Assessing the overhead and scalability of system monitors for large data centers

Current data centers are shifting towards cloud-based architectures as a means to obtain a scalable, cost-effective, robust service platform. In spite of this, the underlying management infrastructure has grown in terms of hardware resources and software complexity, making automated resource monitoring a necessity.There are several infrastructure monitoring tools designed to scale to a very high number of physical nodes. However, these tools either collect performance measure at a low frequency (missing the chance to capture the dynamics of a short-term management task) or are simply not equipped with instrumentation specific to cloud computing and virtualization. In this scenario, monitoring the correctness and efficiency of live migrations can become a nightmare. This situation will only worsen in the future, with the increased service demand due to spreading of the user base.In this paper, we assess the scalability of a prototype monitoring subsystem for different user scenarios. We also identify all the major bottlenecks and give insight on how to remove them

Muistikeskeisen radioverkon vaikutus tietopääsyjen suoritusnopeuteen

Future 5G-based mobile networks will be largely defined by virtualized network functions (VNF). The related computing is being moved to cloud where a set of servers is provided to run all the software components of the VNFs. Such software component can be run on any server in the mobile network cloud infrastructure. The servers conventionally communicate via TCP/IP -network. To realize planned low-latency use cases in 5G, some servers are placed to data centers near the end users (edge clouds). Many of these use cases involve data accesses from one VNF to another, or to other network elements. The accesses are desired to take as little time as possible to stay within the stringent latency requirements of the new use cases. As a possible approach for reaching this, a novel memory-centric platform was studied. The main ideas of the memory-centric platform are to collapse the hierarchy between volatile and persistent memory by utilizing non-volatile memory (NVM) and use memory-semantic communication between computer components. In this work, a surrogate memory-centric platform was set up as a storage for VNFs and the latency of data accesses from VNF application was measured in different experiments. Measurements against a current platform showed that memory-centric platform was significantly faster to access than the current, TCP/IP using platform. Measurements for accessing RAM with different memory bandwidths within the memory-centric platform showed that the order of latency was roughly independent of the available memory bandwidth. These results mean that memory-centric platform is a promising alternative to be used as a storage system for edge clouds. However, more research is needed to study how other service qualities, such as low latency variation, are fulfilled in memory-centric platform in a VNF environment.Tulevaisuuden 5G:hen perustuvissa mobiiliverkoissa verkkolaitteisto on pääosin virtualisoitu. Tällaisen verkon virtuaaliverkkolaite (VNF) koostuu ohjelmistokomponenteista, joita ajetaan tarkoitukseen määrätyiltä mobiiliverkon pilven palvelimilta. Ohjelmistokomponentti voi olla ajossa millä vain mobiiliverkon näistä pilvi-infrastruktuurin palvelimista. Palvelimet on tavallisesti yhdistetty TCP/IP-verkolla. Jotta suunnitellut alhaisen viiveen käyttötapaukset voisivat toteutua 5G-verkoissa, pilvipalvelimia on sijoitettu niin kutsuttuihin reunadatakeskuksiin lähelle loppukäyttäjiä. Monet näistä käyttötapauksista sisältävät tietopääsyjä virtuaaliverkkolaitteesta toisiin tai muihin verkkoelementteihin. Tietopääsyviiveen halutaan olevan mahdollisimman pieni, jotta käyttötapausten tiukoissa viiverajoissa pysytään. Mahdollisena lähestymistapana tietopääsyviiveen minimoimiseen tutkittiin muistikeskeistä laitteistoalustaa. Tämän laitteistoalustan pääperiaatteita on korvata nykyiset lyhytkestoiset ja pysyvät muistit haihtumattomalla muistilla sekä kommunikoida muistisemanttisilla viesteillä tietokonekomponenttien kesken. Tässä työssä muistikeskeisyyttä hyödyntävää sijaislaitteistoa käytettiin VNF-datan varastona ja ohjelmistokomponenttien tietopääsyviivettä sinne mitattiin erilaisilla kokeilla. Kokeet osoittivat nykyisen, TCP/IP-pohjaisen alustan huomattavasti muistikeskeistä alustaa hitaammaksi. Toiseksi, kokeet osoittivat tietopääsyviiveiden olevan saman suuruisia muistikeskeisen alustan sisällä, riippumatta saatavilla olevasta muistikaistasta. Tulokset merkitsevät, että muistikeskeinen alusta on lupaava vaihtoehto reunadatakeskuksen tietovarastojärjestelmäksi. Lisää tutkimusta alustasta kuitenkin tarvitaan, jotta muiden palvelun laatukriteerien, kuten matalan viivevaihtelun, toteutumisesta saadaan tietoa

A Cloud-Oriented Green Computing Architecture for E-Learning Applications

Cloud computing is a highly scalable and cost-effective infrastructure for running Web applications. E-learning or e-Learning is one of such Web application has increasingly gained popularity in the recent years, as a comprehensive medium of global education system/training systems. The development of e-Learning Application within the cloud computing environment enables users to access diverse software applications, share data, collaborate more easily, and keep their data safely in the infrastructure. However, the growing demand of Cloud infrastructure has drastically increased the energy consumption of data centers, which has become a critical issue. High energy consumption not only translates to high operational cost, which reduces the profit margin of Cloud providers, but also leads to high carbon emissions which is not environmentally friendly. Hence, energy-efficient solutions are required to minimize the impact of Cloud-Oriented E-Learning on the environment. E-learning methods have drastically changed the educational environment and also reduced the use of papers and ultimately reduce the production of carbon footprint. E-learning methodology is an example of Green computing. Thus, in this paper, it is proposed a Cloud-Oriented Green Computing Architecture for eLearning Applications (COGALA). The e-Learning Applications using COGALA can lower expenses, reduce energy consumption, and help organizations with limited IT resources to deploy and maintain needed software in a timely manner. This paper also discussed the implication of this solution for future research directions to enable Cloud-Oriented Green Computing

Workload-Aware Database Monitoring and Consolidation

In most enterprises, databases are deployed on dedicated database servers. Often, these servers are underutilized much of the time. For example, in traces from almost 200 production servers from different organizations, we see an average CPU utilization of less than 4%. This unused capacity can be potentially harnessed to consolidate multiple databases on fewer machines, reducing hardware and operational costs. Virtual machine (VM) technology is one popular way to approach this problem. However, as we demonstrate in this paper, VMs fail to adequately support database consolidation, because databases place a unique and challenging set of demands on hardware resources, which are not well-suited to the assumptions made by VM-based consolidation. Instead, our system for database consolidation, named Kairos, uses novel techniques to measure the hardware requirements of database workloads, as well as models to predict the combined resource utilization of those workloads. We formalize the consolidation problem as a non-linear optimization program, aiming to minimize the number of servers and balance load, while achieving near-zero performance degradation. We compare Kairos against virtual machines, showing up to a factor of 12× higher throughput on a TPC-C-like benchmark. We also tested the effectiveness of our approach on real-world data collected from production servers at Wikia.com, Wikipedia, Second Life, and MIT CSAIL, showing absolute consolidation ratios ranging between 5.5:1 and 17:1

Concurrency Lock Issues in Relational Cloud Computing

The widespread popularity of Cloud computing as a preferred platform for the deployment of web applications has resulted in an enormous number of applications moving to the cloud, and the huge success of cloud service providers. Due to the increasing number of web applications being hosted in the cloud, and the growing scale of data which these applications store, process, and serve – scalable data management systems form a critical part of cloud infrastructures. There are issues related to the database security while database is on cloud. The major challenging issues are multi-tenancy, scalability and the privacy. This paper focuses on the problems faced in the data security of Relational Cloud. The problems faced by various types of tenants and the type of access into the database makes a rework on the security of data, by analyzing proper locking strategies on the records accessed from the database. Data security in cloud computing addresses the type of access mode by the users (for analytical or transaction purpose) and the frequency of data access from the physical location (in shared or no-shared disk mode). Accordingly, the various data locking strategies are studied and appropriate locking mechanism will be implemented for real-time applications as in e-commerce. Keywords: Relational Cloud, Multi-tenant, two-phase locking, concurrency control, data management