PIASA: A power and interference aware resource management strategy for heterogeneous workloads in cloud data centers

Cloud data centers have been progressively adopted in different scenarios, as reflected in the execution of heterogeneous applications with diverse workloads and diverse quality of service (QoS) requirements. Virtual machine (VM) technology eases resource management in physical servers and helps cloud providers achieve goals such as optimization of energy consumption. However, the performance of an application running inside a VM is not guaranteed due to the interference among co-hosted workloads sharing the same physical resources. Moreover, the different types of co-hosted applications with diverse QoS requirements as well as the dynamic behavior of the cloud makes efficient provisioning of resources even more difficult and a challenging problem in cloud data centers. In this paper, we address the problem of resource allocation within a data center that runs different types of application workloads, particularly CPU- and network-intensive applications. To address these challenges, we propose an interference- and power-aware management mechanism that combines a performance deviation estimator and a scheduling algorithm to guide the resource allocation in virtualized environments. We conduct simulations by injecting synthetic workloads whose characteristics follow the last version of the Google Cloud tracelogs. The results indicate that our performance-enforcing strategy is able to fulfill contracted SLAs of real-world environments while reducing energy costs by as much as 21%.

Migrating legacy applications to cloud: case TOAS

Cloud computing, despite its success and promises, presents issues for businesses mi­grating their legacy applications to cloud. In this research legacy-to-cloud migration issues are reviewed based on literature findings and an experience report. Solutions are applied to Tieto Open Application Suite (TOAS) software development platform running on cloud infrastructure. It is observed that the migration strategy heavily affects the migration approach. For TOAS a strategy of redesigning the applications for cloud is suggested. Common migration-driven application level modifications in­clude adaptation to service-oriented architecture, load balancing, and runtime and technology changes. A cloud platform such as TOAS might introduce additional needs. Decision making on migration strategy is found to be an issue to be solved case by case. Use of assistive decision making tools is suggested.

Orquestração de migração massiva de máquinas virtuais baseada em análise cíclica para ambientes de computação na nuvem.

Um das principais características da tecnologia de virtualização é a Live Migration, que permite que máquinas virtuais sejam movimentadas entre máquinas físicas sem a interrupção da execução. Esta característica habilita a implementação de políticas mais sofisticadas dentro de um ambiente de computação na nuvem, como a otimização de uso de energia elétrica e recursos computacionais. Entretanto, a Live Migration pode impor severa degradação de desempenho nas aplicações das máquinas virtuais e causar diversos impactos na infraestrutura dos provedores de serviço, como congestionamento de rede e máquinas virtuais co-existentes nas máquinas físicas. Diferente de diversos estudos, este estudo considera a carga de trabalho da máquina virtual um importante fator e argumenta que escolhendo o momento adequado para a migração da máquina virtual pode-se reduzir as penalidades impostas pela Live Migration. Este trabalho introduz a Application-aware Live Migration (ALMA), que intercepta as submissões de Live Migration e, baseado na carga de trabalho da aplicação, adia a migração para um momento mais favorável. Os experimentos conduzidos neste trabalho mostraram que a arquitetura reduziu em até 74% o tempo das migrações para os experimentos com benchmarks e em até 67% os experimentos com carga de trabalho real. A transferência de dados causada pela Live Migration foi reduzida em até 62%. Além disso, o presente introduz um modelo que faz a predição do custo da Live Migration para a carga de trabalho e também um algoritmo de migração que não é sensível à utilização de memória da máquina virtual.

Protecting user privacy in the Cloud: an analysis of terms of service

We present the results of a study that collected, compared and analyzed the terms and conditions of a number of cloud services vis-a-vis privacy and data protection. First, we assembled a list of factors that comprehensively capture cloud companies' treatment of user data with regard to privacy and data protection; then, we assessed how various cloud services of different types protect their users in the collection, retention, and use of their data, as well as in the disclosure to law enforcement authorities. This commentary provides comparative and aggregate analysis of the results.

LocLess: Do You Really Care Where Your Cloud Files Are?

Physical location of data in cloud storage is a problem that gains a lot of attention not only from the actual cloud providers but also from the end users' who lately raise many concerns regarding the privacy of their data. It is a common practice that cloud service providers create replicate users' data across multiple physical locations. However, moving data in different countries means that basically the access rights are transferred based on the local laws of the corresponding country. In other words, when a cloud service provider stores users' data in a different country then the transferred data is subject to the data protection laws of the country where the servers are located. In this paper, we propose LocLess, a protocol which is based on a symmetric searchable encryption scheme for protecting users' data from unauthorized access even if the data is transferred to different locations. The idea behind LocLess is that "Once data is placed on the cloud in an unencrypted form or encrypted with a key that is known to the cloud service provider, data privacy becomes an illusion". Hence, the proposed solution is solely based on encrypting data with a key that is only known to the data owner.

Retrievals of thick cloud optical depth from the Geoscience Laser Altimeter System (GLAS) by calibration of solar background signal

Laser beams emitted from the Geoscience Laser Altimeter System (GLAS), as well as other spaceborne laser instruments, can only penetrate clouds to a limit of a few optical depths. As a result, only optical depths of thinner clouds (< about 3 for GLAS) are retrieved from the reflected lidar signal. This paper presents a comprehensive study of possible retrievals of optical depth of thick clouds using solar background light and treating GLAS as a solar radiometer. To do so one must first calibrate the reflected solar radiation received by the photon-counting detectors of the GLAS 532-nm channel, the primary channel for atmospheric products. Solar background radiation is regarded as a noise to be subtracted in the retrieval process of the lidar products. However, once calibrated, it becomes a signal that can be used in studying the properties of optically thick clouds. In this paper, three calibration methods are presented: (i) calibration with coincident airborne and GLAS observations, (ii) calibration with coincident Geostationary Opera- tional Environmental Satellite (GOES) and GLAS observations of deep convective clouds, and (iii) cali- bration from first principles using optical depth of thin water clouds over ocean retrieved by GLAS active remote sensing. Results from the three methods agree well with each other. Cloud optical depth (COD) is retrieved from the calibrated solar background signal using a one-channel retrieval. Comparison with COD retrieved from GOES during GLAS overpasses shows that the average difference between the two retriev- als is 24%. As an example, the COD values retrieved from GLAS solar background are illustrated for a marine stratocumulus cloud field that is too thick to be penetrated by the GLAS laser. Based on this study, optical depths for thick clouds will be provided as a supplementary product to the existing operational GLAS cloud products in future GLAS data releases.

Managing server energy and reducing operational cost for online service providers

The past decade has seen the energy consumption in servers and Internet Data Centers (IDCs) skyrocket. A recent survey estimated that the worldwide spending on servers and cooling have risen to above $30 billion and is likely to exceed spending on the new server hardware . The rapid rise in energy consumption has posted a serious threat to both energy resources and the environment, which makes green computing not only worthwhile but also necessary. This dissertation intends to tackle the challenges of both reducing the energy consumption of server systems and by reducing the cost for Online Service Providers (OSPs). Two distinct subsystems account for most of IDC’s power: the server system, which accounts for 56% of the total power consumption of an IDC, and the cooling and humidifcation systems, which accounts for about 30% of the total power consumption. The server system dominates the energy consumption of an IDC, and its power draw can vary drastically with data center utilization. In this dissertation, we propose three models to achieve energy effciency in web server clusters: an energy proportional model, an optimal server allocation and frequency adjustment strategy, and a constrained Markov model. The proposed models have combined Dynamic Voltage/Frequency Scaling (DV/FS) and Vary-On, Vary-off (VOVF) mechanisms that work together for more energy savings. Meanwhile, corresponding strategies are proposed to deal with the transition overheads. We further extend server energy management to the IDC’s costs management, helping the OSPs to conserve, manage their own electricity cost, and lower the carbon emissions. We have developed an optimal energy-aware load dispatching strategy that periodically maps more requests to the locations with lower electricity prices. A carbon emission limit is placed, and the volatility of the carbon offset market is also considered. Two energy effcient strategies are applied to the server system and the cooling system respectively. With the rapid development of cloud services, we also carry out research to reduce the server energy in cloud computing environments. In this work, we propose a new live virtual machine (VM) placement scheme that can effectively map VMs to Physical Machines (PMs) with substantial energy savings in a heterogeneous server cluster. A VM/PM mapping probability matrix is constructed, in which each VM request is assigned with a probability running on PMs. The VM/PM mapping probability matrix takes into account resource limitations, VM operation overheads, server reliability as well as energy effciency. The evolution of Internet Data Centers and the increasing demands of web services raise great challenges to improve the energy effciency of IDCs. We also express several potential areas for future research in each chapter.

Memory resource balancing for virtualized computing

Virtualization has become a common abstraction layer in modern data centers. By multiplexing hardware resources into multiple virtual machines (VMs) and thus enabling several operating systems to run on the same physical platform simultaneously, it can effectively reduce power consumption and building size or improve security by isolating VMs. In a virtualized system, memory resource management plays a critical role in achieving high resource utilization and performance. Insufficient memory allocation to a VM will degrade its performance dramatically. On the contrary, over-allocation causes waste of memory resources. Meanwhile, a VM’s memory demand may vary significantly. As a result, effective memory resource management calls for a dynamic memory balancer, which, ideally, can adjust memory allocation in a timely manner for each VM based on their current memory demand and thus achieve the best memory utilization and the optimal overall performance. In order to estimate the memory demand of each VM and to arbitrate possible memory resource contention, a widely proposed approach is to construct an LRU-based miss ratio curve (MRC), which provides not only the current working set size (WSS) but also the correlation between performance and the target memory allocation size. Unfortunately, the cost of constructing an MRC is nontrivial. In this dissertation, we first present a low overhead LRU-based memory demand tracking scheme, which includes three orthogonal optimizations: AVL-based LRU organization, dynamic hot set sizing and intermittent memory tracking. Our evaluation results show that, for the whole SPEC CPU 2006 benchmark suite, after applying the three optimizing techniques, the mean overhead of MRC construction is lowered from 173% to only 2%. Based on current WSS, we then predict its trend in the near future and take different strategies for different prediction results. When there is a sufficient amount of physical memory on the host, it locally balances its memory resource for the VMs. Once the local memory resource is insufficient and the memory pressure is predicted to sustain for a sufficiently long time, a relatively expensive solution, VM live migration, is used to move one or more VMs from the hot host to other host(s). Finally, for transient memory pressure, a remote cache is used to alleviate the temporary performance penalty. Our experimental results show that this design achieves 49% center-wide speedup.

Leveraging heterogeneity for energy minimization in data centers

Energy consumption in data centers is nowadays a critical objective because of its dramatic environmental and economic impact. Over the last years, several approaches have been proposed to tackle the energy/cost optimization problem, but most of them have failed on providing an analytical model to target both the static and dynamic optimization domains for complex heterogeneous data centers. This paper proposes and solves an optimization problem for the energy-driven configuration of a heterogeneous data center. It also advances in the proposition of a new mechanism for task allocation and distribution of workload. The combination of both approaches outperforms previous published results in the field of energy minimization in heterogeneous data centers and scopes a promising area of research.

A cyber-physical approach to combined HW-SW monitoring for improving energy efficiency in data centers

High-Performance Computing, Cloud computing and next-generation applications such e-Health or Smart Cities have dramatically increased the computational demand of Data Centers. The huge energy consumption, increasing levels of CO2 and the economic costs of these facilities represent a challenge for industry and researchers alike. Recent research trends propose the usage of holistic optimization techniques to jointly minimize Data Center computational and cooling costs from a multilevel perspective. This paper presents an analysis on the parameters needed to integrate the Data Center in a holistic optimization framework and leverages the usage of Cyber-Physical systems to gather workload, server and environmental data via software techniques and by deploying a non-intrusive Wireless Sensor Net- work (WSN). This solution tackles data sampling, retrieval and storage from a reconfigurable perspective, reducing the amount of data generated for optimization by a 68% without information loss, doubling the lifetime of the WSN nodes and allowing runtime energy minimization techniques in a real scenario.

Design and implementation of an adaptive dashboard and visualization interface for the dynamic optimization and control of Data Centers

Over the last few years, the Data Center market has increased exponentially and this tendency continues today. As a direct consequence of this trend, the industry is pushing the development and implementation of different new technologies that would improve the energy consumption efficiency of data centers. An adaptive dashboard would allow the user to monitor the most important parameters of a data center in real time. For that reason, monitoring companies work with IoT big data filtering tools and cloud computing systems to handle the amounts of data obtained from the sensors placed in a data center.Analyzing the market trends in this field we can affirm that the study of predictive algorithms has become an essential area for competitive IT companies. Complex algorithms are used to forecast risk situations based on historical data and warn the user in case of danger. Considering that several different users will interact with this dashboard from IT experts or maintenance staff to accounting managers, it is vital to personalize it automatically. Following that line of though, the dashboard should only show relevant metrics to the user in different formats like overlapped maps or representative graphs among others. These maps will show all the information needed in a visual and easy-to-evaluate way. To sum up, this dashboard will allow the user to visualize and control a wide range of variables. Monitoring essential factors such as average temperature, gradients or hotspots as well as energy and power consumption and savings by rack or building would allow the client to understand how his equipment is behaving, helping him to optimize the energy consumption and efficiency of the racks. It also would help him to prevent possible damages in the equipment with predictive high-tech algorithms.

Global 2009 1km MODIS (MOD35/MOD09) cloud frequency and MOD35 processing path

Identifying cloud interference in satellite-derived data is a critical step toward developing useful remotely sensed products. Most MODIS land products use a combination of the MODIS (MOD35) cloud mask and the 'internal' cloud mask of the surface reflectance product (MOD09) to mask clouds, but there has been little discussion of how these masks differ globally. We calculated global mean cloud frequency for both products, for 2009, and found that inflated proportions of observations were flagged as cloudy in the Collection 5 MOD35 product. These erroneously categorized areas were spatially and environmentally non-random and usually occurred over high-albedo land-cover types (such as grassland and savanna) in several regions around the world. Additionally, we found that spatial variability in the processing path applied in the Collection 5 MOD35 algorithm affects the likelihood of a cloudy observation by up to 20% in some areas. These factors result in abrupt transitions in recorded cloud frequency across landcover and processing-path boundaries impeding their use for fine-scale spatially contiguous modeling applications. We show that together, these artifacts have resulted in significantly decreased and spatially biased data availability for Collection 5 MOD35-derived composite MODIS land products such as land surface temperature (MOD11) and net primary productivity (MOD17). Finally, we compare our results to mean cloud frequency in the new Collection 6 MOD35 product, and find that landcover artifacts have been reduced but not eliminated. Collection 6 thus increases data availability for some regions and land cover types in MOD35-derived products but practitioners need to consider how the remaining artifacts might affect their analysis.

Challenges and Opportunities in Edge Computing

Many cloud-based applications employ a data centre as a central server to process data that is generated by edge devices, such as smartphones, tablets and wearables. This model places ever increasing demands on communication and computational infrastructure with inevitable adverse effect on Quality-of-Service and Experience. The concept of Edge Computing is predicated on moving some of this computational load towards the edge of the network to harness computational capabilities that are currently untapped in edge nodes, such as base stations, routers and switches. This position paper considers the challenges and opportunities that arise out of this new direction in the computing landscape.

A performance analysis of mesh models for cloud-based workflows

Nell'ambito della loro trasformazione digitale, molte organizzazioni stanno adottando nuove tecnologie per supportare lo sviluppo, l'implementazione e la gestione delle proprie architetture basate su microservizi negli ambienti cloud e tra i fornitori di cloud. In questo scenario, le service ed event mesh stanno emergendo come livelli infrastrutturali dinamici e configurabili che facilitano interazioni complesse e la gestione di applicazioni basate su microservizi e servizi cloud. L’obiettivo di questo lavoro è quello di analizzare soluzioni mesh open-source (istio, Linkerd, Apache EventMesh) dal punto di vista delle prestazioni, quando usate per gestire la comunicazione tra applicazioni a workflow basate su microservizi all’interno dell’ambiente cloud. A questo scopo è stato realizzato un sistema per eseguire il dislocamento di ognuno dei componenti all’interno di un cluster singolo e in un ambiente multi-cluster. La raccolta delle metriche e la loro sintesi è stata realizzata con un sistema personalizzato, compatibile con il formato dei dati di Prometheus. I test ci hanno permesso di valutare le prestazioni di ogni componente insieme alla sua efficacia. In generale, mentre si è potuta accertare la maturità delle implementazioni di service mesh testate, la soluzione di event mesh da noi usata è apparsa come una tecnologia ancora non matura, a causa di numerosi problemi di funzionamento.

O Futuro dos Centros de Dados

Atualmente, as Tecnologias de Informação (TI) são cada vez mais vitais dentro das organizações. As TI são o motor de suporte do negócio. Para grande parte das organizações, o funcionamento e desenvolvimento das TI têm como base infraestruturas dedicadas (internas ou externas) denominadas por Centro de Dados (CD). Nestas infraestruturas estão concentrados os equipamentos de processamento e armazenamento de dados de uma organização, por isso, são e serão cada vez mais desafiadas relativamente a diversos fatores tais como a escalabilidade, disponibilidade, tolerância à falha, desempenho, recursos disponíveis ou disponibilizados, segurança, eficiência energética e inevitavelmente os custos associados. Com o aparecimento das tecnologias baseadas em computação em nuvem e virtualização, abrese todo um leque de novas formas de endereçar os desafios anteriormente descritos. Perante este novo paradigma, surgem novas oportunidades de consolidação dos CD que podem representar novos desafios para os gestores de CD. Por isso, é no mínimo irrealista para as organizações simplesmente eliminarem os CD ou transforma-los segundo os mais altos padrões de qualidade. As organizações devem otimizar os seus CD, contudo um projeto eficiente desta natureza, com capacidade para suportar as necessidades impostas pelo mercado, necessidades dos negócios e a velocidade da evolução tecnológica, exigem soluções complexas e dispendiosas tanto para a sua implementação como a sua gestão. É neste âmbito que surge o presente trabalho. Com o objetivo de estudar os CD inicia-se um estudo sobre esta temática, onde é detalhado o seu conceito, evolução histórica, a sua topologia, arquitetura e normas existentes que regem os mesmos. Posteriormente o estudo detalha algumas das principais tendências condicionadoras do futuro dos CD. Explorando o conhecimento teórico resultante do estudo anterior, desenvolve-se uma metodologia de avaliação dos CD baseado em critérios de decisão. O estudo culmina com uma análise sobre uma nova solução tecnológica e a avaliação de três possíveis cenários de implementação: a primeira baseada na manutenção do atual CD; a segunda baseada na implementação da nova solução em outro CD em regime de hosting externo; e finalmente a terceira baseada numa implementação em regime de IaaS.