A survey on cost-effective context-aware distribution of social data streams over energy-efficient data centres

Social media have emerged in the last decade as a viable and ubiquitous means of communication. The ease of user content generation within these platforms, e.g. check-in information, multimedia data, etc., along with the proliferation of Global Positioning System (GPS)-enabled, always-connected capture devices lead to data streams of unprecedented amount and a radical change in information sharing. Social data streams raise a variety of practical challenges, including derivation of real-time meaningful insights from effectively gathered social information, as well as a paradigm shift for content distribution with the leverage of contextual data associated with user preferences, geographical characteristics and devices in general. In this article we present a comprehensive survey that outlines the state-of-the-art situation and organizes challenges concerning social media streams and the infrastructure of the data centres supporting the efficient access to data streams in terms of content distribution, data diffusion, data replication, energy efficiency and network infrastructure. We systematize the existing literature and proceed to identify and analyse the main research points and industrial efforts in the area as far as modelling, simulation and performance evaluation are concerned

Strategic and operational services for workload management in the cloud

In hosting environments such as Infrastructure as a Service (IaaS) clouds, desirable application performance is typically guaranteed through the use of Service Level Agreements (SLAs), which specify minimal fractions of resource capacities that must be allocated by a service provider for unencumbered use by customers to ensure proper operation of their workloads. Most IaaS offerings are presented to customers as fixed-size and fixed-price SLAs, that do not match well the needs of specific applications. Furthermore, arbitrary colocation of applications with different SLAs may result in inefficient utilization of hosts' resources, resulting in economically undesirable customer behavior. In this thesis, we propose the design and architecture of a Colocation as a Service (CaaS) framework: a set of strategic and operational services that allow the efficient colocation of customer workloads. CaaS strategic services provide customers the means to specify their application workload using an SLA language that provides them the opportunity and incentive to take advantage of any tolerances they may have regarding the scheduling of their workloads. CaaS operational services provide the information necessary for, and carry out the reconfigurations mandated by strategic services. We recognize that it could be the case that there are multiple, yet functionally equivalent ways to express an SLA. Thus, towards that end, we present a service that allows the provably-safe transformation of SLAs from one form to another for the purpose of achieving more efficient colocation. Our CaaS framework could be incorporated into an IaaS offering by providers or it could be implemented as a value added proposition by IaaS resellers. To establish the practicality of such offerings, we present a prototype implementation of our proposed CaaS framework

Edge Intelligence Simulator:a platform for simulating intelligent edge orchestration solutions

Abstract. To support the stringent requirements of the future intelligent and interactive applications, intelligence needs to become an essential part of the resource management in the edge environment. Developing intelligent orchestration solutions is a challenging and arduous task, where the evaluation and comparison of the proposed solution is a focal point. Simulation is commonly used to evaluate and compare proposed solutions. However, there does not currently exist openly available simulators that would have a specific focus on supporting the research on intelligent edge orchestration methods. This thesis presents a simulation platform called Edge Intelligence Simulator (EISim), the purpose of which is to facilitate the research on intelligent edge orchestration solutions. In its current form, the platform supports simulating deep reinforcement learning based solutions and different orchestration control topologies in scenarios related to task offloading and resource pricing on edge. The platform also includes additional tools for creating simulation environments, running simulations for agent training and evaluation, and plotting results. This thesis gives a comprehensive overview of the state of the art in edge and fog simulation, orchestration, offloading, and resource pricing, which provides a basis for the design of EISim. The methods and tools that form the foundation of the current EISim implementation are also presented, along with a detailed description of the EISim architecture, default implementations, use, and additional tools. Finally, EISim with its default implementations is validated and evaluated through a large-scale simulation study with 24 simulation scenarios. The results of the simulation study verify the end-to-end performance of EISim and show its capability to produce sensible results. The results also illustrate how EISim can help the researcher in controlling and monitoring the training of intelligent agents, as well as in evaluating solutions against different control topologies.Reunaälysimulaattori : alusta älykkäiden reunalaskennan orkestrointiratkaisujen simulointiin. Tiivistelmä. Älykkäiden ratkaisujen täytyy tulla olennaiseksi osaksi reunaympäristön resurssien hallinnointia, jotta tulevaisuuden vuorovaikutteisten ja älykkäiden sovellusten suoritusta voidaan tukea tasolla, joka täyttää sovellusten tiukat suoritusvaatimukset. Älykkäiden orkestrointiratkaisujen kehitys on vaativa ja työläs prosessi, jonka keskiöön kuuluu olennaisesti menetelmien testaaminen ja vertailu muita menetelmiä vasten. Simulointia käytetään tyypillisesti menetelmien arviointiin ja vertailuun, mutta tällä hetkellä ei ole avoimesti saatavilla simulaattoreita, jotka eritoten keskittyisivät tukemaan älykkäiden reunaorkestrointiratkaisujen kehitystä. Tässä opinnäytetyössä esitellään simulaatioalusta nimeltään Edge Intelligence Simulator (EISim; Reunaälysimulaattori), jonka tarkoitus on helpottaa älykkäiden reunaorkestrointiratkaisujen tutkimusta. Nykymuodossaan se tukee vahvistusoppimispohjaisten ratkaisujen sekä erityyppisten orkestroinnin kontrollitopologioiden simulointia skenaarioissa, jotka liittyvät laskennan siirtoon ja resurssien hinnoitteluun reunaympäristössä. Alustan mukana tulee myös lisätyökaluja, joita voi käyttää simulaatioympäristöjen luomiseen, simulaatioiden ajamiseen agenttien koulutusta ja arviointia varten, sekä simulaatiotulosten visualisoimiseen. Tämä opinnäytetyö sisältää kattavan katsauksen reunaympäristön simuloinnin, reunaorkestroinnin, laskennan siirron ja resurssien hinnoittelun nykytilaan kirjallisuudessa, mikä tarjoaa kunnollisen lähtökohdan EISimin toteutukselle. Opinnäytetyö esittelee menetelmät ja työkalut, joihin EISimin tämänhetkinen toteutus perustuu, sekä antaa yksityiskohtaisen kuvauksen EISimin arkkitehtuurista, oletustoteutuksista, käytöstä ja lisätyökaluista. EISimin validointia ja arviointia varten esitellään laaja simulaatiotutkimus, jossa EISimin oletustoteutuksia simuloidaan 24 simulaatioskenaariossa. Simulaatiotutkimuksen tulokset todentavat EISimin kokonaisvaltaisen toimintakyvyn, sekä osoittavat EISimin kyvyn tuottaa järkeviä tuloksia. Tulokset myös havainnollistavat, miten EISim voi auttaa tutkijoita älykkäiden agenttien koulutuksessa ja ratkaisujen arvioinnissa eri kontrollitopologioita vasten

Enabling Scalable and Sustainable Softwarized 5G Environments

The fifth generation of telecommunication systems (5G) is foreseen to play a fundamental role in our socio-economic growth by supporting various and radically new vertical applications (such as Industry 4.0, eHealth, Smart Cities/Electrical Grids, to name a few), as a one-fits-all technology that is enabled by emerging softwarization solutions \u2013 specifically, the Fog, Multi-access Edge Computing (MEC), Network Functions Virtualization (NFV) and Software-Defined Networking (SDN) paradigms. Notwithstanding the notable potential of the aforementioned technologies, a number of open issues still need to be addressed to ensure their complete rollout. This thesis is particularly developed towards addressing the scalability and sustainability issues in softwarized 5G environments through contributions in three research axes: a) Infrastructure Modeling and Analytics, b) Network Slicing and Mobility Management, and c) Network/Services Management and Control. The main contributions include a model-based analytics approach for real-time workload profiling and estimation of network key performance indicators (KPIs) in NFV infrastructures (NFVIs), as well as a SDN-based multi-clustering approach to scale geo-distributed virtual tenant networks (VTNs) and to support seamless user/service mobility; building on these, solutions to the problems of resource consolidation, service migration, and load balancing are also developed in the context of 5G. All in all, this generally entails the adoption of Stochastic Models, Mathematical Programming, Queueing Theory, Graph Theory and Team Theory principles, in the context of Green Networking, NFV and SDN