicting future network traffic demands. The simulation results verify the validity of the proposed approach, identifying fields for future research and experimentation. Index Terms—Content delivery networks (CDNs), epidemic spread models, media distribution middleware (MDM), multime-dia services systems, network architectures, quality of experience (QoE), resource prediction engine (RPE).

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.

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.

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.

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.

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.

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.