The EU-funded EMPYREAN project (empyrean-horizon.eu) aims to establish a hyper-distributed computing paradigm, leveraging collaborative, heterogeneous IoT devices and federated resources. EMPYREAN focuses on developing technologies for efficient AI workload processing, secure distributed edge storage and cloud-native application development. It will offer open and standardised APIs and use open-source platforms. EMPYREAN's capabilities will be demonstrated through three use cases: advanced manufacturing, smart agriculture, and warehouse automation.

Abstract The orchestration of cloud computing infrastructures is challenging, considering the number, heterogeneity and dynamicity of the involved resources, along with the highly distributed nature of the applications that use them for computation and storage. Evidently, the volume of relevant monitoring data can be significant, and the ability to collect, analyze, and act on this data in real time is critical for the infrastructure’s efficient use. In this study, we introduce a novel methodology that adeptly manages the diverse, dynamic, and voluminous nature of cloud resources and the applications that they support. We use knowledge graphs to represent computing and storage resources and illustrate the relationships between them and the applications that utilize them. We then train GraphSAGE to acquire vector-based representations of the infrastructures’ properties, while preserving the structural properties of the graph. These are efficiently provided as input to two unsupervised machine learning algorithms, namely CBLOF and Isolation Forest, for the detection of storage and computing overusage events, where CBLOF demonstrates better performance across all our evaluation metrics. Following the detection of such events, we have also developed appropriate re-optimization mechanisms that ensure the performance of the served applications. Evaluated in a simulated environment, our methods demonstrate a significant advancement in anomaly detection and infrastructure optimization. The results underscore the potential of this closed-loop operation in dynamically adapting to the evolving demands of cloud infrastructures. By integrating data representation and machine learning methods with proactive management strategies, this research contributes substantially to the field of cloud computing, offering a scalable, intelligent solution for modern cloud infrastructures.

Cell-Free (CF) technology is considered as a candidate to support the “5G and beyond” networks, mitigating the limitations of the traditional networks in terms of flexibility and intercell interference. These networks consist of distributed Access Points (APs) that form clusters, and co-operate in time to serve the User Equipment (UE) demands. The number of the AP that participate in a cluster and the level at which they cooperate impacts both the achieved spectral efficiency and the size of the utilized communication and processing resources, which in most cases are scarce and limited. In our work, we propose mechanisms that perform joint allocation of fiber and wireless resources in a converged fiber-wireless infrastructure, consisting of a TWDM PON, mMIMO Base Stations and CF. To perform the joint allocation of the wireless and wired resources, we propose an optimal Mixed Integer Linear Program (MILP). As the complexity is high and the execution time prohibitively long for real size scenarios, we also present a multi-agent rollout mechanism to efficiently tradeoff execution time with performance.

We present an end-to-end solution to facilitate the seamless execution of hardware-accelerated compute-intensive tasks on heterogeneous hardware platforms spanning the Cloud-Edge continuum. Our approach includes a programming interface, orchestration, application management components, the vAccel framework, and a library of hardware-accelerated kernels. These components enable a Function-as-a-Service (FaaS) based operational flow that supports numerous diverse use cases while minimizing the time required for the developer to integrate their code and for the vendor to provide hardware acceleration capabilities to end users. Experimental results showcase the merits of our approach.

Distributed storage systems spanning across different cloud data centers have substantially improved availability and flexibility for data storage and retrieval operations. However, stringent latency requirements of emerging applications necessitate optimized selection of storage resources that exhibit smaller delay. Introducing edge resources into distributed storage systems enables data placement closer to its source, but simultaneously increases the complexity of decision-making and orchestration processes for optimal data placement. In this work, we develop mechanisms for storing data across an infrastructure that includes both edge and cloud resources. Our approach focuses on optimizing data integrity, longevity, security, and cost, while leveraging erasure coding when performing the resource allocation. We first present a comprehensive mixed integer linear programming formulation of the storage resource orchestration problem. As the search space for the optimal solution can be vast and the execution time prohibitively large for real size problems, we also propose an innovative multi-agent heuristic approach that uses the rollout, a reinforcement based policy, to balance performance and execution time efficiently. Through various simulation experiments, we evaluate the developed mechanisms and trade-offs involved in our approach. By incorporating data from a multi-cloud provider, we further enhance the validity of the simulations and the conclusions drawn.

The emergence of Internet of Things (IoT) and the anticipated 5G/6G applications lead to several challenges regarding the rapid and the efficient processing of massive amounts of data, which are generated, transferred and processed within a city boundaries. Towards this end, the convergence of computing, storage and networking infrastructures operating in a megacity environment is pivotal. In this work, we present several related research innovations regarding the service of user and application demands, the orchestration of cloud and edge resources and the realization of edge infrastructures.

The uncertain and non-dispatchable nature of renewable energy sources renders Demand Response (DR) a critical component of modern electricity distribution systems. Demand Response (DR) service provision takes place via aggregators and special distribution-level markets (e.g., flexibility markets), where small, distributed DR resources, such as building energy management systems, electric vehicle charging stations, micro-generation and storage, connected to the low-voltage distribution grid, offer DR services. In such systems, energy balancing (and thus, also DR decisions) have to be made close to real-time. Thus, market clearing algorithms for DR service provision must fulfill several requirements related to the efficiency of their operation. More specifically, a DR market clearing algorithm needs to be optimal in terms of cost-efficiency, scalable in terms of number of assets and locations, and able to satisfy real-time constraints. In order to cope with these challenges, this article presents a distributed DR market clearing algorithm based on Lagrangian decomposition, combined with an optimal cloud resource allocation algorithm for assigning the required computation power. A heuristic algorithm is also presented, able to achieve a near-optimal solution, within negligible computational time. Simulations, performed on a testbed, demonstrate the computational burden introduced by various DR models, as well as the heuristic algorithm's near-optimal performance. The resource allocation algorithm is able to service multiple DR requests (e.g., in multiple distribution networks), and minimize the cost of computational resources while respecting the execution time constraints of each request. This enables third parties to offer cost-efficient and competitive DR operation as a service.

The design and operation of any network are complex processes that require the evaluation, utilization and configuration of a variety of usually expensive network devices. Through the use of an emulation platform, network operators are able to examine different scenarios and network parameters and benefit from multi-objective decision mechanisms. These enable the decrease of the network design phase duration and the optimal operation of the network under different well examined conditions. In this work, we present an emulation platform for SDN-enabled 5G integrated Fiber-Wireless networks that provides a transparent view of the 5G infrastructure to any SDN-based control plane. We present the overall architecture and design of the emulator, along with the implementation details of its main components. Network devices are described through YANG models and are emulated using containerized processes, configured and managed through the Network Configuration (NETCONF) protocol. Finally, a number of emulation scenarios are described and evaluated, utilizing a joint fiber and wireless resource allocation algorithm that drives the SDN-enabled devices.

We present the Disaster Recovery Layer (DRL) that enables OpenStack-managed datacenter workloads, Virtual Machines (VMs) and Volumes, to be protected and recovered in another datacenter, in case of a disaster. This work has been carried out in the context of the EU FP7 ORBIT project that develops technologies for enabling business continuity as a service. The DRL framework is based on a number of autonomous components and extensions of OpenStack modules, while its functionalities are available through OpenStack's Horizon UI and command line interface. Also, the DRL's architecture is extensible, allowing for the easy and dynamic integration of protection, restoration and orchestration plug-ins that adopt new approaches. A distributed disaster detection mechanism was also developed for identifying datacenter disasters and alerting the DRL. For the evaluation of the DRL, a two (active and backup) datacenters testbed has been setup in respective sites in Umea and Lulea, 265km apart and connected through the Swedish national research and education network. In case of a disaster, traffic is redirected between the datacenters utilizing the BGP anycast scheme. The experiments performed, show that DRL can efficiently protect VMs and Volumes, with minimum service disruption in case of failures and low overhead, even when the available bandwidth is limited.

The efficient allocation of network resources is key to the overall performance and the quality of services provided. Thanks to their high data rates, optical networks are the cornerstone of present and future core, metro, access, and datacenter networking. Many works in the field, formulate resource allocation operations as offline combinatorial problems, assuming a known static traffic matrix, and use integer linear programming (ILP) as well as heuristics. In contrast, other works assume randomly generated traffic and propose online schemes that serve connection requests one by one. In practice, traffic in optical networks is neither static nor completely random, but is usually semi-periodic, following some (e.g., daily or weekly) pattern. We present a traffic-pattern-driven approach for elastic optical networks for serving immediate and in advance network requests, where the decisions of an offline process, optimizing resource allocation for the traffic pattern expected during an epoch (day, week, etc.), are analyzed and then drive the operation of an online process that serves requests one by one, as they arrive. In this way, the online mechanism's decisions come close to the optimal ones, if the traffic pattern indeed repeats itself to some extent, while its execution time remains small. We present two alternatives of this approach, the exact and the relative, based on the way the offline mechanism's decisions are analyzed and translated to online actions. Our simulation results exhibit the performance benefits of the pattern-driven approach under various traffic conditions.

The shift of the radio processing to the cloud, through cloud Radio Access Networks (C-RAN) technologies and of the cloud processing to the edge, through edge computing, form the environment in which 5G systems are being implemented, fostered and transformed from a future technology to a mainstream one. By its nature, global optimization of the edge resource deployment cannot by easily performed considering the number and the diversity of the players that will be involved in the edge computing arena. As a result, building and maintaining more and more edge-located resources for serving radio and application data will eventually lead to increased cost and energy consumption and resource underutilization. One way to overcome this predicament, is through virtual resource consolidation, where separate but efficiently interconnected edge resources appear as a single computing entity, serving radio and application data. In this context, we present the Virtual Elastic Datacenters (VEDC) in the edge notion for 5G networks that can alleviate these issues. We also describe an Integer Linear Programming (ILP) based mechanism for the placement of baseband and application processing loads in a VEDC-based environment, and perform respective experiments. We show that through VEDC resource consolidation better quality services can be provided, while improving resource efficiency.

Energy consumption and the associated costs constitute a crucial issue concerning the design and operation of data networks and data centers. Energy-awareness is required in all levels, ranging from physical layer to algorithms, protocols and applications. Architecture-wise, a promising solution for tackling the increasing energy requirements is the deployment of optics at both long and shorter distances, including within data centers. Vertical Cavity Surface Emitting Lasers (VCSEL) constitute a popular photonic transmitter technology used in numerous short-range applications, providing also the ability to reduce energy consumption by scaling down the transmission bit rate. In this study we focus on the algorithmic aspects of energy management by proposing an OptiMal EnerGy Aware (OMEGA) routing algorithm to operate in optical networks utilizing VCSEL-based opto-electronic links. The algorithm leverages the capability of VCSELs to adapt the energy dissipation with respect to the transmission bit rate. Simulation results, under various traffic patterns, show that OMEGA balances efficiently the traffic load over the network's links, resulting in high throughput and low energy consumption.

The millions of tweets submitted daily overwhelm users who find it difficult to identify content of interest revealing the need for event detection algorithms in Twitter. Such algorithms are proposed in this paper covering both short (identifying what is currently happening) and long term periods (reviewing the most salient recently submitted events). For both scenarios, we propose fuzzy represented and timely evolved tweet-based theoretic information metrics to model Twitter dynamics. The Riemannian distance is also exploited with respect to words' signatures to minimize temporal effects due to submission delays. Events are detected through a multiassignment graph partitioning algorithm that: 1) optimally retains maximum coherence within a cluster and 2) while allowing a word to belong to several clusters (events). Experimental results on real-life data demonstrate that our approach outperforms other methods.

Cloud computing and virtualized infrastructures are currently the baseline environments for the provision of services in different application domains. While the number of service consumers increasingly grows, service providers aim at exploiting infrastructures that enable non-disruptive service provisioning, thus minimizing or even eliminating downtime. Nonetheless, to achieve the latter current approaches are either application-specific or cost inefficient, requiring the use of dedicated hardware. In this paper we present the reference architecture of a fault-tolerance scheme, which not only enhances cloud environments with the aforementioned capabilities but also achieves high-performance as required by mission critical every day applications. To realize the proposed approach, a new paradigm for memory and I/O externalization and consolidation is introduced, while current implementation references are also provided.

We present a methodology, called communication-aware virtual infrastructures (COMAVI), for the concurrent migration of multiple Virtual Machines (VMs) in cloud computing infrastructures, which aims at the optimum use of the available computational and network resources, by capturing the interdependencies between the communicating VMs. This methodology uses multiple criteria for selecting the VMs that will migrate, with different weights assigned to each of them. COMAVI also selects the computing sites/units where the migrating VMs will be hosted, by accounting for the way migration affects the logical (or virtual) topologies formed by the communicating VMs and viewing this selection as a logical topology reconfiguration problem. COMAVI resolves the maximum possible number of VM resource shortages, while tending to minimize the number of migrations performed, the induced network overhead, the logical topology reconfigurations required, and the corresponding service interruptions. We evaluate the proposed method through simulations, where we exhibit their performance benefits.

Resource selection and task assignment are basic operations in distributed computing environments, like the grid and the cloud, where tasks compete for resources. The decisions made by the corresponding algorithms should be judged based not only on metrics related to user satisfaction, such as the percentage of tasks served without violating their quality-of-service (QoS) requirements, but also based on resource-related performance metrics, such as the number of resources used to serve the tasks and their utilization efficiency. In our work, we focus on the case of tasks with fixed but not strict time requirements, given in the form of a requested start and finish time. We propose an algorithm for assigning tasks to resources that minimizes the violations of the tasks' time requirements while simultaneously maximizing the resources' utilization efficiency for a given number of resources. The exact time scheduling of the tasks on the resources is then decided by taking into account the time constraints. The proposed scheme exploits concepts derived from graph partitioning, and groups together tasks so as to 1) minimize the time overlapping of the tasks assigned to a given resource and 2) maximize the time overlapping among tasks assigned to different resources. The partitioning is performed using a spectral clustering methodology through normalized cuts. Experimental results show that the proposed algorithm outperforms other scheduling algorithms for different values of the granularity and the load of the task requests.

The monitoring and the analysis of public clouds gains momentum, due to their widespread exploitation by individual users, researchers and companies for their daily tasks. We propose an algorithm for optimizing the cost and the utilization of a set of running Amazon EC2 instances by resizing them appropriately. The algorithm, namely Cost and Utilization Optimization (CUO) algorithm, receives information regarding the current set of instances used (their number, type, utilization) and proposes a new set of instances for serving the same load, so as to minimize cost and maximize utilization, or increase performance efficiency. CUO is integrated in Smart cloud Monitoring (SuMo), an open-source tool we develop for collecting monitoring data from Amazon Web Services (AWS) and analyzing them. A number of experiments are performed, using input data that correspond to realist AWS configuration scenarios, which exhibit the benefits of the CUO algorithm.

The cooperation among mobile hosts in wireless ad-hoc networks is usually in the form of nodes acting as intermediate relays that forward data from a source to an otherwise distant destination using point-to-point or point-to-multipoint links. A technique that has gained considerable recent attention is cooperative diversity, where nodes are organized for transmitting the same signal to a given, often otherwise unreachable, node. The receiver combines the multiple receptions to reconstruct the original signal. In this work, we examine the routing and power allocation problem under such a cooperative communications model, so as to obtain a cross-layer design of the network and the physical layer. We present and evaluate a multi-criteria cooperative routing algorithm that uses as parameters the nodes' residual energy and their transmission power. This algorithm selects for each source-destination pair a path, in the form of a sequence of groups of cooperative nodes, and the nodes' transmission powers. We perform a number of simulation experiments, assuming nodes with variable or fixed transmission power, evaluating the benefits of the proposed multi-criteria cooperative routing algorithm. The results show that our algorithm achieves significant energy savings and a larger number of successfully delivered packets than in the case where cooperation is not applied.

SUMMARY Grid scheduling algorithms are usually implemented in a simulation environment using tools that hide the complexity of the Grid and assumptions that are not always realistic. In our work, we describe the steps followed, the difficulties encountered and the solutions provided to develop and evaluate a scheduling policy, initially implemented in a simulation environment, in the gLite Grid middleware. Our focus is on a scheduling algorithm that allocates in a fair way the available resources among the requested users or jobs. During the actual implementation of this algorithm in gLite, we observed that the validity of the information used by the scheduler for its decisions affects greatly its performance. To improve the accuracy of this information, we developed an internal feedback mechanism that operates along with the scheduling algorithm. Also, a Grid computation resource cannot be shared concurrently between different users or jobs, making it difficult to provide actual fairness. For this reason we investigated the use of virtualization technology in the gLite middleware. We did a proof‐of‐concept implementation and performed an experimental evaluation of our scheduling algorithm in a small gLite testbed that proves the validity and applicability of our solutions. Copyright © 2012 John Wiley & Sons, Ltd.

ABSTRACT We propose a class of novel energy‐efficient multi‐cost routing algorithms for wireless mesh networks, and evaluate their performance. In multi‐cost routing, a vector of cost parameters is assigned to each network link, from which the cost vectors of candidate paths are calculated using appropriate operators. In the end these parameters are combined in various optimization functions, corresponding to different routing algorithms, for selecting the optimal path. We evaluate the performance of the proposed energy‐aware multi‐cost routing algorithms under two models. In the network evacuation model, the network starts with a number of packets that have to be transmitted and an amount of energy per node, and the objective is to serve the packets in the smallest number of steps, or serve as many packets as possible before the energy is depleted. In the dynamic one‐to‐one communication model, new data packets are generated continuously and nodes are capable of recharging their energy periodically, over an infinite time horizon, and we are interested in the maximum achievable steady‐state throughput, the packet delay, and the energy consumption. Our results show that energy‐aware multi‐cost routing increases the lifetime of the network and achieves better overall network performance than other approaches. Copyright © 2011 John Wiley & Sons, Ltd.

We consider the online impairment-aware routing and wavelength assignment (IA-RWA) problem in transparent WDM networks. To serve a new connection, the online algorithm, in addition to finding a route and a free wavelength (a lightpath), has to guarantee its transmission quality, which is affected by physical-layer impairments. Due to interference effects, the establishment of the new lightpath affects and is affected by the other lightpaths. We present two multicost algorithms that account for the actual current interference among lightpaths, as well as for other physical effects, performing a cross-layer optimization between the network and physical layers. In multicost routing, a vector of cost parameters is assigned to each link, from which the cost vectors of the paths are calculated. The first algorithm utilizes cost vectors consisting of impairment-generating source parameters, so as to be generic and applicable to different physical settings. These parameters are combined into a scalar cost that indirectly evaluates the quality of candidate lightpaths. The second algorithm uses specific physical-layer models to define noise variance-related cost parameters, so as to directly calculate theQ-factor of candidate lightpaths. The algorithms find a set of so-called nondominated paths to serve the connection in the sense that no path is better in the set with respect to all cost parameters. To select the lightpath, we propose various optimization functions that correspond to different IA-RWA algorithms. The proposed algorithms combine the strength of multicost optimization with low execution times, making them appropriate for serving online connections.

We consider routing and wavelength assignment (RWA) in a WDM network consisting of optical cross-connect (OXC) nodes that have color and direction constraints. These restricted node architectures have a smaller cost than the more flexible (and best performing) ones usually assumed in the RWA problem. This introduces an interesting tradeoff between the network performance achieved, in terms of network blocking and number of manual interventions required, and the cost of the node architecture used. In the process of comparing the node architectures, we propose an adaptation of an RWA algorithm that accounts for the lack of node flexibility, aiming to achieve using the constrained node architectures, performance similar to that obtained with the fully flexible node architectures. Additionally, we consider different transponder assignment policies and determine their effect on performance.

We consider path protection in the routing and wavelength assignment (RWA) problem for impairment constrained WDM optical networks. The proposed multicost RWA algorithms select the primary and the backup lightpaths by accounting for physical layer impairments. The backup lightpath may either be activated (1+1 protection) or it may be reserved and not activated, with activation taking place when/if needed (1:1 protection). In case of 1:1 protection the period of time where the quality of its transmission (QoT) is valid, despite the possible establishment of future connections, should be preserved, so as to be used in case the primary lightpath fails. We show that, by using the multicost approach for solving the RWA with protection problem, great benefits can be achieved both in terms of the connection blocking rate and in terms of the validity period of the backup lightpath. Moreover the multicost approach, by providing a set of candidate lightpaths for each source destination pair, instead of a single one, offers ease and flexibility in selecting the primary and the backup lightpaths.

We examine multi-player pervasive games that rely on the use of ad-hoc mobile sensor networks. The unique feature in such games is that players interact with each other and their surrounding environment by using movement and presence as a means of performing game-related actions, utilizing sensor devices. We briefly discuss the fundamental issues and challenges related to these type of games and the scenarios associated with them. We have also developed a framework, called Fun in Numbers (FinN) that handles a number of these issues, such as such as neighbors discovery, localization, synchronization and delay-tolerant communication. FinN is developed using Java and is based on a multilayer architecture, which provides developers with a set of templates and services for building and operating new games

We propose information aggregation as a method for summarizing the resource-related information, used by the task scheduler. Through this method the information of a set of resources can be uniformly represented, reducing at the same time the amount of information transferred in a Grid network. A number of techniques are described for aggregating the information of the resources belonging to a hierarchical Grid domain. This information includes the cpu and storage capacities at a site, the number of tasks queued, and other resource-related parameters. The quality of the aggregation scheme affects the efficiency of the schedulerpsilas decisions. We use as a metric of aggregation efficiency the Stretch Factor (SF), defined as the ratio of the task delay when the task is scheduled using complete resource information over the task delay when an aggregation scheme is used. The simulation experiments performed show that the proposed aggregation schemes achieve large information reduction, while enabling good task scheduling decisions as indicated by the SF achieved.

We describe our experiences from implementing and integrating a new job scheduling algorithm in the gLite Grid middleware and present experimental results that compare it to the existing gLite scheduling algorithms. It is the first time that gLite scheduling algorithms are put under test and compared with a new algorithm under the same conditions. We describe the problems that were encountered and solved, going from theory and simulations to practice and the actual implementation of our scheduling algorithm. In this work we also describe the steps one needs to follow in order to develop and test a new scheduling algorithm in gLite. We present the methodology followed and the testbed that was set up for the comparisons. Our research sheds light on some of the problems of the existing gLite scheduling algorithms and makes clear the need for the development of new.

We propose and evaluate an impairment-aware multi-parametric routing and wavelength assignment algorithm for online traffic in transparent optical networks. In such networks the signal quality of transmission degrades due to physical layer impairments. In the multi-parametric approach, a vector of cost parameters is assigned to each link, from which the cost vectors of candidate lightpaths are calculated. In the proposed scheme the cost vector includes impairment generating source parameters, such as the path length, the number of hops, the number of crosstalk sources and other inter-lightpath interfering parameters, so as to indirectly account for the physical layer effects. For a requested connection the algorithm calculates a set of candidate lightpaths, whose quality of transmission is validated using a function that combines the impairment generating parameters. For selecting the lightpath we propose and evaluate various optimization functions that correspond to different IA-RWA algorithms. Our performance results indicate that the proposed algorithms utilize efficiently the available resources and minimize the total accumulated signal degradation on the selected lightpaths, while having low execution times.

In this work, we propose an energy-efficient multicasting algorithm for wireless networks for the case where the transmission powers of the nodes are fixed. Our algorithm is based on the multicost approach and selects an optimal energy-efficient set of nodes for multicasting, taking into account: i) the node residual energies, ii) the transmission powers used by the nodes, and iii) the set of nodes covered. Our algorithm is optimal, in the sense that it can optimize any desired function of the total power consumed by the multicasting task and the minimum of the current residual energies of the nodes, provided that the optimization function is monotonic in each of these parameters. Our optimal algorithm has non-polynomial complexity, thus, we propose a relaxation producing a near-optimal solution in polynomial time. The performance results obtained show that the proposed algorithms outperform established solutions for energy-aware multicasting, with respect to both energy consumption and network lifetime. Moreover, it is shown that the near-optimal multicost algorithm obtains most of the performance benefits of the optimal multicost algorithm at a smaller computational overhead.

In this work we examine a task scheduling and data migration problem for grid networks, which we refer to as the data consolidation (DC) problem. DC arises when a task needs for its execution two or more pieces of data, possibly scattered throughout the grid network. In such a case, the scheduler and the data manager must select the data replicas to be used and the site where these will accumulate for the task to be executed. The policies for selecting the data replicas and the data consolidating site comprise the data consolidation problem. We propose and experimentally evaluate a number of DC techniques. Our simulation results brace our belief that DC is an important technique for data grids since it can substantially improve task delay, network load and other performance related parameters.

In this paper we present a platform for developing mobile, locative and collaborative distributed games comprised of small programmable object technologies (e.g., wireless sensor networks) and traditional networked processors. The platform is implemented using a combination of JAVA Standard and Mobile editions, targeting also mobile phones that have some kind of sensors installed. We briefly present the architecture of our platform and demonstrate its capabilities by reporting two pervasive multiplayer games. The key characteristic of these games is that players interact with each other and their surrounding environment by moving, running and gesturing as a means to perform game related actions, using small programmable object technologies.

In this work we present two mobile, locative and collaborative distributed games that are played using wireless sensor devices. We briefly present the architecture of the two games and demonstrate their capabilities. The key characteristic of these games is that players interact with each other and their surrounding environment by moving, running and gesturing as a means to perform game related actions, using sensor devices. We demonstrate our system's implementation, which uses a combination of JAVA Standard and Mobile editions.

Future grid networks should be able to provide quality of service (QoS) guarantees to their users. In this work we propose a framework for grid networks that provides deterministic delay guarantees to its guaranteed service (GS) users and best effort service to its best effort (BE) users. The proposed framework is theoretically and experimentally analyzed. We also define four types of computational resources based on the type of users (GS, BE) these resources serve and the priority they give them. We implement the proposed QoS framework for grids and verify that it not only satisfies the delay guarantees given to GS users, but also improves performance in terms of deadlines missed and resource use. In our simulations, data from a real grid network are used, validating in this way the appropriateness and usefulness of the proposed framework.

In this work we study the combination of multicost routing and variable transmission power in wireless ad-hoc networks. In multicost routing, each link is assigned a cost vector consisting of several parameters. These parameters are treated separately and are combined at the end of the algorithm using various optimization functions, corresponding to different routing schemes, for selecting the optimal path. The cost parameters we use are the hop count, the interference caused, the node residual energies, and the node transmission powers. We assume that nodes can use power control to adjust their transmission power to the desired level. The experiments conducted show that the combination of multicost routing and adjustable transmission power can lead to reduced interference and energy consumption, improving network performance and lifetime.

We study energy efficient routing strategies for wireless ad-hoc networks. In this kind of network, energy is a scarce resource and its conservation and efficient use is a major issue. Our strategy follows the multi-cost routing approach, according to which a cost vector of various parameters is assigned to each link. The parameters of interest are the number of hops on a path, and the residual energy and transmission power of the nodes on the path. These parameters are combined in various optimization functions, corresponding to different routing algorithms, for selecting the optimal path. We evaluate the routing algorithms proposed in a number of scenarios, with respect to energy consumption, throughput and other performance parameters of interest. From the experiments conducted, we conclude that routing algorithms that take into account energy related parameters, increase the lifetime of the network, while achieving better performance than other approaches, such as minimum hop routing.