Context awareness, intended as providing the current execution environment at the service level, is a fundamental capability in future mobile systems. Unfortunately, the real-world realization of such scenarios is currently undermined by inefficient context data delivery mechanisms, which introduce excessive overhead over bandwidth-constrained wireless fixed infrastructures. To efficiently offload context access from fixed infrastructures to mobile nodes, this paper presents a new data caching algorithm that exploits peculiar aspects of context distribution, mainly limited data lifetime and interests similarity between nodes in physical proximity, to properly select the data to evict when necessary. Our solution considers a history over past data accesses and information over data replication to better exploit the limited available space. Extensive simulation results, collected in NS2 simulator, support our assumptions and demonstrate that our caching solution improves system scalability while adding a limited management overhead.

ABSTRACT In the last years, context awareness, namely the provisioning of the current execution context to the application level, has received an increasing attention up to becoming a core capability in next generation mobile scenarios. Context awareness intrinsically forces a continuous delivery of context data to resource‐constrained mobile devices, such as mobile phones and personal digital assistants, to allow application adaptation, and that can become too severe a constraint even for modern platforms (Android, iOS, etc.). This paper focuses on the realization of a context data distribution support for Android‐based mobile phones with guaranteed quality levels on the context data delivery time. Android, notwithstanding its great potential, puts harsh constraints on the implementation of specific context distribution primitives, thus preventing the realization of a wide set of significant deployment scenarios. At this stage, we face that a large campaign of tests are necessary. We have collected main experimental results in a real testbed to highlight noteworthy details on the runtime performance obtainable with a real Android deployment. Copyright © 2012 John Wiley & Sons, Ltd.

Virtual Machine (VM) placement has to carefully consider the aggregated resource consumption of co-located VMs in order to obey service level agreements at lower possible cost. In this paper, we focus on satisfying the traffic demands of the VMs in addition to CPU and memory requirements. This is a much more complex problem both due to its quadratic nature (being the communication between a pair of VMs) and since it involves many factors beyond the physical host, like the network topologies and the routing scheme. Moreover, traffic patterns may vary over time and predicting the resulting effect on the actual available bandwidth between hosts within the data center is extremely difficult. We address this problem by trying to allocate a placement that not only satisfies the predicted communication demand but is also resilient to demand time-variations. This gives rise to a new optimization problem that we call the Min Cut Ratio-aware VM Placement (MCRVMP). The general MCRVMP problem is NP-Hard, hence, we introduce several heuristics to solve it in reasonable time. We present extensive experimental results, associated with both placement computation and run-time performance under time-varying traffic demands, to show that our heuristics provide good results (compared to the optimal solution) for medium size data centers.

Context awareness, namely the provisioning of the current execution context at the application level, forces the continuous delivery of context data to resource-constrained mobile devices, and that can become too severe a constraint even for modern support (Android, iOS, etc.). This article focuses on the realization of a context data distribution infrastructure for Android-based mobile phones, and highlights important details on the implementation of specific context distribution primitives. Finally, we present new experimental results to assess the runtime performances obtainable with a real Android deployment.

Context-aware scenarios require the distribution of large amounts of context data over fixed wireless infrastructures. The opportunistic usage of mobile nodes as data carriers has received a great deal of interests in recent years in order to increase system scalability. In this paper, we focus on the overhead introduced by context distribution on mobile devices, and we highlight the need of resource-aware solutions to trade off run-time overhead with available resources. Next, we propose a novel algorithm to dynamically adapt data distribution task processing load at mobile devices. Our experimental results, obtained using a real testbed, demonstrate that our approach ensures correct resource management under a wide range of different working conditions.

Disaster area scenarios are the consequence of sudden and unexpected disasters due to either human or natural causes, such as terrorist attacks and earthquakes. Towards the main goal of saving as many human lives as possible, context-aware services are emerging as standard-de-facto solutions to improve the coordination of involved rescue teams. Unfortunately, the scarce resources (communication bandwidth, memory, etc.) lead to very tough deployment scenarios to deal with. This paper presents our real-world quality-based context data distribution infrastructure for context-aware services in disaster areas. The main contribution is a quality-based caching solution that self-adapts by using quality requirements associated to close neighbors. Collected experimental results confirm that our proposal improves both system scalability and data availability.

In the recent years, Cloud computing is emerging as the next big revolution of both computer networks and web provisioning. Due to its enormous promises, several vendors, such as Amazon and IBM, started designing, developing, and deploying Cloud solutions to optimize the usage of their own data centers. Unfortunately, several management issues of the Cloud are still open and deserve additional research. Among them, and fuelled by the emerging Green Computing research, Cloud architectures have to consolidate virtual machines in the minimal number of physical servers to reduce the run-time power consumption. In this paper, we present a project on power saving through server consolidation conducted at the IBM Innovation Centre in Dublin. Our experimental results, collected on a real testbed, show that server consolidation can effectively save energy, while introducing minimum performance degradation.

The possibility of distributing data to all interested entities connected to a mobile system is a fundamental core function in future mobile ubiquitous class based applications. Unfortunately, its realization poses many problems especially due to the typically scarce resources. With the goal of enhancing scalability, this paper presents a novel data distribution infrastructure for wireless mobile environments. After the introduction of quality-based indicators useful to manage data distribution, we present an adaptive data batching technique that exploits load information to reduce wireless channel congestion. Our results indicate that our data distribution infrastructure scales well also under high loads.

Disaster areas typical to deal with unexpected and sudden disasters, such as earthquakes and terrorist attacks, can take advantage of context-awareness, namely the capability of providing applications with full awareness of current execution context, to increase the possibility of saving human lives. Unfortunately, context-aware services in disaster areas require efficient and reliable context data distribution. Towards this direction, we present our quality-based context data distribution infrastructure, and we introduce two optimizations useful to improve both distribution efficiency and reliability. Experimental results, obtained by means of simulations, support our solutions.

Recent advances in portable client devices are enabling new scenarios where mobile users assume to continuously access to various services, such as email, printing, and social computing applications, by opportunistically exploiting any computing resource and any wireless connectivity possibility encountered during their roam. That requirement calls for suitable context-aware middlewares capable of retrieving and using context data to personalize service provisioning. However, existing solutions still exhibit very limited support for context data management and distribution, especially in wide-scale and highly heterogeneous systems. The paper proposes a novel context-aware middleware to achieve scalability in context data dissemination. The primary design guideline is to reduce context data traffic by using a hierarchical distributed architecture and pervasive physical/logical caching techniques. To validate our design choices, we exploited our context-aware middleware to realize an advertisements service and a service discovery facility in our university campus. Obtained performance results demonstrate that our solution positively affects system scalability and average context dissemination time.