Introductory programming courses can be challenging due to heterogeneous student backgrounds. While novice programmers find even basic contents very demanding, experienced programmers are not sufficiently challenged. Having both novice and experienced programmers attend the same lab exercise can have a negative impact on novice programmers' self-efficacy. To create a better teaching environment for students, two different tracks were introduced in our introductory programming course, one for beginners and one for advanced students. Incoming students are offered a voluntary exam that can be used to obtain a place in the advanced track. Only one course is held throughout the semester but the advanced track does not begin lab exercises until the middle of the semester. This paper outlines the design and implementation of our course and its placement exam. It describes how, over the past four years, internal factors such as student feedback and grades, as well as external factors such as COVID-19, have influenced the design of the placement exam. Various statistics collected during this period are analysed and compared with results from the CEd literature.

In this paper we present the implementation of a framework for accelerating training and classification of arbitrary Convolutional Neural Networks (CNNs) on the GPU. CNNs are a derivative of standard Multilayer Perceptron (MLP) neural networks optimized for two-dimensional pattern recognition problems such as Optical Character Recognition (OCR) or face detection. We describe the basic parts of a CNN and demonstrate the performance and scalability improvement that can be achieved by shifting the computation-intensive tasks of a CNN to the GPU. Depending on the network topology training and classification on the GPU performs 2 to 24 times faster than on the CPU. Furthermore, the GPU version scales much better than the CPU implementation with respect to the network size.

Ordinal time series analysis is a simple approach to the investigation of experimental data. The basic idea is to consider the order relations between the values of a time series and not the values themselves. This results in fast and robust algorithms that extract the basic intrinsic structure of the given data series. This paper gives a short overview of this approach and describes the application of simple ordinal time series methods like rank autocorrelation, local rank autocorrelation and permutation entropy to workload traces from large grid computing systems. We show how these methods can be used to extract important correlation information from experimental traces and how these methods outperform traditional methods.

The drift towards new challenges in grid computing, including the utility grid paradigm and service level agreements based on quality-of-service guarantees, implies the need for new, robust, multi-criteria scheduling algorithms that can be applied by the user in an intuitive way. Multiple scheduling criteria addressed by the related grid research include execution time, the cost of running a task on a machine, reliability, and different data quality metrics. The existing bi-criteria scheduling approaches are usually dedicated for certain criterion pairs only that require the user to define one's preferences either as weights assigned to the criteria or as fixed constraints defined for one of the criteria. These requirements are often not feasible for the user and not suited to the specificity of the multi- criteria scheduling problem. We propose a novel requirement specification method based on a sliding constraint, and we model the problem as an extension of the multiple-choice knapsack problem. We propose a general bi-criteria scheduling heuristic called dynamic constraint algorithm (DCA) based on dynamic programming, dedicated to the problem model defined by us. In the experimental study, we show that in most of the problem variants, DCA outperforms two existing algorithms designed for the same problem. It also shows relatively low scheduling times for workflows of medium size.

Today's massively multiplayer online games (MMOGs) can include millions of concurrent players spread across the world. To keep these highly-interactive virtual environments online, a MMOG operator may need to provision tens of thousands of computing resources from various data centers. Faced with large resource demand variability, and with misfit resource renting policies, the current industry practice is to maintain for each game tens of self-owned data centers. In this work we investigate the dynamic resource provisioning from external data centers for MMOG operation. We introduce a novel MMOG workload model that represents the dynamics of both the player population and the player interactions. We evaluate several algorithms, including a novel neural network predictor, for predicting the resource demand. Using trace-based simulation, we evaluate the impact of the data center policies on the resource provisioning efficiency; we show that dynamic provisioning can be much more efficient than its static alternative.

Grid economy models have long been considered as a promising alternative for the classical Grid resource management, due to their dynamic and decentralized nature, and because the financial valuation of resources and services is inherent in any such model. In particular, auction models are widely used in the existing Grid research, as they are easy to implement and are shown to successfully manage resource allocation on the Grid market. The focus on the current work is on workflow scheduling in the Grid resource allocation model based on Continuous Double Auctions (CDA). We analyze different scheduling strategies that can be applied by the user to execute workflows in such an environment, and try to identify the general behavioral patterns that can lead to a fast and cheap workflow execution. In the experimental study, we show that under certain circumstances some benefit can be gained by applying an ldquoaggressiverdquo scheduling strategy.

Due to the increasing availability and use of digital video data on the Web, video caching will be an important performance factor in the future WWW. We propose an architecture of a video proxy cache that integrates modern multimedia and communication stan-dards. Especially we describe features of the MPEG-4 and MPEG-7 multimedia standards that can be helpful for a video proxy cache. QBIX1 supports real-time adaptation in the compressed and in the decompressed domain. It uses adaptation to improve the cache replacement strategies in the proxy, but also to realize media gate-way functionality driven by the clients ’ terminal capabilities.

Video caching will be an important performance factor in future networked multimedia systems. A major component of caches is the replacement strategy. This paper presents replacement strategies for video caches that incorporate quality reduction and the use of metadata given by the content provider. The strategies are evaluated by simulation.