A heuristic approach for the allocation of resources in large-scale computing infrastructures

An increasing number of enterprise applications are intensive in their consumption of IT, but are infrequently used. Consequently, organizations either host an oversized IT infrastructure or they are incapable of realizing the benefits of new applications. A solution to the challenge is provided by the large-scale computing infrastructures of Clouds and Grids which allow resources to be shared. A major challenge is the development of mechanisms that allow efficient sharing of IT resources. Market mechanisms are promising, but there is a lack of research in scalable market mechanisms. We extend the Multi-Attribute Combinatorial Exchange mechanism with greedy heuristics to address the scalability challenge. The evaluation shows a trade-off between efficiency and scalability. There is no statistical evidence for an influence on the incentive properties of the market mechanism. This is an encouraging result as theory predicts heuristics to ruin the mechanism’s incentive properties. Copyright © 2015 John Wiley & Sons, Ltd

DISSECT-CF: a simulator to foster energy-aware scheduling in infrastructure clouds

Infrastructure as a service (IaaS) systems offer on demand virtual infrastructures so reliably and flexibly that users expect a high service level. Therefore, even with regards to internal IaaS behaviour, production clouds only adopt novel ideas that are proven not to hinder established service levels. To analyse their expected behaviour, new ideas are often evaluated with simulators in production IaaS system-like scenarios. For instance, new research could enable collaboration amongst several layers of schedulers or could consider new optimisation objectives such as energy consumption. Unfortunately, current cloud simulators are hard to employ and they often have performance issues when several layers of schedulers interact in them. To target these issues, a new IaaS simulation framework (called DISSECT-CF) was designed. The new simulator's foundation has the following goals: easy extensibility, support energy evaluation of IaaSs and to enable fast evaluation of many scheduling and IaaS internal behaviour related scenarios. In response to the requirements of such scenarios, the new simulator introduces concepts such as: a unified model for resource sharing and a new energy metering framework with hierarchical and indirect metering options. Then, the comparison of several simulated situations to real-life IaaS behaviour is used to validate the simulator's functionality. Finally, a performance comparison is presented between DISSECT-CF and some currently available simulators

The Next-Generation OS Process Abstraction

Operating Systems are built upon a set of abstractions to provide resource management and programming APIs for common functionality, such as synchronization, communication, protection, and I/O. The process abstraction is the bridge across these two aspects; unsurprisingly, research efforts pay particular attention to the process abstraction, aiming at enhancing security, improving performance, and supporting hardware innovations. However, given the intrinsic difficulties to implement modifications at the OS level, recent endeavors have not yet been widely adopted in production-oriented OSes. Still, we believe the current hardware evolution and new application requirements provide favorable conditions to change this trend. This paper evaluates recent research on OS process features identifying potential evolution paths. We derive a set of relevant process characteristics, and propose how to extend them as to benefit OSes and applications

Construction and validation of learning style assessment instrument SU-19

The aim of this study was to construct and validate a new instrument for assessing high-school students’ learning styles. The instrument consists of 7 dimensions that measure a person’s approach to learning through 52 items. A total of 801 pupils took part in the study, 160 of which were gifted scholarship students. Results confirm sound psychometric properties and validity of the scale. Exploratory factor analysis identified 7 factors that explain 46% of the total variance: Time management, Individuality, Relating ideas, Deep meaning, Strategies, Abstractness and Motivation. Confirmatory factor analysis confirms the basic factor structure while highlighting room for improvement. The scale significantly contributed to the prediction of general academic achievement and grades in specific subjects. Discriminant analysis demonstrated the instrument’s ability to differentiate between gifted students and the general student population with an 82.4% success rate. We conclude that our instrument can be used to assess the klearning styles of students and can serve as a useful tool for predicting individual academic achievemen

Farview: Disaggregated Memory with Operator Off-loading for Database Engines

Cloud deployments disaggregate storage from compute, providing more flexibility to both the storage and compute layers. In this paper, we explore disaggregation by taking it one step further and applying it to memory (DRAM). Disaggregated memory uses network attached DRAM as a way to decouple memory from CPU. In the context of databases, such a design offers significant advantages in terms of making a larger memory capacity available as a central pool to a collection of smaller processing nodes. To explore these possibilities, we have implemented Farview, a disaggregated memory solution for databases, operating as a remote buffer cache with operator offloading capabilities. Farview is implemented as an FPGA-based smart NIC making DRAM available as a disaggregated, network attached memory pool capable of performing data processing at line rate over data streams to/from disaggregated memory. Farview supports query offloading using operators such as selection, projection, aggregation, regular expression matching and encryption. In this paper we focus on analytical queries and demonstrate the viability of the idea through an extensive experimental evaluation of Farview under different workloads. Farview is competitive with a local buffer cache solution for all the workloads and outperforms it in a number of cases, proving that a smart disaggregated memory can be a viable alternative for databases deployed in cloud environments

Threat Modeling the Cloud:An Ontology Based Approach

Critical Infrastructures (CIs) such as e-commerce, energy, transportation, defense, monitoring etc., form the basis of the modern ICT society, and these CI’s increasingly utilize ICT services such as the Cloud to provide for scalable, robust and cost-efficient services. Consequently, the resilience of the CI is directly connected with the resilience of the underlying Cloud infrastructure. However, performing a Cloud threat analysis (TA) is a challenging task given the complex interconnection of underlying computing and communication services. Thus, the need is of a comprehensive TA approach that can holistically analyze the relation across system level requirements and Cloud vulnerabilities. We target achieving such a requirement based threat analysis by developing an ontology depicting the relations among actors involved in the Cloud ecosystem. The ontology comprehensively covers requirement specifications, interaction among the Cloud services and vulnerabilities violating the requirements. By mapping the ontology to a design structure matrix, our approach obtains security assessments from varied actor perspectives. We demonstrate the effectiveness of our approach by assessing the security of OpenStack, an open source Cloud platform, covering user requirements and services involved in Cloud operations