Architecture Strategies for Cyber-Foraging: Preliminary Results from a Systematic Literature Review

Mobile devices have become for many the preferred way of interacting with the Internet, social media and the enterprise. However, mobile devices still do not have the computing power and battery life that will allow them to perform effectively over long periods of time or for executing applications that require extensive communication or computation, or low latency. Cyber-foraging is a technique to enable mobile devices to extend their computing power and storage by offloading computation or data to more powerful servers located in the cloud or in single-hop proximity. This paper presents the preliminary results of a systematic literature review (SLR) on architectures that support cyber-foraging. The preliminary results show that this is an area with many opportunities for research that will enable cyber-foraging solutions to become widely adopted as a way to support the mobile applications of the present and the future

CloudMedia: When cloud on demand meets video on demand

Internet-based cloud computing is a new computing paradigm aiming to provide agile and scalable resource access in a utility-like fashion. Other than being an ideal platform for computation-intensive tasks, clouds are believed to be also suitable to support large-scale applications with periods of flash crowds by providing elastic amounts of bandwidth and other resources on the fly. The fundamental question is how to configure the cloud utility to meet the highly dynamic demands of such applications at a modest cost. In this paper, we address this practical issue with solid theoretical analysis and efficient algorithm design using Video on Demand (VoD) as the example application. Having intensive bandwidth and storage demands in real time, VoD applications are purportedly ideal candidates to be supported on a cloud platform, where the on-demand resource supply of the cloud meets the dynamic demands of the VoD applications. We introduce a queueing network based model to characterize the viewing behaviors of users in a multichannel VoD application, and derive the server capacities needed to support smooth playback in the channels for two popular streaming models: client-server and P2P. We then propose a dynamic cloud resource provisioning algorithm which, using the derived capacities and instantaneous network statistics as inputs, can effectively support VoD streaming with low cloud utilization cost. Our analysis and algorithm design are verified and extensively evaluated using large-scale experiments under dynamic realistic settings on a home-built cloud platform. © 2011 IEEE.published_or_final_versionThe 31st International Conference on Distributed Computing Systems (ICDCS 2011), Minneapolis, MN., 20-24 June 2011. In Proceedings of 31st ICDCS, 2011, p. 268-27

Energy Efficiency through Virtual Machine Redistribution in Telecommunication Infrastructure Nodes

Energy efficiency is one of the key factors impacting the green behavior and operational expenses of telecommunication core network operations. This thesis study is aimed for finding out possible technique to reduce energy consumption in telecommunication infrastructure nodes. The study concentrates on traffic management operation (e.g. media stream control, ATM adaptation) within network processors [LeJ03], categorized as control plane. The control plane of the telecommunication infrastructure node is a custom built high performance cluster which consists of multiple GPPs (General Purpose Processor) interconnected by high-speed and low-latency network. Due to application configurations in particular GPP unit and redundancy issues, energy usage is not optimal. In this thesis, our approach is to gain elastic capacity within the control plane cluster to reduce power consumption. This scales down and wakes up certain GPP units depending on traffic load situations. For elasticity, our study moves toward the virtual machine (VM) migration technique in the control plane cluster through system virtualization. The traffic load situation triggers VM migration on demand. Virtual machine live migration brings the benefit of enhanced performance and resiliency of the control plane cluster. We compare the state-of-the-art power aware computing resource scheduling in cluster-based nodes with VM migration technique. Our research does not propose any change in data plane architecture as we are mainly concentrating on the control plane. This study shows, VM migration can be an efficient approach to significantly reduce energy consumption in control plane of cluster-based telecommunication infrastructure nodes without interrupting performance/throughput, while guaranteeing full connectivity and maximum link utilization

Addressing Application Latency Requirements through Edge Scheduling

Abstract Latency-sensitive and data-intensive applications, such as IoT or mobile services, are leveraged by Edge computing, which extends the cloud ecosystem with distributed computational resources in proximity to data providers and consumers. This brings significant benefits in terms of lower latency and higher bandwidth. However, by definition, edge computing has limited resources with respect to cloud counterparts; thus, there exists a trade-off between proximity to users and resource utilization. Moreover, service availability is a significant concern at the edge of the network, where extensive support systems as in cloud data centers are not usually present. To overcome these limitations, we propose a score-based edge service scheduling algorithm that evaluates network, compute, and reliability capabilities of edge nodes. The algorithm outputs the maximum scoring mapping between resources and services with regard to four critical aspects of service quality. Our simulation-based experiments on live video streaming services demonstrate significant improvements in both network delay and service time. Moreover, we compare edge computing with cloud computing and content delivery networks within the context of latency-sensitive and data-intensive applications. The results suggest that our edge-based scheduling algorithm is a viable solution for high service quality and responsiveness in deploying such applications

Traffic analysis of Internet user behavior and content demand patterns

El estudio del trafico de internet es relevante para poder mejorar la calidad de servicio de los usuarios. Ser capaz de conocer cuales son los servicios más populares y las horas con más usuarios activos permite identificar la cantidad de tráfico producido y, por lo tanto, diseñar una red capaz de soportar la actividad esperada. La implementación de una red considerando este conocimiento puede reducir el tiempo de espera considerablemente, mejorando la experiencia de los usuarios en la web. Ya existen análisis del trafico de los usuarios y de sus patrones de demanda. Pero, los datos utilizados en estos estudios no han sido renovados, por lo tanto los resultados obtenidos pueden estar obsoletos y se han podido producir cambios importantes. En esta tesis, se estudia la cantidad de trafico entrante y saliente producido por diferentes aplicaciones y se ha hecho una evolución teniendo en cuenta datos presentes y pasados. Esto nos permitirá entender los cambios producidos desde 2007 hasta 2015 y observar las tendencias actuales. Además, se han analizado los patrones de demanda de usuarios del inicio de 2016 y se han comparado con resultados previos. La evolución del tráfico demuestra cambios en las preferencias de los usuarios, a pesar de que los patrones de demanda siguen siendo los mismos que en años anteriores. Los resultados obtenidos en esta tesis confirman las predicciones sobre un aumento del tráfico de 'Streaming Media'; se ha comprobado que el tráfico de 'Streaming Media' es el tráfico total dominante, con Netflix como el mayor contribuidor.L'estudi del trànsit d'Internet és rellevant per a poder millor la qualitat de servei dels usuaris. Ser capaç de conèixer quins són els serveis més popular i les hores amb més usuaris actius permet identificar la quantitat de trànsit produït i, per tant, dissenyar una xarxa capaç de soportar la activitat esperada. L'implementació d'una xarxa considerant aquest coneixement pot reduir el temps d'espera considerablement, millorant l'experiència dels usuaris a la web. Ja existeixen anàlisis del transit dels usuaris i els seus patrons de demanda. Però, les dades utilitzades en aquests estudis no han sigut renovades, per tant els resultats obtinguts poden estar obsolets i s'han produït canvis importants. En aquesta tesis, s'estudia la quantitat de transit entrant i sortint produit per diferents aplicacions i s'ha fet una evolució, tenint en compte dades presents i passades. Això ens permetrà entendre els canvis produïts des de 2007 fins 2015 i observar les tendències actuals. A més, s'han analitzat els patrons de demanda de usuaris de principis de 2016 i s'han comparat amb resultats previs. L'evolució del trànsit mostra canvis en las preferències dels usuaris, en canvi els patrons de demanda continuen sent els mateixos que en anys posteriors. Els resultats obtinguts en aquesta tesis confirmen les prediccions sobre un augment del trànsit de 'Streaming Media'; s'ha comprovat que el trànsit de 'Streaming Media' es el trànsit total dominant, amb Netflix com el major contribuïdor.The study of Internet traffic is relevant in order to improve the quality of service of users. Being able to know which are the most popular services and the hours with most active users can let us identify the amount of inbound and outbound traffic produced, and hence design a network able to support the activity expected. The implementation of a network considering that knowledge can reduce the waiting time of users considerably, improving the users’ experience in the web. Analysis of users’ traffic and user demand patterns already exist. However, the data used in these studies is not renewed, thus the results found can be obsolete and considerable changes would have happened. In this bachelor’s thesis, it is studied the amount of inbound and outbound traffic produced considering different applications and the evolution when regarding previous and actual data has been taken into account. This would let us understand the changes produced from 2007 to 2015 and observe the tendencies nowadays. In addition, it has been analyzed the user demand patterns in the beginning of 2016 and it has been contrasted with previous results. The evolution of traffic has shown changes in users’ preferences, although their demand patterns are still the same as previous years. The results found in this thesis confirmed the expectations about an increase of streaming media Internet traffic; it was proved that streaming media traffic is the dominant total traffic, with Netflix as the major contributor

Dynamic Content-based Indexing in Mobile edge Networks

Recently, we have seen a huge growth in the usage of mobile devices, and with this growth, the data generated has also increased, being in a huge scale, user generated, e.g, photos, books, texts or messages/e-mails. Usually this data requires a permanent storage and its respective indexing for users to efficiently access it however, due to the unpredictability of this data, a concern regarding its indexing starts to raise as it can be hard to predict labels and indexes capable of representing every possible set of data. For instance, during a birthday party, users may want to share photos and videos of this event which can be seen as uploading streams of data to a content sharing system. This content stream will most likely have no index, unless it is explicitly generated, making its retrieval difficult. However, when clustering this stream, as data keeps increasing, we might, somewhere in the future, be capable of detecting similarities between each photo (e.g. a guest’s face) and might want to index them. Indices can directly impact a system’s performance however, there is a drawback from having either too many or too few indices, posing a challenge when it comes to evolving content. We propose Chives, a Content-Based Indexing framework, built on top of a content sharing publish/subscribe system at the edge named Thyme, where we evaluate unsupervised learning in data stream techniques to generate indices. It also offers a content-based query to automatically subscribe to indices containing similar content, e.g images. After evaluating our proposal in a simulated environment, we can see that our framework offers a great abstraction, allowing an easy extension, furthermore our implementation can generate indices from data streams and the indexing follows a clustering criteria, generating the indices as conditions are met. Furthermore, results show that our clustering quality and consequently its generated indices rely strongly on the quality of the image discrimination and its ability to extract features representing its face. In Conclusion, more studies should be done regarding this framework as such, our solution is built in a way where we can exclusively study each component and upgrade it in future work.Recentemente, tem-se observado um enorme crescimento na adesão a dispositivos móveis e com este crescimento, tem também aumentado a quantidade de dados partilhados, sendo em grande escala, gerado pelos utilizadores, por exemplo, fotos, livros, textos ou até mensagens/e-mails. Normalmente estes dados necessitam de um local de armazenamento permanente e a sua respectiva indexação de modo a poderem ser acedidos de forma eficiente por parte dos utilizadores no entanto, dada a imprevisibilidade destes dados, pode surgir um problema relativamente à indexação dado que poderá ser difícil prever etiquetas e índices capazes de representar qualquer conjunto de dados. Por exemplo, durante uma festa de anos, utilizadores poderão partilhar fotografias e vídeos deste evento que poderá ser também interpretado como um upload de dados em stream para um sistema de partilha de conteúdo. Esta stream de dados, muito provavelmente não terá nenhum índice capaz de o descrever, tornando difícil a obtenção deste visto que não existe representação semântica desta. No entanto, ao agrupar esta stream, à medida que os dados vão crescendo, poderemos, algures no tempo ser capaz de detectar semelhanças entre cada fotografia (por exemplo. a cara de um convidado) e podemos querer indexar. Índices podem causar um impacto directo sobre o sistema, no entanto o inverso pode acontecer quando existe índices em défice ou em excesso, apresentando um desafio acerca de dados evolutivos. Nós propomos uma framework de indexação baseada em conteúdo, construído por cima de um sistema de partilha de conteúdo que usa um sistema de Publish/Subscribe na edge denominado Thyme, onde avaliamos técnicas de aprendizagem não supervisionada em data streams para gerar dinamicamente índices. Depois de avaliar a nossa framework, conseguimos concluir que esta oferece uma boa abstração, facilitando a sua extensão, para além disso a nossa proposta permite gerar índices quando as condições definidas para o clustering são respeitadas. Para além disso, os resultados demonstram que o clustering realizado pelo nosso algoritmo dependem fortemente da qualidade de discriminação de imagens e das características obtidas por este discriminador em relação às faces. Concluindo, mais estudos devem feitos em relação à framework, como tal esta foi construída de modo a permitir uma rápida e fácil extensão para futuros melhoramentos

System analysis of a Peer-to-Peer Video-on-Demand architecture : Kangaroo

Architectural design and deployment of Peer-to-Peer Video-on-Demand (P2PVoD) systems which support VCR functionalities is attracting the interest of an increasing number of research groups within the scientific community; especially due to the intrinsic characteristics of such systems and the benefits that peers could provide at reducing the server load. This work focuses on the performance analysis of a P2P-VoD system considering user behaviors obtained from real traces together with other synthetic user patterns. The experiments performed show that it is feasible to achieve a performance close to the best possible. Future work will consider monitoring the physical characteristics of the network in order to improve the design of different aspects of a VoD system.El disseny arquitectònic i el desplegament de sistemes de Vídeo sota Demanda "Peer-to-Peer" que soporten funcionalitats VCR està captant l'interès d'un nombre creixent de grups de recerca a la comunitat científica, degut especialment a les característiques intrínsiques dels mencionats sistemes i als beneficis que els peers podrien proporcionar a la reducció de la càrrega en el servidor. Aquest treball tracta l'anàlisi del rendiment d'un sistema P2P-VoD considerant el comportament d'usuaris obtingut amb traçes reals i amb patrons sintètics. Els experiments realitzats mostren que és viable assolir un rendiment proper al cas més óptim. Com treball futur es considerarà la monitorització de les característiques físiques de la xarxa per a poder millorar el disseny dels diferents aspectes que formen un sistema de VoD.El diseño arquitectónico y el despliegue de sistemas de Video bajo Demanda "Peer-to-Peer" que soportan funcionalidades VCR está captando el interés de un número creciente de grupos de investigación dentro de la comunidad científica; especialmente debido a las características intrínsecas de tales sistemas y a los beneficios que los peers podrían proporcionar en la reducción de la carga en el servidor. Este trabajo se enfoca en el análisis de rendimiento de un sistema P2PVoD considerando el comportamiento de usuarios obtenido de trazas reales, junto a otros patrones sintéticos. Los experimentos realizados muestran que es viable lograr un rendimiento cercano al caso más óptimo. El trabajo futuro considerará la monitorización de las características físicas de la red para poder mejorar el diseño de los diferentes aspectos que conforman un sistema de VoD

Quaestor: Query web caching for database-as-a-service providers

Today, web performance is primarily governed by round-trip latencies between end devices and cloud services. To improve performance, services need to minimize the delay of accessing data. In this paper, we propose a novel approach to low latency that relies on existing content delivery and web caching infrastructure. The main idea is to enable application-independent caching of query results and records with tunable consistency guarantees, in particular bounded staleness. Q uaestor (Query Store) employs two key concepts to incorporate both expiration-based and invalidation-based web caches: (1) an Expiring Bloom Filter data structure to indicate potentially stale data, and (2) statistically derived cache expiration times to maximize cache hit rates. Through a distributed query invalidation pipeline, changes to cached query results are detected in real-time. The proposed caching algorithms offer a new means for data-centric cloud services to trade latency against staleness bounds, e.g. in a database-as-a-service. Q uaestor is the core technology of the backend-as-a-service platform Baqend, a cloud service for low-latency websites. We provide empirical evidence for Q uaestor 's scalability and performance through both simulation and experiments. The results indicate that for read-heavy workloads, up to tenfold speed-ups can be achieved through Q uaestor 's caching. </jats:p