Streaming Support for Data Intensive Cloud-Based Sequence Analysis

Cloud computing provides a promising solution to the genomics data deluge problem resulting from the advent of next-generation sequencing (NGS) technology. Based on the concepts of “resources-on-demand” and “pay-as-you-go”, scientists with no or limited infrastructure can have access to scalable and cost-effective computational resources. However, the large size of NGS data causes a significant data transfer latency from the client’s site to the cloud, which presents a bottleneck for using cloud computing services. In this paper, we provide a streaming-based scheme to overcome this problem, where the NGS data is processed while being transferred to the cloud. Our scheme targets the wide class of NGS data analysis tasks, where the NGS sequences can be processed independently from one another. We also provide the elastream package that supports the use of this scheme with individual analysis programs or with workflow systems. Experiments presented in this paper show that our solution mitigates the effect of data transfer latency and saves both time and cost of computation.

A Case for CDN-as-a-Service in the Cloud: A Mobile Cloud Networking Argument

Content Distribution Networks are mandatory components of modern web architectures, with plenty of vendors offering their services. Despite its maturity, new paradigms and architecture models are still being developed in this area. Cloud Computing, on the other hand, is a more recent concept which has expanded extremely quickly, with new services being regularly added to cloud management software suites such as OpenStack. The main contribution of this paper is the architecture and the development of an open source CDN that can be provisioned in an on-demand, pay-as-you-go model thereby enabling the CDN as a Service paradigm. We describe our experience with integration of CDNaaS framework in a cloud environment, as a service for enterprise users. We emphasize the flexibility and elasticity of such a model, with each CDN instance being delivered on-demand and associated to personalized caching policies as well as an optimized choice of Points of Presence based on exact requirements of an enterprise customer. Our development is based on the framework developed in the Mobile Cloud Networking EU FP7 project, which offers its enterprise users a common framework to instantiate and control services. CDNaaS is one of the core support components in this project as is tasked to deliver different type of multimedia content to several thousands of users geographically distributed. It integrates seamlessly in the MCN service life-cycle and as such enjoys all benefits of a common design environment, allowing for an improved interoperability with the rest of the services within the MCN ecosystem.

Cloud-based Orchestration of Multimedia Services and Applications

The Mobile Cloud Networking project develops among others, several virtualized services and applications, in particular: (1) IP Multimedia Subsystem as a Service that gives the possibility to deploy a virtualized and on-demand instance of the IP Multimedia Subsystem platform, (2) Digital Signage Service as a Service that is based on a re-designed Digital Signage Service architecture, adopting the cloud computing principles, and (3) Information Centric Networking/Content Delivery Network as a Service that is used for distributing, caching and migrating content from other services. Possible designs for these virtualized services and applications have been identified and are being implemented. In particular, the architectures of the mentioned services were specified, adopting cloud computing principles, such as infrastructure sharing, elasticity, on-demand and pay-as-you-go. The benefits of Reactive Programming paradigm are presented in the context of Interactive Cloudified Digital Signage services in a Mobile Cloud Platform, as well as the benefit of interworking between different Mobile Cloud Networking Services as Digital Signage Service and Content Delivery Network Service for better performance of Video on Demand content deliver. Finally, the management of Service Level Agreements and the support of rating, charging and billing has also been considered and defined.

Demo -- Closer to Cloud-RAN: RAN as a Service

Commoditization and virtualization of wireless networks are changing the economics of mobile networks to help network providers (e.g., MNO, MVNO) move from proprietary and bespoke hardware and software platforms toward an open, cost-effective, and flexible cellular ecosystem. In addition, rich and innovative local services can be efficiently created through cloudification by leveraging the existing infrastructure. In this work, we present RANaaS, which is a cloudified radio access network delivered as a service. RANaaS provides the service life-cycle of an ondemand, elastic, and pay as you go 3GPP RAN instantiated on top of the cloud infrastructure. We demonstrate an example of realtime cloudified LTE network deployment using the OpenAirInterface LTE implementation and OpenStack running on commodity hardware as well as the flexibility and performance of the platform developed.

As entidades fechadas de previdência complementar enquanto instrumentos de atuação do Estado na economia

Com as transformações ocorridas nas últimas décadas do século XX, notadamente a expansão financeira pela qual passou o capitalismo, o enfraquecimento fiscal dos Estados nacionais e o questionamento aos sistemas de previdência pública por repartição, ganham importância em todo o mundo os fundos de pensão. Estes fundos, ao lado de outros investidores institucionais, como seguradoras e fundos de investimentos, passam a cumprir papel central no mercado acionário e também no mercado de títulos públicos e privados. Com o objetivo de realizar lucros para pagar benefícios de aposentadoria para os seus participantes, os fundos de pensão arrecadam e concentram poupança privada pulverizada, transformando-a em um ativo poderoso. No Brasil, as Entidades Fechadas de Previdência Complementar nomenclatura jurídica dos fundos de pensão possuem um total de 702 bilhões de reais em ativos, que se concentram nas três maiores entidades do país: Previ, Petros e Funcef. Em comum, estes três fundos têm o fato de serem patrocinados por empresas estatais, o que, pela legislação vigente, dá ao Poder Executivo a competência de indicar metade de seus dirigentes, incluindo o seu presidente que possui voto de desempate. O presente trabalho pesquisou o papel que estas três EFPCs cumprem enquanto instrumento de atuação do Estado no domínio econômico, especialmente para o provimento de fundos para o desenvolvimento. Para isso, primeiramente, o estudo explora o movimento de expansão financeira do capitalismo e a crise no padrão de desenvolvimento brasileiro. Depois, investiga de maneira sistemática o arcabouço jurídico que regula os fundos de pensão; e, por fim, analisa a alocação dos seus investimentos e o perfil dos seus dirigentes.

Municipal research : to promote the application of scientific principles to government /

Nos. 1-56, July 26, 1913-Aug. 15, 1914, were issued weekly in the form of leaflets; no. 57-92, Jan. 1915-Dec. 1917, monthly, in the form of pamphlets, containing studies in government; no. 93-95, irregularly issued.

Health investment, saving, and public policy

This paper develops an overlapping-generations model in which agents invest in health to prolong life in both working and retirement periods. It explores how unfunded social security with or without health subsidies affects life expectancy, economic growth, and welfare. In particular, by extending life at a possible cost of capital accumulation, health subsidies and a pay-as-you-go pension can improve welfare, especially in the short run.

A quick guide to surviving redundancy:your essential legal guide

What do you do if your boss calls you in to talk about job cuts? What are your rights? What are his or her rights? Do you know the procedures that should be followed? If redundancy looms, or you just want to be prepared for the worst,you need to know where you stand.Author Kathy Daniels is well placed to help. She writes and lectures extensively on human resources and employee rights, and as a member of the Employment Tribunal she regularly comes face-to-face with redundancy claims. In this easy-to-understand guide she answers all the important questions on redundancy and its aftermath, including: How are staff selected for redundancy? What is voluntary redundancy? Are full-time,part-time and agency staff treated differently? What is the consultation process bosses must adhere to? How much redundancy pay can you expect? How do you take a claim to the Employment Tribunal? As well as covering all the legal dos and don'ts, helpful guidance is provided on: Budgets and personal finances after redundancy Benefits you may be able to claim Coping with stress and strain Finding a new job or changing career The Quick Guide to Surviving Redundancy is full of real-life case studies and top tips on your employment rights. It also includes template letters for a range of redundancy situations.

Specification and quantitative analysis of probabilistic cloud deployment patterns

Cloud computing is a new technological paradigm offering computing infrastructure, software and platforms as a pay-as-you-go, subscription-based service. Many potential customers of cloud services require essential cost assessments to be undertaken before transitioning to the cloud. Current assessment techniques are imprecise as they rely on simplified specifications of resource requirements that fail to account for probabilistic variations in usage. In this paper, we address these problems and propose a new probabilistic pattern modelling (PPM) approach to cloud costing and resource usage verification. Our approach is based on a concise expression of probabilistic resource usage patterns translated to Markov decision processes (MDPs). Key costing and usage queries are identified and expressed in a probabilistic variant of temporal logic and calculated to a high degree of precision using quantitative verification techniques. The PPM cost assessment approach has been implemented as a Java library and validated with a case study and scalability experiments. © 2012 Springer-Verlag Berlin Heidelberg.

Demográfiai hatások és implicit hozamok kapcsolata a nyugdíjrendszerekben = The relationship of demographic effects and implicit returns in pension systems

E tanulmány központi témája a nyugdíjrendszerek implicit hozama. Az együtt élő nemzedékek figyelembevételével felépülő nyugdíjmodellekben kétféle implicit hozamot különböztetünk meg. A hosszmetszeti implicit hozamot valamely adott nemzedékhez tartozó, különböző években esedékes pénzáramlások alapján, a keresztmetszeti implicit hozamot pedig több, különböző nemzedék adott évben jellemző pénzáramlásai alapján számíthatjuk ki. A hosszmetszeti és keresztmetszeti implicit hozamok értékeit és a közük lévő összefüggéseket a tőkefedezeti, a névleges egyéni számlás és a hagyományos felosztó-kirovó nyugdíjrendszerek egyszerű elméleti modelljeiben hasonlítjuk össze. A számításokhoz használt modellkeret fontos eleme a várható élettartam figyelembevétele. Az eredmények azt mutatják, hogy a maximális és a várható élettartam eltérésekor a hosszmetszeti és a keresztmetszeti implicit hozamok közötti összefüggések még egyszerű elméleti modellben is meglehetősen összetettek lehetnek. ____ The focus of this study is on the implicit returns of pension systems. Two types are analysed using an overlapping generations model: the calculation of longitudinal\" return is based on cash flows in different years belonging to a given generation, while cross-section\" implicit return is calculated in a given year with cash flows of multiple generations. Values and relationships of longitudinal and cross-section implicit returns are compared in simple theoretical models of fully funded\", notional defined-contribution\" and traditional pay-as-you-go\" pension systems. An important element of the theoretical model is the inclusion of an assumption about life expectancy. Model results point to the complexity of the relation between longitudinal and cross-section implicit returns, if expected and maximum life expectancy differ. The study maps and introduces these complex relationships.

Quantitative comparison of SensibleThings and Microsoft Azure IoT Hub

With the development of the Internet-of-Things, more and more IoT platforms come up with different structures and characteristics. Making balance of their advantages and disadvantages, we should choose the suitable platform in differ- ent scenarios. For this project, I make comparison of a cloud-based centralized platform, Microsoft Azure IoT hub and a fully distributed platform, Sensi- bleThings. Quantitative comparison is made for performance by 2 scenarios, messages sending speed adds up, devices lie in different location. General com- parison is made for security, utilization and the storage. Finally I draw the con- clusion that SensibleThings performs more stable when a lot of messages push- es to the platform. Microsoft Azure has better geographic expansion. For gener- al comparison, Microsoft Azure IoT hub has better security. The requirement of local device for Microsoft Azure IoT hub is lower than SensibleThings. The SensibleThings are open source and free while Microsoft Azure follow the con- cept “pay as you go” with many throttling limitations for different editions. Microsoft is more user-friendly.

BUILDING EFFICIENT AND COST-EFFECTIVE CLOUD-BASED BIG DATA MANAGEMENT SYSTEMS

In today’s big data world, data is being produced in massive volumes, at great velocity and from a variety of different sources such as mobile devices, sensors, a plethora of small devices hooked to the internet (Internet of Things), social networks, communication networks and many others. Interactive querying and large-scale analytics are being increasingly used to derive value out of this big data. A large portion of this data is being stored and processed in the Cloud due the several advantages provided by the Cloud such as scalability, elasticity, availability, low cost of ownership and the overall economies of scale. There is thus, a growing need for large-scale cloud-based data management systems that can support real-time ingest, storage and processing of large volumes of heterogeneous data. However, in the pay-as-you-go Cloud environment, the cost of analytics can grow linearly with the time and resources required. Reducing the cost of data analytics in the Cloud thus remains a primary challenge. In my dissertation research, I have focused on building efficient and cost-effective cloud-based data management systems for different application domains that are predominant in cloud computing environments. In the first part of my dissertation, I address the problem of reducing the cost of transactional workloads on relational databases to support database-as-a-service in the Cloud. The primary challenges in supporting such workloads include choosing how to partition the data across a large number of machines, minimizing the number of distributed transactions, providing high data availability, and tolerating failures gracefully. I have designed, built and evaluated SWORD, an end-to-end scalable online transaction processing system, that utilizes workload-aware data placement and replication to minimize the number of distributed transactions that incorporates a suite of novel techniques to significantly reduce the overheads incurred both during the initial placement of data, and during query execution at runtime. In the second part of my dissertation, I focus on sampling-based progressive analytics as a means to reduce the cost of data analytics in the relational domain. Sampling has been traditionally used by data scientists to get progressive answers to complex analytical tasks over large volumes of data. Typically, this involves manually extracting samples of increasing data size (progressive samples) for exploratory querying. This provides the data scientists with user control, repeatable semantics, and result provenance. However, such solutions result in tedious workflows that preclude the reuse of work across samples. On the other hand, existing approximate query processing systems report early results, but do not offer the above benefits for complex ad-hoc queries. I propose a new progressive data-parallel computation framework, NOW!, that provides support for progressive analytics over big data. In particular, NOW! enables progressive relational (SQL) query support in the Cloud using unique progress semantics that allow efficient and deterministic query processing over samples providing meaningful early results and provenance to data scientists. NOW! enables the provision of early results using significantly fewer resources thereby enabling a substantial reduction in the cost incurred during such analytics. Finally, I propose NSCALE, a system for efficient and cost-effective complex analytics on large-scale graph-structured data in the Cloud. The system is based on the key observation that a wide range of complex analysis tasks over graph data require processing and reasoning about a large number of multi-hop neighborhoods or subgraphs in the graph; examples include ego network analysis, motif counting in biological networks, finding social circles in social networks, personalized recommendations, link prediction, etc. These tasks are not well served by existing vertex-centric graph processing frameworks whose computation and execution models limit the user program to directly access the state of a single vertex, resulting in high execution overheads. Further, the lack of support for extracting the relevant portions of the graph that are of interest to an analysis task and loading it onto distributed memory leads to poor scalability. NSCALE allows users to write programs at the level of neighborhoods or subgraphs rather than at the level of vertices, and to declaratively specify the subgraphs of interest. It enables the efficient distributed execution of these neighborhood-centric complex analysis tasks over largescale graphs, while minimizing resource consumption and communication cost, thereby substantially reducing the overall cost of graph data analytics in the Cloud. The results of our extensive experimental evaluation of these prototypes with several real-world data sets and applications validate the effectiveness of our techniques which provide orders-of-magnitude reductions in the overheads of distributed data querying and analysis in the Cloud.

Un framework per l'analisi di big data con elevata eterogeneità all'interno di multistore

I big data sono caratterizzati dalle ben note 4v: volume, velocità, veracità e varietà. Quest'ultima risulta di importanza critica nei sistemi schema-less, dove il concetto di schema non è rigido. In questo contesto rientrano i database NoSQL, i quali offrono modelli dati diversi dal classico modello dati relazionale, ovvero: documentale, wide-column, grafo e key-value. Si parla di multistore quando ci si riferisce all'uso di database con modelli dati diversi che vengono esposti con un'unica interfaccia di interrogazione, sia per sfruttare caratteristiche di un modello dati che per le maggiori performance dei database NoSQL in contesti distribuiti. Fare analisi sui dati all'interno di un multistore risulta molto più complesso: i dati devono essere integrati e va ripristinata la consistenza. A questo scopo nasce la necessità di approcci più soft, chiamati pay-as-you-go: l'integrazione è leggera e incrementale, aggira la complessità degli approcci di integrazione tradizionali e restituisce risposte best-effort o approssimative. Seguendo tale filosofia, nasce il concetto di dataspace come rappresentazione logica e di alto livello dei dataset disponibili. Obiettivo di questo lavoro tesi è studiare, progettare e realizzare una modalità di interrogazione delle sorgenti dati eterogenee in contesto multistore con l'intento di fare analisi situazionali, considerando le problematiche di varietà e appoggiandosi all'integrazione fornita dal dataspace. Lo scopo finale è di sviluppare un prototipo che esponga un'interfaccia per interrogare il dataspace con la semantica GPSJ, ovvero la classe di query più comune nelle applicazioni OLAP. Un'interrogazione nel dataspace dovrà essere tradotta in una serie di interrogazioni nelle sorgenti e, attraverso un livello middleware, i risultati parziali dovranno essere integrati tra loro in modo che il risultato dell'interrogazione sia corretto e allo stesso tempo completo.

It's what you say not what you pay

We study manager-employee interactions in experiments set in a corporate environment where payoffs depend on employees coordinating at high effort levels; the underlying game being played repeatedly by employees is a weak-link game. In the absence of managerial intervention subjects invariably slip into coordination failure. To overcome a history of coordination failure, managers have two instruments at their disposal, increasing employees' financial incentives to coordinate and communication with employees. We find that communication is a more effective tool than incentive changes for leading organizations out of performance traps. Examining the content of managers' communication, the most effective messages specifically request a high effort, point out the mutual benefits of high effort, and imply that employees are being paid well.