Piotr Rygielski

Dynamic Resources Allocation for Delivery of Personalized Services

The aim of this paper is to introduce a problem of e-health services quality management. The process of delivering e-health services to users consists of two major tasks: service personalization and resources allocation. In this paper we introduce a use-cases of e-health system and distinguish services that can be offered. In order to satisfy user requirements one have to manage resources properly especially when communication conditions change (e.g. in an ambulance). An exemplary solution has been presented and conclusions for further work have been formulated.

Network Virtualization for QoS-Aware Resource Management in Cloud Data Centers: A Survey

The increasing popularity of Cloud Computing is leading to the emergence of large virtualized data centers hosting increasingly complex and dynamic IT systems and services. Over the past decade, the efficient sharing of computational resources through virtualization has been subject to intensive research, while network management in cloud data centers has received less attention. A variety of network-intensive applications require QoS (Quality-of-Service) provisioning, performance isolation and support for flexible and efficient migration of virtual machines. In this paper, we survey existing network virtualization approaches and evaluate the extent to which they can be used as a basis for realizing the mentioned requirements in a cloud data center. More specifically, we identify generic network virtualization techniques, characterize them according to their features related to QoS management and performance isolation, and show how they can be composed together and used as building blocks for complex network virtualization solutions. We then present an overview of selected representative cloud platforms and show how they leverage the generic techniques as a basis for network resource management. Finally, we outline open issues and research challenges in the area of performance modeling and proactive resource management of virtualized data center infrastructures.

Context change detection for resource allocation in service-oriented systems

In this paper, the problem of detecting the major changes in the stream of service requests is formulated. The change of stream component varies over time and depends on, e.g., a time of a day. The underlying cause of the change is called a context. Hence, at each moment there exists a probability distribution determining the probability of requesting the system service conditioned by the context. The aim is to find such a moment in which the distributions change. To solve that problem dissimilarity measures between two probability distributions are given. Nevertheless, detecting every change is not interesting but only long-lasting changes because of the costs of the service system resources reallocation. Therefore, in the proposed algorithm an issue of sensitivity to temporary changes detection is considered.

Specialized Heuristics for the Controller Placement Problem in Large Scale SDN Networks

The Software Defined Networking (SDN) concept introduces a paradigm shift in the networking world towards an externalized control plane which is logically centralized. When designing an SDN-based WAN architecture, it is of vital importance to find a feasible solution to the controller placement problem, i.e., to decide where to position a limited amount of resources within the network. In addition to time-independent constraints regarding aspects like scalability, resilience, and control plane communication delays, dynamically changing network conditions like traffic patterns or bandwidth demands need to be considered as well. Consequently, such dynamic environments call for a regular and fast recalculation of placements in order to adapt to the current situation in a timely manner. While an exhaustive evaluation of all possible solutions can be performed within a practically feasible time frame for small and medium-sized networks, such an approach is out of scope for large problem instances which have significantly higher time and memory requirements. Therefore, this work investigates a specialized heuristic, which takes into account a particular set of optimization objectives and returns solutions representing the possible trade-offs between them. Due to its low computation time and acceptable margin of error, this heuristic can be employed by automatic decision systems operating in dynamic environments.

Data Center Network Throughput Analysis Using Queueing Petri Nets

In this paper, we contribute performance modeling and analysis approach in computer networks. We present a meta-model designed for the performance modeling of network infrastructures in modern data centers. Instances of our meta-model can be automatically transformed into stochastic simulation models for performance prediction. In this paper, we present a transformation to Queueing Petri Nets (QPNs). We show that despite the high level of abstraction of the QPN models, we are able to predict the utilization of resources with good accuracy within a short time.

Model-based throughput prediction in data center networks

In this paper, we address the problem of performance analysis in computer networks. We present a new meta-model designed for the performance modeling of network infrastructures in modern data centers. Instances of our metamodel can be automatically transformed into stochastic simulation models for performance prediction. We evaluate the approach in a case study of a road traffic monitoring system. We compare the performance prediction results against the real system and a benchmark. The presented results show that our approach, despite of introducing many modeling abstractions, delivers predictions with errors less than 32% and correctly detects bottlenecks in the modeled network.

A meta-model for performance modeling of dynamic virtualized network infrastructures

In this work-in-progress paper, we present a new meta-model designed for the performance modeling of dynamic data center network infrastructures. Our approach models characteristic aspects of Cloud data centers which were not crucial in classical data centers. We present our meta-model and demonstrate its use for performance modeling and analysis through an example, including a transformation into OMNeT++ for performance simulation.

USER ASSIGNMENT AND MOVEMENT PREDICTION IN WIRELESS NETWORKS

The article is devoted to the problem of mobility and resource management in heterogeneous wireless networks. It is assumed that in certain areas covered by multiple overlapping wireless networks there are a certain number of mobile clients that consume networks resources by use of the available communication services (e.g., voice or data transmission) delivered by network providers. Moreover, it is assumed that the continuity of communication services may be assured by the use of common handover techniques supporting client mobility (e.g., Mobile IPv6, IEEE 802.21, etc.). The task of mobility and resource management consists of making decisions concerning the moment and the network to which particular clients should be handed over in order to optimize certain quality criterion (e.g., utilization of network resources). In this article we show that gathering knowledge about client movements and prediction of their future positions may significantly improve the overall quality of the services delivered and network resource utilization.

Flexible performance prediction of data center networks using automatically generated simulation models

Using different modeling and simulation approaches for predicting network performance requires extensive experience and involves a number of time consuming manual steps regarding each of the modeling formalisms. In this paper, we propose a generic approach to modeling the performance of data center networks. The approach offers multiple performance models but requires to use only a single modeling language. We propose a two-step modeling methodology, in which a high-level descriptive model of the network is built in the first step, and in the second step model-to-model transformations are used to automatically transform the descriptive model to different network simulation models. We automatically generate three performance models defined at different levels of abstraction to analyze network throughput. By offering multiple simulation models in parallel, we provide flexibility in trading-off between the modeling accuracy and the simulation overhead. We analyze the simulation models by comparing the prediction accuracy with respect to the simulation duration. We observe, that in the investigated scenarios the solution duration of coarser simulation models is up to 300 times shorter, whereas the average prediction accuracy decreases only by 4 percent.

Migration-aware Optimization of Virtualized Computational Resources Allocation in Complex Systems

In this paper a task of resources allocation in the complex system is considered. Novelty of the formulated task consists of assumption that the applications assigned with resources of one machine can be migrated to another machine during system lifetime. The formulated task has been solved using proposed heuristic optimization method. Due to non-convex set of valid solutions the optimization procedure has been decomposed into two stages and forms approach similar to the relax-and-round approach. Proposed decomposition approach facilitates fast algorithm convergence and guaranties that achieved solution satisfies assumed constraints.