Ronny Hans

Bypassing the cloud: Peer-assisted event dissemination for augmented reality games

The rising number of mobile devices and their increasing computational capabilities enable new interactive context-sensitive applications. Popular examples are augmented reality games such as Googles Ingress, where users interact with each other in the real world while being part of the game at the same time. This local interaction pattern in the real world as well as in the game is not reflected in the underlying communication pattern. Every locally generated game event is first transferred to a backend server via a cellular connection, from where it is then further disseminated to all players within the given area of interest. This communiation pattern introduces significant delays and limits the interactivity of the game. In this work, we propose an event dissemination system that exploits the locality characteristics of mobile augmented reality games to (i) enable and configure local peer-to-peer dissemination of events when appropriate and (ii) reconfigure or replace the utilized peer-to-peer protocol to adapt to a wide range of requirements. Through extensive evaluation we show that the proposed system decreases the delivery delay by a factor of eight compared to the existing communication pattern, leading to significantly increased information accuracy.

Will Mobile Cloud Gaming Work? Findings on Latency, Energy, and Cost

Mobile cloud gaming is a new and promising concept for the cloud-based delivery of video games to mobile devices in a platform-independent manner. In our ongoing work, we examine potential challenges of this service model concerning latency, energy consumption, and cost. The paper outlines our approach and presents initial results, which indicate that mobile cloud gaming could work well in wireless networks, specifically on modern smartphones, but may suffer from latency and cost issues in cellular networks.Software solution providers face numerous pitfalls when using standardized SOA-based solutions. One cause is variability between customer organizations—for example, variability occurs in local e-government because of municipalities’ autonomy when it comes to implementing national laws. This autonomy results in variations in business processes, which then cause variability in the information systems that implement them. This article presents pitfalls the authors observed in Dutch e-government, an example of a variability-intensive environment. They also present best practices that help deal with these pitfalls and organize them in an architecture pattern. Applying the pattern at more than 20 Dutch municipalities led to successful SOA implementations. Because such pitfalls are not just applicable to e-government, the proposed pattern can also be applied to other domains.Software solution providers face numerous pitfalls when using standardized SOA-based solutions. Once cause for pitfalls is variability between customer organizations. For example, in local e-government, variability occurs because of the autonomy of municipalities when implementing national laws. This autonomy results in variations in business processes in different municipalities, which then cause variability in information systems that implement business processes. In this article we report pitfalls that we observed in Dutch e-government, an example of a variability-intensive environment. Furthermore, we present best practices that help deal with these pitfalls and organize the best practices in an architecture pattern. Applying the pattern in more than 20 Dutch municipalities led to successful SOA implementations. As pitfalls are not only applicable to e-government, the proposed pattern can also be applied in other domains to deal with the pitfalls.

Let the clouds compute: cost-efficient workload distribution in infrastructure clouds

With cloud computing, a virtually inexhaustible pool of computing capacity has become available to IT users. However, given the large number of Infrastructure as a Service offers with differing pricing options, the cost-efficient distribution of workloads poses a complex challenge. In this work-in-progress paper, we formally describe the Cloud-oriented Workload Distribution Problem and propose an exact as well as a heuristic optimization approach. Through an evaluation that is based on realistic data from the cloud market, we examine the performance and practical applicability of these approaches.

Towards Heuristic Optimization of Complex Service-Based Workflows for Stochastic QoS Attributes

The problem of selecting services from a set of functionally appropriate ones under Quality of Service constraints - the Service Selection Problem - is well-recognized in the literature based on deterministic parameters. However, Quality of Service may rather follow a stochastic distribution and, thus, may change at runtime. In order to cope with differing Quality of Service, we present a heuristic approach for efficiently addressing the Service Selection Problem in conjunction with stochastic Quality of Service attributes. Accounting for penalty cost which accrue due to Quality of Service violations, our approach reduces the impact of stochastic Quality of Service behavior on total cost significantly.

Role-Based Templates for Cloud Monitoring

Cloud computing has evolved in the recent years to a well established computing paradigm. With this evolution, the complexity and requirements for monitoring cloud-based services have also increased. Without a doubt, monitoring for cloud computing is a crucial task which has been addressed in a number of research works. However, monitoring for cloud computing is often designed to be carried out by cloud providers. Monitoring by cloud providers on the one hand offers the flexibility and full control required for monitoring, on the other hand, the trustworthiness of the cloud provider is often questioned. In this work, we present a generic approach that can harmonize both of the aforementioned issues. Our solution abstracts from the complexity by using role-based templates for monitoring in combination with autonomous agents, thus, this approach can be used by both, cloud consumers and cloud providers. With a proof of concept prototype, we show that our approach can be adapted for large scale cloud monitoring scenarios. Furthermore, we discuss the possibility that our monitoring solution can be extended to be applicable for different domains.

Assessing Latency in Cloud Gaming

With the emergence of cloud computing, diverse types of Information Technology services are increasingly provisioned through large data centers via the Internet. A relatively novel service category is cloud gaming, where video games are executed in the cloud and delivered to a client as audio/video stream. While cloud gaming substantially reduces the demand of computational power on the client side, thus enabling the use of thin clients, it may also affect the Quality of Service through the introduction of network latencies. In this work, we quantitatively examined this effect, using a self-developed measurement tool and a set of actual cloud gaming providers. For the two providers and three games in our experiment, we found absolute increases in latency between approximately 40 ms and 150 ms, or between 85 % and 800 % in relative terms, compared to a local game execution. In addition, based on a second complementary experiment, we found mean round-trip times ranging from about 30 ms to 380 ms using WLAN and approximately 40 ms to 1050 ms using UMTS between a local computer and globally distributed compute nodes. Bilaterally among the compute nodes, results were in the range from approximately 10 ms to 530 ms. This highlights the importance of data center placement for the provision of cloud gaming services with adequate Quality of Service properties.

QoS-Aware, Cost-Efficient Selection of Cloud Data Centers

Cloud computing is increasingly used to deliver multimedia services with stringent Quality of Service (QoS) requirements, rather than simple infrastructure. Due to these requirements, the QoS-aware, cost-efficient selection of data centers arises as a new research challenge. In our ongoing work, we examine the corresponding Cloud Data Center Selection Problem and propose an exact optimization approach. A brief evaluation indicates that the proposed approach is primarily suited for small problem instances due to its high computational complexity, and hence highlights the need for the development of heuristic optimization approaches.

Knowledge for a Longer Life: Development Impetus for Energy-Efficient Smartphone Applications

In recent years, there has been a rapid growth in the spread of smartphones and thus in the utilization of mobile applications. Such applications require a substantial portion of the available energy. Since a short battery lifetime has a very negative impact for the user experience, application developers should have the skills and the knowledge to avoid energy-inefficient applications. In this paper, we present a comprehensive survey of approaches and methods to reduce the energy consumption and thus help software developers to improve their applications.

Switching Push and Pull: An Energy Efficient Notification Approach

An increasing number of modern smartphone applications are dependent on information updates from the cloud. To realize such information updates mainly two communication approaches are common, namely push- and pull. Due to different communication patterns both approaches differ in their energy consumption and notification latency. The energy constrained nature of mobile devices entails a sensible selection of the appropriate notification approach. In this paper we provide an evaluation of the energy consumption of both communication approaches. Based on this we provide a transition approach that is able to use the best of both, low latency and low energy consumption. Our results show that energy savings of up to 7% of the total smartphone battery per day can be achieved by switching between both approaches, depending on the context.

Cost-efficient Capacitation of Cloud Data Centers for QoS-aware Multimedia Service Provision

Cloud infrastructure is increasingly used for the provision of sophisticated multimedia services, such as cloud gaming or Desktop as a Service, with stringent Quality of Service demands. Serving these service demands results in the need to cost-efficiently select and capacitate data centers. In the work at hand, we introduce the corresponding Cloud Data Center Capacitation Problem and propose two optimization approaches. Through a quantitative evaluation, we demonstrate that an exact solution approach is only practically applicable to small problem instances, whereas a heuristic based on LP relaxation achieves significant reductions in computation time of about 80% while retaining a favorable solution quality, with cost increases of approximately 5% or less.