Marjan Gusev

Architecture and organization of e-Assessment cloud solution

All e-Assessment systems have several challenges, such as accurate evaluation, security and data privacy, performance, scalability etc. In this paper we focus on e-Assessment scalability and performance. We propose a SOA architecture of a cloud hosted e-Assessment system which uses scalability and elasticity in order to achieve sustainable performance. Our solution consists of three subsystems, the first for management, the second for reports, and the third for on-demand activities during the assessments. It reduces the overall costs since it uses minimum resources utilized only during the e-Assessment. Better performance is expected to be achieved since the active subsystem for each assessment works with much smaller data compared to the centralized one.

Compute and Memory Intensive Web Service Performance in the Cloud

Migration of web services from company’s on-site premises to cloud provides ability to exploit flexible, scalable and dynamic resources payable per usage and therefore it lowers the overall IT costs. However, additional layer that virtualization adds in the cloud decreases the performance of the web services. Our goal is to test the performance of compute and memory intensive web services on both on-premises and cloud environments. We perform a series of experiments to analyze the web services performance and compare what is the level of degradation if the web services are migrating from on-premises to cloud using the same hardware resources. The results show that there is a performance degradation on cloud for each test performed varying the server load by changing the message size and the number of concurrent messages. The cloud decreases the performance to 71.10% of on-premise for memory demand and to 73.86% for both memory demand and compute intensive web services. The cloud achieves smaller performance degradation for greater message sizes using the memory demand web service, and also for greater message sizes and smaller number of concurrent messages for both memory demand and compute intensive web services.

A superlinear speedup region for matrix multiplication

The realization of modern processors is based on a multicore architecture with increasing number of cores per processor. Multicore processors are often designed such that some level of the cache hierarchy is shared among cores. Usually, last level cache is shared among several or all cores (e.g., L3 cache) and each core possesses private low level caches (e.g., L1 and L2 caches). Superlinear speedup is possible for matrix multiplication algorithm executed in a shared memory multiprocessor due to the existence of a superlinear region. It is a region where cache requirements for matrix storage of the sequential execution incur more cache misses than in parallel execution. This paper shows theoretically and experimentally that there is a region, where the superlinear speedup can be achieved. We provide a theoretical proof of existence of a superlinear speedup and determine boundaries of the region where it can be achieved. The experiments confirm our theoretical results. Therefore, these results will have impact on future software development and exploitation of parallel hardware on the basis of a shared memory multiprocessor architecture. Copyright

Optimal Resource Allocation to Host Web Services in Cloud

In this paper, we analyze the performance of computation intensive and memory demanding web services hosted in different environments with the same amount of resources, but orchestrated differently. A single-VM addresses the environment where all the resources are allocated in one huge virtual machine instance (VMI), while a multi-VM environment uses several smaller VMIs, each allocated with only one CPU core, and the load is balanced among them. We realize series of experiments with different server loads by changing the message size and the number of concurrent messages to analyze the optimal resource allocation to host web services in order to achieve maximum performance from the same resources in the cloud, i.e., for the same price. Despite the hypothesis that the single-VM environment provides better performance than the multi-VM environment, the results show totally opposite for almost all test cases. We achieve maximal relative speedup of 9.83 comparing the multi-VM environment to the single-VM.

A Serverless Real-Time Data Analytics Platform for Edge Computing

Contemporary solutions for cloud-supported, edge-data analytics mostly apply analytics techniques in a rigid bottom-up approach, regardless of the datas origin. Typically, data are generated at the edge of the infrastructure and transmitted to the cloud, where traditional data analytics techniques are applied. Currently, developers are forced to resort to ad hoc solutions specifically tailored for the available infrastructure (for example, edge devices) when designing, developing, and operating the data analytics applications. Here, a novel approach implements cloud-supported, real-time data analytics in edge-computing applications. The authors introduce their serverless edge-data analytics platform and application model and discuss their main design requirements and challenges, based on real-life healthcare use case scenarios.

Windows Azure: Resource Organization Performance Analysis

Cloud customers can scale the resources according to their needs in order to avoid application bottleneck. The scaling can be done in two ways, either by increasing the existing virtual machine instance with additional resources, or by adding an additional virtual machine instance with the same resources. Although it is expected that the costs rise proportionally to scaling, we are interested in finding out which organization offers scaling with better performance. The goal of this paper is to determine the resource organization that produces better performance for the same cost, and help the customers decide if it is better to host a web application on a more ”small” instances or less ”large” instances. The first hypothesis states that better performance is obtained by using more and smaller instances. The second hypothesis is that the obtained speedup while scaling the resources is smaller than the scaling factor. The results from the provided experiments have not proven any of the hypotheses, meaning that using less, but larger instances results with better performance and that the user gets more performances than expected by scaling the resources.

P-TOSCA Portability of SOA Applications

Even more frequently, the customers express their increasing need to change the cloud provider and/or the operating cloud environment in order to avoid vendor lock-in. We analyze portability as the transferability of an application from on-premise onto a cloud (migration) and among different clouds (porting). The contribution of this paper is twofold: 1) demonstration of the P-TOSCA model for automated migration and porting of SOA applications onto a cloud and/or switch between cloud providers, and 2) evaluation of a significant time reduction in migration and porting.

Modifications and Improvements on CEN/BII Profiles

This paper introduces a new approach to modeling of e-Ordering and e-Invoicing solutions. Today, electronic means to conduct purchases and sales are essential activities for companies operations, and they need to decide how to implement e-Ordering and e-Invoicing, what software solutions to use, and how to integrate them successfully with their current software. The nature and application field of electronic services such as e-Ordering and e-Invoicing dictates increased interoperability between different platforms. EU-introduced Pan-European Public Procurement Online project, presents an effort for unifying the large variety of procurement solutions from various EU and non-EU countries, and uses CEN/BII profiles to introduce interoperability between them. This paper presents the summary of our analysis of the CEN/BII profiles, and introduces modifications and improvements to this model, to help the flexibility and ease of further modification of the model.

Software design patterns to develop an interoperable cloud environment

Software development has provided methods and tools to facilitate the development process, resulting in scalable, efficient, testable, readable and bug-free code. This endeavor has resulted in a multitude of products, many of them nowadays known as good practices, specialized environments, improved compilers, as well as software design patterns. Software design patterns are a tested methodology, and are most often language neutral. In this paper, we identify the problem of the heterogeneous cloud market, as well as the various APIs per a single cloud. By using a set of software design patterns, we developed a pilot software component that unifies the APIs of heterogeneous clouds. It offers an interface that would greatly simplify the development process of cloud based applications. The pilot adapter is developed for two open source clouds - Eucalyptus and OpenStack, but the usage of software design patterns allows an easy enhancement for all other clouds that have APIs for cloud management, either open source or commercial.

A new model for cloud elastic services efficiency

ABSTRACT The speedup measures the improvement in performance when the computational resources are being scaled. The efficiency, on the other side, provides the ratio between the achieved speedup and the number of scaled computational resources (processors). Both parameters (speedup and efficiency), which are defined according to Amdahl’s Law, provide very important information about performance of a computer system with scaled resources compared with a computer system with a single processor. However, as cloud elastic services’ load is variable, apart of the scaled resources, it is vital to analyse the load in order to determine which system is more effective and efficient. Unfortunately, both the speedup and efficiency are not sufficient enough for proper modeling of cloud elastic services, as the assumptions for both the speedup and efficiency are that the system’s resources are scaled, while the load is constant. In this paper, we extend the scaling of resources and define two additional scaled systems by (i) scaling the load and (ii) scaling both the load and resources. We introduce a model to determine the efficiency for each scaled system, which can be used to compare the efficiencies of all scaled systems, regardless if they are scaled in terms of load or resources. We have evaluated the model by using Windows Azure and the experimental results confirm the theoretical analysis. Although one can argue that web services are scalable and comply with Gustafson’s Law only, we provide a taxonomy that classifies scaled systems based on the compliance with both the Amdahl’s and Gustafson’s laws. For three different scaled systems (scaled resources R, scaled load L and combination RL), we introduce a model to determine the scaling efficiency. Our model extends the current definition of efficiency according to Amdahl’s Law, which assumes scaling the resources, and not the load. GRAPHICAL ABSTRACT