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Panagiotis Kokkinos describes a case of acute feline paraparesis he saw when working as a rotating intern at the Centre of Small Animal Studies, Animal Health Trust.

A 20-month-old, British shorthair cat was presented for treatment of a ventral thoracic wall defect. Physical examination showed cleft sternum with supraumbilical body wall defect, an immediately palpable heart under the skin between the cleft halves and a unilateral forelimb ectrodactyly. Survey radiographs and sonographic examination revealed ectopia cordis associated with the sternal cleft, a supraumbilical abdominal hernia, ventral diaphragmatic defect and cardiac abnormalities. Radiography of the dysostotic limb confirmed ectrodactyly. Surgery involved reconstruction of the ventral body wall defects using a transversus abdominis muscle flap supported by an omental flap. Clinical, imaging and intraoperative findings supported the diagnosis of complete pentalogy of Cantrell. The cat recovered from surgery uneventfully and periodic follow-ups to 17 months after surgery confirmed a good outcome.

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 paper 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.

Networking is enabling a continuum comprising cloud, edge and last-mile systems. This continuum allows services and applications to join the fabric at any point and have access to processing power independently of the location of entrance. To enable this paradigm, we need to make sure we have a networking fabric capable of transporting and access network resources seamlessly and communication channels that are securitized, ensuring confidentiality and integrity of data. A challenge in securitization is that it takes computing resources away, and hence it may reduce the overall network performance. Hardware accelerations or offloads enable to reduce the burden on processing resources of securitization processes; in this contribution, we present how IPsec and TLS protocols provide a tool for a high level of securitization of the communication channels and approaches to accelerate/offload those protocols.

We are witnessing a wave of emerging cloud computing technologies and services that empower advanced applications from different vertical sectors, with diverse requirements. These trends give rise to a number of fundamental challenges that relate to the application deployment, the support of heterogeneous infrastructures and the provided security. In this setting, the SERRANO project steps in to define an intent-based paradigm of operating federated infrastructures consisting of edge, cloud and HPC resources, which will be realized through the SERRANO platform. Applications' high-level requirements will be translated to infrastructure-aware configuration parameters. SERRANO orchestration will then provide adaptive and efficient access to secure by design and accelerated resources. In this way, SERRANO will support cloud-native applications and services towards the cloud continuum.

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.

We present a self-configured and unified access and metro network architecture, named ARMONIA. The ARMONIA network monitors its status, and dynamically (re-)optimizes its configuration. ARMONIA leverages software defined networking (SDN) and network functions virtualization (NFV) technologies. These technologies enable the access and metro convergence and the joint and efficient control of the optical and the IP equipment used in these different network segments. Network monitoring information is collected and analyzed utilizing machine learning and big data analytics methods. Dynamic algorithms then decide how to adapt and dynamically optimize the unified network. The ARMONIA network enables unprecedented resource efficiency and provides advanced virtualization services, reducing the capital expenditures (CAPEX) and operating expenses (OPEX) and lowering the barriers for the introduction of new services. We demonstrate the benefits of the ARMONIA network in the context of dynamic resource provisioning of network slices. We observe significant spectrum and equipment savings when compared to static overprovisioning.

The number and size of megacities and their respective networking and computing infrastructures constantly increases, while a large part of the network traffic originates and terminates locally. In such a dense and heterogeneous environment, network slices are formed, utilizing various optical networking infrastructures that carry data from end users to distributed, intra-city edge computing units, in which applications' workload is appropriately offloaded. In our work, we examine mechanisms for the joint resource allocation and the applications' workload placement in the access and metro network segments.

Objective: To determine contamination rates of scrub suits worn by veterinary surgeons and nurses following a single shift. Methods: Cross sectional pilot study. Sterilised scrub suits were distributed to veterinary surgeons (n = 9) and nurses (n = 9) at the beginning of their clinical shift and worn for at least 8 hours, at a UK small animal referral center. Microbiological analysis of the scrub suits was conducted before and after home laundry at 30ºC. A questionnaire was distributed to hospital clinical staff regarding uniform habits.Results: Median bacterial counts of 47 (interquartile range [IQR] 14 to 162) and 7 (IQR 0 to 27) colony forming units per cm2 (cfu/cm2) were present before and after laundry of scrub suits (difference: -40 cfu/cm2, p = 0.025). Bacteria identified included: Staphylococcus sp., Enterococcus sp., Escherichia coli, Bacillus sp., Pseudomonas aeruginosa, Micrococcus sp., β-haemolytic Streptococci and a Group G Streptococcus. From 101 staff surveyed, 64.0% reported wearing fresh, clean scrub tops and 58.4% fresh, clean trousers each day, while 64.4% left the workplace wearing the same clothing in which they undertook clinical work. Conclusion: Workwear contamination risks spread of pathogens into the community and staff compliance with workplace guidelines warrants further attention. Home laundry at 30 ºC significantly decreases, but does not eliminate, the bacterial burden after a single shift. Clinical significance: The significant presence of bacteria on the uniforms after the shift ended necessitates further study, particularly regarding wearing health care uniforms outside of the work environment.

Today, home appliances like air-conditions, ovens, refrigerators, and washing machines, become more and more intelligent, enabling homeowners to monitor and manage them remotely. However, the plethora of different and usually proprietary interfaces provided by the manufacturers makes it difficult to build smart home and smart city applications that involve the concurrent remote management of multiple home appliances. In this work, we categorize the basic characteristics of the available intelligent home appliances and propose YANG models that describe these characteristics. NETCONF experimental demonstrations are carried out to display the control and management capabilities of the technologies applied.

The plethora, the diversity of Internet of Things (IoT) devices and the targeted multi-domain applications, have created a complex environment for new developers to create applications. Also, the introduction of science, technology, engineering, and mathematics (STEM) courses, starting from the elementary school, it is not just a trend anymore but an important part of the educational curriculum. Usually, however, the platforms used in these STEM courses, like Arduino, Raspberry Pi and others, lack any capabilities for central or distributed coordination between the involved devices. In our work, we present OpenMIC library for the development of multi-agent applications running in several devices, providing intelligence and computing capabilities. We discuss the architecture and the implementation details of the OpenMIC library and describe how new applications based on the OpenMIC library can be developed. An example application is also presented for the monitoring and the management of multiple IoT devices.

The current study investigated for the first time the occurrence of filarial infections in dogs in Cyprus. Blood samples of dogs from five districts of Cyprus (Lefkosia, Lemessos, Larnaka, Pafos and Ammochostos) were examined by the modified Knott's method and by serology, and the morphological classification of microfilariae was confirmed by molecular methods. A total of 200 dogs, 153 living in shelters and 47 owned dogs, were included in the study. Acanthocheilonema reconditum microfilariae were found in 9 (4.5%) samples and one (0.5%) sample was seropositive for D. immitis. No statistical significance was observed between microfilaraemic samples and the evaluated variables apart from the district (p = .005). The present study showed that dogs in Cyprus can be infected with blood circulating microfilariae and for the first time A. reconditum was reported as autochthonous infection in dogs in the country. No microfilariae of Dirofilaria spp. were detected. However, veterinarians should remain vigilant regarding Dirofilaria infections and should consider preventive protection to the animals, at least in case of travel in enzootic areas.

Hypervisors' smooth operation and efficient performance has an immediate effect in the supported Cloud services. We investigate scheduling algorithms that match I/O requests originated from virtual resources, to the physical CPUs that do the actual processing. We envisage a new paradigm of virtualized resource consolidation, where I/O resources required by several Virtual Machines (VMs) in different physical hosts, are provided by one (or more) external powerful dedicated appliance(s), namely the I/O Hypervisor (IOH). For this reason I/O operations are transferred from the VMs to the IOH, where they are executed. We propose and evaluate a number of scheduling algorithms for this hypervisor model, concentrating on providing guaranteed fairness among the virtual resources. A simulator has been built that describes this model and is used for the implementation and the evaluation of the algorithms. We also analyze the performance of the different hypervisor models and highlight the importance of fair scheduling.

We present a hierarchical orchestration platform for inter-domain datacenter networks that includes hybrid optical-electrical intra-datacenter networking and inter-datacenter networking utilizing elastic technologies. We demonstrate dynamic and joint allocation of capacity in an emulated testbed.

We present an admission control and solution validation-based online, routing, spectrum allocation approach for elastic optical networks. This is parameterized periodically by an optimal offline mechanism that drives the blocking of traffic requests, which if served would negatively affect network performance.

The recent developments in network programmability and flexibility, shape the environment for the creation and offering of networking capacity services. Through such services one will be able to reserve on demand raw network capacity and for a specific duration, in the form of virtual links or of virtual networks that interconnect branch offices, datacenters or devices around the world. In this work, we initially present the capacity services landscape as it is formulated and discuss on the relations with the cloud computing service model. Next, we propose optimal Integer Linear Programming (ILP) based mechanisms for matching network demands to networking capacity services, in the same way users’ computing requirements can be matched to virtual computing instances. We show through simulations that the proposed mechanisms can optimize the bandwidth usage, while minimizing the cost associated with the use of the network resources. We also investigate the effects of malleable capacity requests and of dynamic pricing.

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.

A 3-year-old female spayed dog was presented with a history of sanguineous vaginal discharge of 2 month duration. The dog was spayed 1.5 years before presentation. Clinical examination revealed a large, solid, ovoid mass in the caudal abdomen, recognized by diagnostic imaging as an enlargement at the top of the uterine stump. Additionally, high serum progesterone concentration was measured, confirming the presence of functional ovarian tissue in dioestrus. With a suspicion for a related uterine stump pyometra or less likely, for other enlargements, a cοeliotomy was performed. A mass at the apex of the uterine body and a right side ovarian remnant were found. Both structures and the remaining uterine stump were excised. The uterine remnant mass was histologically diagnosed as uterine adenocarcinoma, herein detected for the first time at the uterine stump in the bitch. Unsuccessful, incomplete ovariohysterectomy may permit even neoplastic transformation of uterine stump tissues.

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.

We discuss the basic algorithmic issues in planning and operating flexible optical networks, highlighting the challenges and differences from fixed-grid WDM networks. We also overview commercially available planning and operation tools, which (expectedly) lack support for flexible optical networks at the moment, as flexible technologies are not yet deployed in real networks. To fill this need we present the Mantis tool. Mantis' modular architecture provides fast running times, efficient and fault-tolerant usage of computation resources, and execution as both a desktop application and a cloud software-as-a-service.

We propose new burst assembly schemes and fast reservation (FR) protocols for Optical Burst Switched (OBS) networks that are based on traffic prediction. The burst assembly schemes aim at minimizing (for a given burst size) the average delay of the packets incurred during the burst assembly process, while the fast reservation protocols aim at further reducing the end-to-end delay of the data bursts. The burst assembly techniques use a linear prediction filter to estimate the number of packet arrivals at the ingress node in the following interval, and launch a new burst into the network when a certain criterion, different for each proposed scheme, is met. The fast reservation protocols use prediction filters to estimate the expected length of the burst and the time needed for the burst assembly process to complete. A Burst Header Packet (BHP) packet carrying these estimates is sent before the burst is completed, in order to reserve bandwidth at intermediate nodes for the time interval the burst is expected to pass from these nodes. Reducing the packet aggregation delay and the time required to perform the reservations, reduces the total time needed for a packet to be transported over an OBS network and is especially important for real-time applications. We evaluate the performance of the proposed burst assembly schemes and show that a number of them outperform the previously proposed timer-based, length-based and average delay-based burst assembly schemes. We also look at the performance of the fast reservation (FR) protocols in terms of the probability of successfully establishing the reservations required to transport the burst.

The present study investigated for the first time the occurrence of pulmonary and intestinal parasites of cats in Cyprus. Cats from five districts of Cyprus (Lefkosia, Lemesos, Larnaka, Pafos and Ammochostos) were examined by classical parasitological methods and the identity of lungworm larvae, whenever present, was confirmed by PCR-coupled sequencing. A total of 185 cats, 48 living exclusively indoors and 137 with outdoor access, were included in the study. Parasites were found in 66 (35.7%) of the examined cats, i.e. Toxocara cati (12%), Cystoisospora rivolta (12%), Joyeuxiella/Diplopylidium spp. (7%), Giardia spp. (6.5%), Troglostrongylus brevior (5%), Cystoisospora felis (2.5%), Aelurostrongylus abstrusus (2%), Taenia spp. (0.5%) and Dipylidium caninum (0.5%). Mixed infections were recorded in 18 cats. Cats that lived exclusively indoors or had received an antiparasitic treatment in the last 6 months were less likely to be infected (p

The availability of cloud applications and their respective server high availability is affected by inefficient load balancing configurations, denial of service attacks (DoS), link failures, power outages etc. As these issues may never disappear, traffic redirection from the affected server to a backup one is an important procedure followed for restoring the provided service. In this work, we initially evaluate a reverse proxy server-based scheme for traffic redirection, using High Availability proxy server(HAproxy) and also we develop a Software Defined Network (SDN)-based solution. We compare the proxy server-based and the SDN-based schemes, exhibiting how each can be used to mitigate downtime in case of a failure, providing high availability.

Dynamic optical networking allows operators to effectively maximize the capacity of their physical infrastructure and cope with the rapid growth rates of the Internet traffic. In the framework of the European DICONET project we proposed and developed a comprehensive solution that utilizes the dynamicity as well as the valuable physical layer information of a reconfigurable WDM core network to provide a smooth transition from the quasi-static networking of today to an intelligent reconfigurable and physical impairment-aware architecture. In this work we discuss the benefits of implementing the DICONET solution and present some of the major achievements of the project that support both the planning and operation phase of a core optical network.

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.

We consider information aggregation as a method for reducing the information exchanged in a Grid network and used by the resource manager in order to make scheduling decisions. In this way, information is summarized across nodes and sensitive or detailed information can be kept private, while resources are still publicly available for use. We present a general framework for information aggregation, trying to identify issues that relate to aggregation in Grids. In this context, we describe a number of techniques, including single point and intra-domain aggregation, define appropriate grid-specific domination relations and operators for aggregating static and dynamic resource information, and discuss resource selection optimization functions. The quality of an aggregation scheme is measured both by its effects on the efficiency of the scheduler's decisions and also by the reduction it brings on the amount of resource information recorded, a tradeoff that we examine in detail. Simulation experiments demonstrate that the proposed schemes achieve significant information reduction, either in the amount of information exchanged, or in the frequency of the updates, while at the same time maintaining most of the value of the original information as expressed by a stretch factor metric we introduce.

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 the Q-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.

The impact of physical layer impairments in the planning and operation of all-optical (and translucent) networks is the consideration of the DICONET project. The impairment-aware network planning and operation tool (NPOT) is the main outcome of the DICONET project, and is explained in detail in this paper. We describe the key building blocks of NPOT, consisting of the network description repositories, the physical layer performance evaluator, the impairment-aware routing and wavelength assignment (IA-RWA) engines, the component placement modules, the failure handling and the integration of NPOT in the control plane. Also, we present several experimental results for NPOT, evaluating the performance of its IA-RWA engines.

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 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 concurrently requests multiple pieces of data, possibly scattered throughout the grid network, that have to be present at a selected site before the task's execution starts. In such a case, the scheduler and the data manager must select (i) the data replicas to be used, (ii) the site where these data will be gathered for the task to be executed, and (iii) the routing paths to be followed; this is assuming that the selected datasets are transferred concurrently to the execution site. The algorithms or policies for selecting the data replicas, the data consolidating site and the corresponding paths comprise a Data Consolidation scheme. We propose and experimentally evaluate several DC schemes of polynomial number of operations that attempt to estimate the cost of the concurrent data transfers, to avoid congestion that may appear due to these transfers and to provide fault tolerance. Our simulation results strengthen our belief that DC is an important problem that needs to be addressed in the design of data grids, and can lead, if performed efficiently, to significant benefits in terms of task delay, network load and other performance parameters.

Hyperscale datacenters (DCs), spread in various locations around the globe, provide computing and storage resources for cloud and other applications. Elastic optical networks (both landline and subsea) operated by a single or multiple entities are used to form the inter-DC network and serve the constantly increasing traffic. In addition, optical switching technologies are researched for intra-DC networks as a means to achieve higher capacity and energy efficiency. Joint optimization and orchestration of inter-DC and intra-DC network infrastructures is the key for realizing the network virtualization and network as service visions. We present a hierarchical software defined networking orchestration platform that treats intra-DC and inter-DC networks as domains and cooperates with domain specific orchestrators/controllers to achieve interdomain, end-to-end orchestration. The systems developed are evaluated both in an emulated and in a realistic testbed, showcasing dynamic end-to-end path establishment functionality with dedicated capacity and low control overhead.

In this work we study the combination of multi-cost routing and adjustable transmission power in wireless ad hoc networks, so as to obtain dynamic energy- and interference-efficient routes to optimize network 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. Only at the end these parameters are combined in various optimization functions, corresponding to different routing algorithms, for selecting the optimal path. The multi-cost routing problem is a generalization of the multi-constrained problem, where no constraints exist, and is also significantly more powerful than single-cost routing. Since energy is an important limitation of wireless communications, the cost parameters considered are the number of hops, the interference caused, the residual energy and the transmission power of the nodes on the path; other parameters could also be included, as desired. 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 multi-cost routing and adjustable transmission power can lead to reduced interference and energy consumption, improving network performance and lifetime.

In translucent (or managed reach) WDM optical networks, regenerators are employed at specific nodes. Some of the connections in such networks are routed transparently, while others have to go through a sequence of 3R regenerators that serve as ?refueling stations? to restore their quality of transmission (QoT). We extend an online multicost algorithm for transparent networks presented in our previous study, to obtain an IA-RWA algorithm that works in translucent networks and makes use, when required, of the regenerators present at certain locations of the network. To characterize a path, the algorithm uses a multicost formulation with several cost parameters, including the set of available wavelengths, the length of the path, the number of regenerators used, and noise variance parameters that account for the physical layer impairments. Given a new connection request and the current utilization state of the network, the algorithm calculates a set of non dominated candidate paths, meaning that any path in this set is not inferior with respect to all cost parameters than any other path. This set consists of all the cost-effective (in terms of the domination relation) and feasible (in terms of QoT) lightpaths for the given source-destination pair, including all the possible combinations for the utilization of available regenerators of the network. An optimization function or policy is then applied to this set in order to select the optimal lightpath. Different optimization policies correspond to different IA-RWA algorithms. We propose and evaluate several optimization policies, such as the most used wavelength, the best quality of transmission, the least regeneration usage, or a combination of these rules. Our results indicate that in a translucent network the employed IA-RWA algorithm has to consider all problem parameters, namely, the QoT of the lightpaths, the utilization of wavelengths and the availability of regenerators, to efficiently serve the online traffic.

A key problem in Grid networks is how to efficiently manage the available infrastructure, in order to satisfy user requirements and maximize resource utilization. This is in large part influenced by the algorithms responsible for the routing of data and the scheduling of tasks. In this paper, we present several multi-cost algorithms for the joint scheduling of the communication and computation resources that will be used by a Grid task. We propose a multi-cost scheme of polynomial complexity that performs immediate reservations and selects the computation resource to execute the task and determines the path to route the input data. Furthermore, we introduce multi-cost algorithms that perform advance reservations and thus also find the starting times for the data transmission and the task execution. We initially present an optimal scheme of non-polynomial complexity and by appropriately pruning the set of candidate paths we also give a heuristic algorithm of polynomial complexity. Our performance results indicate that in a Grid network in which tasks are either CPU- or data-intensive (or both), it is beneficial for the scheduling algorithm to jointly consider the computational and communication problems. A comparison between immediate and advance reservation schemes shows the trade-offs with respect to task blocking probability, end-to-end delay and the complexity of the algorithms.

We present and theoretically and experimentally analyze a Quality of Service (QoS) framework for Grids that provides (i) deterministic delay bounds to Guaranteed Service (GS) users and (ii) fair sharing of resources to Best Effort (BE) users. The framework adopts concepts from Data Networks and applies them in the Grid environment. We initially describe the proposed framework assuming that task computational workloads are known (or can be estimated), and then provide extensions for the more realistic case where we have no a-priori knowledge of the task workloads. Task migration across multiple resources is also examined in this context. We also look at a specific implementation of the proposed QoS scheme, where we distinguish computational resources, based on the type of users (GS or BE) they serve and the priority they give to each type. We validate experimentally the proposed QoS framework for Grids, verifying that it satisfies the delay guarantees promised to GS users and provides fairness among BE users, while simultaneously improving performance in terms of deadlines missed and resource utilization. In our simulations, data from a real Grid Network are used.

Hypervisors' smooth operation and efficient performance has an immediate effect in the supported Cloud services. We investigate scheduling algorithms that match I/O requests originated from virtual resources, to the physical CPUs that do the actual processing. We envisage a new paradigm of virtualized resource consolidation, where I/O resources required by several Virtual Machines (VMs) in different physical hosts, are provided by one (or more) external powerful dedicated appliance(s), namely the I/O Hypervisor (IOH). For this reason I/O operations are transferred from the VMs to the IOH, where they are executed. We propose and evaluate a number of scheduling algorithms for this hypervisor model, concentrating on providing guaranteed fairness among the virtual resources. A simulator has been built that describes this model and is used for the implementation and the evaluation of the algorithms. We also analyze the performance of the different hypervisor models and highlight the importance of fair scheduling.

Background Reducing energy consumption and CO2 emissions in order to address climate change requires behavioral changes by the citizens, who will have to adopt more environmentally friendly and energy-saving practices. Methods We present a system and corresponding practices for gathering energy consumption data from public school buildings over the Internet, processing them to identify hidden correlations and produce actionable advice and presenting the results in real-time to its occupants over the Web. We describe the metering infrastructure installed at schools for energy consumption monitoring and the related actions carried out in order to motivate local school communities towards an environmentally friendly behavior. The introduction of IPv6 was found to be a key enabling technology for setting up such a system in a simple, secure, and efficient way. Results The proposed system and processes are put under test in a pilot installation composed of about 50 IPv6-enabled schools of the Greek School Network (GSN). These are also combined with appropriate educational and social engagement tools. In this way, energy consumption, operating costs, and greenhouse effects can be reduced in the area of educational and public administration buildings in general. Thus, people become almost instantly aware of the energy and environmental implications of their actions, which motivates them towards behavioral changes and the adoption of environmentally friendly practices. Conclusions The pilot provides a good example of collaboration between the ICT sector, the smart building and automation vendors, and the public authorities.

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.

An optical network, like any system, has to be observable before it can become subject to optimization, and this is the main capability that ORCHESTRA project introduces. ORCHESTRA's high observability will rely on information provided by the coherent transceivers that can be extended, almost for free, to operate as software defined optical performance monitors (soft-OPM). Novel digital signal processing (DSP) OPM algorithms will be developed and combined with a novel hierarchical monitoring plane, cross-layer optimization algorithms and active-control functionalities. ORCHESTRA vision is to close the control loop, enabling true network dynamicity and unprecedented network capacity efficiency.

Optical interconnection networks are being used in systems on chip, supercomputers, and datacenters, fueling exascale computing, big data, and artificial intelligence applications. The vertical cavity surface emitting laser (VCSEL) is a popular, mature, and cost-effective photonic transmitter technology that enables energy proportionality by allowing the links’ data rate and the associated power consumption to be adjusted. Our work assumes VCSEL-based optical interconnects and presents intelligent centralized and distributed mechanisms to jointly and optimally select the routes, the flow sizes, and the transmission powers needed to serve a given input traffic load, minimize the consumed energy, and optimize performance. For this purpose, we use a detailed VCSEL energy model and formulate the energy minimization problem as a constrained nonlinear multicommodity optimization problem, which is solved optimally with the proposed approaches. The simulation results, carried out under a variety of scenarios, show the efficiency of these methods in terms of throughput and energy consumption.