Multi-access edge computing (MEC) brings storage and computational capabilities at the edge of the network into so-called edge data centers (EDCs) to better support low-latency applications. In this paper, we tackle the problem of EDC deployment in urban environments. Previous research on mobile phone data has exposed a strong correlation between the demand for mobile communications and the urban tissue. For example, joint analysis of mobile data and vehicle traffic can be extrapolated to estimate demand for transportation and human activities, thereby inferring the land use of the area where such activities take place. Our work takes into account the mobility of citizens and their spatial patterns to estimate the optimal placement of MEC EDCs in urban environments, in order to minimize outages while guaranteeing energy-efficiency. This is achieved by modeling both the energy consumption attributed to network components (e.g., base stations) and computing components (e.g., servers). We propose and compare three heuristics and show that mobility-aware deployments achieve superior performance. The results are obtained with a custom-designed simulator able to operate over large-scale realistic urban environments.

Carsharing services have successfully established their presence and are now growing steadily in many cities around the globe. Carsharing helps to ease traffic congestion and reduce city pollution. To be efficient, carsharing fleet vehicles need to be located on city streets in high population density areas and considering demographics, parking restrictions, traffic and other relevant information in the area to satisfy travel demand. This work proposes to formulate the initial placement of a fleet of cars for a round-trip carsharing service as a multi-objective optimization problem. The performance of state-of-the-art metaheuristic algorithms, namely, SPEA2, NSGA-II, and NSGA-III, on this problem is evaluated on a novel benchmark composed of synthetic and real-world instances built from real demographic data and street network. Inverted generational distance (IGD), spread and hypervolume metrics are used to compare the algorithms. Our findings demonstrate that NSGA-II yields significantly lower IGD and higher hypervolume than the rest and SPEA2 has a significantly better diversity if compared with NSGA-II and NSGA-III.

Multi-access Edge Computing (MEC) brings storage and computational capabilities at the edge of the network into so-called Edge Data Centers (EDCs) to better low-latency applications. To this end, effective placement of EDCs in urban environments is key for proper load balance and to minimize outages. In this paper, we specifically tackle this problem. To fully understand how the computational demand of EDCs varies, it is fundamental to analyze the complex dynamics of cities. Our work takes into account the mobility of citizens and their spatial patterns to estimate the optimal placement of MEC EDCs in urban environments in order to minimize outages. To this end, we propose and compare two heuristics. In particular, we present the mobility-aware deployment algorithm (MDA) that outperforms approaches that do not consider citizens mobility. Simulations are conducted in Luxembourg City by extending the CrowdSenSim simulator and show that efficient EDCs placement significantly reduces outages.

Urban planning typically relies on experience-based solutions and traditional methodologies to face urbanization issues and investigate the complex dynamics of cities. Recently, novel data-driven approaches in urban computing have emerged for researchers and companies. They aim to address historical urbanization issues by exploiting sensing data gathered by mobile devices under the so-called mobile crowdsensing (MCS) paradigm. This work shows how to exploit sensing data to improve traditionally experience-based approaches for urban decisions. In particular, we apply widely known Machine Learning (ML) techniques to achieve highly accurate results in predicting categories of local businesses (LBs) (e.g., bars, restaurants), and their attractiveness in terms of classes of temporal demands (e.g., nightlife, business hours). The performance evaluation is conducted in Luxembourg city and the city of Munich with publicly available crowdsensed datasets. The results highlight that our approach does not only achieve high accuracy, but it also unveils important hidden features of the interaction of citizens and LBs.

The huge increase of population living in cities calls for a sustainable urban development. Mobile crowdsensing (MCS) leverages participation of active citizens to improve performance of existing sensing infrastructures. In typical MCS systems, sensing tasks are allocated and reported on individual-basis. In this paper, we investigate on collaboration among users for data delivery as it brings a number of benefits for both users and sensing campaign organizers and leads to better coordination and use of resources. By taking advantage from proximity, users can employ device-to-device (D2D) communications like Wi-Fi Direct that are more energy efficient than 3G/4G technology. In such scenario, once a group is set, one of its member is elected to be the owner and perform data forwarding to the collector. The efficiency of forming groups and electing suitable owners defines the efficiency of the whole collaborative-based system. This paper proposes three policies optimized for MCS that are compliant with current Android implementation of Wi-Fi Direct. The evaluation results, obtained using CrowdSenSim simulator, demonstrate that collaborative-based approaches outperform significantly individual-based approaches.

The unprecedented growth of the population living in urban environments calls for a rational and sustainable urban development. Smart cities can fill this gap by providing the citizens with high-quality services through efficient use of Information and Communication Technology (ICT). To this end, active citizen participation with mobile crowdsensing (MCS) techniques is a becoming common practice. As MCS systems require wide participation, the development of large scale real testbeds is often not feasible and simulations are the only alternative solution. Modeling the urban environment with high precision is a key ingredient to obtain effective results. However, currently existing tools like OpenStreetMap (OSM) fail to provide sufficient levels of details. In this paper, we apply a procedure to augment the precision (AOP) of the graph describing the street network provided by OSM. Additionally, we compare different mobility models that are synthetic and based on a realistic dataset originated from a well known MCS data collection campaign (ParticipAct). For the dataset, we propose two arrival models that determine the users' arrivals and match the experimental contact distribution. Finally, we assess the scalability of AOP for different cities, verify popular metrics for human mobility and the precision of different arrival models.

Street lighting is an essential community service, but current implementations are not energy efficient and and require municipalities to spend up to 40% of their allocated budget. In this paper, we propose heuristics and devise a comparison methodology for new smart lighting solutions in next generation smart cities. The proposed smart lighting techniques make use of Internet of Things (IoT) augmented lampposts, which save energy by turning off or dimming the light in the absence of citizens nearby. Assessing costs and benefits in adopting the new smart lighting solutions is a pillar step for municipalities to foster real implementation. For evaluation purposes, we have developed a custom simulator which allows the deployment of lampposts in realistic urban environments. The citizens travel on foot along the streets and trigger activation of the lampposts according to the proposed heuristics. For the city of Luxembourg, the results highlight that replacing all existing lamps with LEDs and dimming light intensity in the absence of users in the vicinity of the lampposts is convenient and provides an economical return already after the first year of deployment.

Summary Modern computing hardware has a very good task parallelism, but resource contention between tasks remains high. This renders large fractions of CPU time wasted and leads to application interference. Even tasks running on dedicated CPU cores can still incur interference from other tasks, most notably because of the caches and other hardware components shared by more than one core. The level of interference depends on the nature of executed tasks and is difficult to predict. A customer who has been granted that his task will run as if it were alone (e.g., a CPU core dedicated to a virtual machine), indeed suffers from significant performance degradation due to the time spent waiting for resources occupied by other tasks. Measuring actual performance of a task or a virtual machine can be difficult. However, even more challenging is estimating what the performance of the task should be if it were running completely in isolation. In this paper, we present a measurement techniqueFreeze'nSense. It is based on the hardware performance counters and allows measuring actual performance of a task and estimating performance as if the task were in isolation, all during runtime. To estimate performance in isolation, the proposed technique performs a short‐time freezing of the potentially interfering tasks.Freeze'nSenseintroduces lower than 1% overhead and is confirmed to provide accurate and reliable measurements. In practice,Freeze'nSensebecomes a valuable tool helping to automatically identify tasks that suffer the most in a shared environment and move them to a distant core. The observed performance improvement can be as large as 80–100% for individual tasks, and scale up to 15–20% for the computing node. Copyright © 2016 John Wiley & Sons, Ltd.

Cloud computing has become a de facto approach for service provisioning over the Internet. It operates relying on a pool of shared computing resources available on demand and usually hosted in data centers. Assessing performance and energy efficiency of data centers becomes fundamental. Industries use a number of metrics to assess efficiency and energy consumption of cloud computing systems, focusing mainly on the efficiency of IT equipment, cooling and power distribution systems. However, none of the existing metrics is precise enough to distinguish and analyze the performance of data center communication systems from IT equipment. This paper proposes a framework of new metrics able to assess performance and energy efficiency of cloud computing communication systems, processes and protocols. The proposed metrics have been evaluated for the most common data center architectures including fat tree three-tier, BCube, DCell and Hypercube.

State-of-the-art cloud simulators in use today are limited in the number of features they provide, lack real network communication models, and do not provide extensive Graphical User Interface (GUI) to support developers and researchers to extend the behavior of the cloud environment. We propose CloudNetSim++, a comprehensive packet level simulator that enables simulation of cloud environments. CloudNetSim++ can be used to evaluate a wide spectrum of cloud components, such as processing elements, storage, networking, Service Level Agreement (SLA), scheduling algorithms, fine grained energy consumption, and VM consolidation algorithms. CloudNetSim++ offers extendibility, which means that the developers and researchers can easily incorporate own algorithms for scheduling, workload consolidation, VM migration, and SLA agreement. The simulation environment of CloudNetSim++ offers a rich GUI that provides a high level view of distributed data centers connected with various network topologies. The package also includes an energy computation module that provides a fine grained analysis of energy consumed by each component. This paper shows the flexibility and effectiveness of CloudNetSim++ through experimental results demonstrated using real-world data center workloads. Moreover, to demonstrate the correctness of CloudNetSim++, we performed formal modeling, analysis, and verification using High-level Petri Nets, Satisfiability Modulo Theories (SMT), and Z3 solver.

Mobile crowd sensing received significant attention in the recent years and has become a popular paradigm for sensing. It operates relying on the rich set of built-in sensors equipped in mobile devices, such as smartphones, tablets, and wearable devices. To be effective, mobile crowd sensing systems require a large number of users to contribute data. While several studies focus on developing efficient incentive mechanisms to foster user participation, data collection policies still require investigation. In this paper, we propose a novel distributed and sustainable framework for gathering information in cloud-based mobile crowd sensing systems with opportunistic reporting. The proposed framework minimizes cost of both sensing and reporting, while maximizing the utility of data collection and, as a result, the quality of contributed information. Analytical and simulation results provide performance evaluation for the proposed framework by providing a fine-grained analysis of the energy consumed. The simulations, performed in a real urban environment and with a large number of participants, aim at verifying the performance and scalability of the proposed approach on a large scale under different user arrival patterns.

Smart cities exploit the most advanced information technologies to improve and add value to existing public services. Having citizens involved in the process through mobile crowdsensing (MCS) augments the capabilities of the platform without enquiring additional costs. In this paper, we propose a novel framework for data acquisition in MCS deployed over a fog computing platform which facilitates a number of key operations including user recruitment and task completion. Proper data acquisition minimizes the monetary expenditure the platform sustains to recruit and compensate users as well as the energy they spend to sense and deliver data. We propose a new user recruitment policy called Distance, Sociability, Energy (DSE). This policy exploits three criteria: (i) spatial distance between users and tasks, (ii) user sociability, which is an estimate of the willingness of users to contribute to sensing tasks, and (iii) remaining battery charge of the devices. Performance evaluation is conducted in a real urban environment for a large number of participants with new metrics assessing the efficiency of recruitment and the accuracy of task completion. Results reveal that the average number of recruited users improves by nearly 20 percent if compared to policies using only spatial distance as selection criterion.

Data centers provision industry and end users with the necessary computing and communication resources to access the vast majority of services online and on a pay-as-you-go basis. In this paper, we study the problem of energy proportionality in data center networks (DCNs). Devices are energy proportional when any increase of the load corresponds to a proportional increase of energy consumption. In data centers, energy consumption is concern as it considerably impacts on the operational expenses (OPEX) of the operators. In our analysis, we investigate the impact of three different allocation policies on the energy proportionality of computing and networking equipment for different DCNs, including 2-Tier, 3-Tier, and Jupiter topologies. For evaluation, the size of the DCNs varies to accommodate up to several thousands of computing servers. Validation of the analysis is conducted through simulations. We propose new metrics with the objective to characterize in a holistic manner the energy proportionality in data centers. The experiments unveil that, when consolidation policies are in place and regardless of the type of architecture, the size of the DCN plays a key role, i.e., larger DCNs containing thousands of servers are more energy proportional than small DCNs.

Cloud computing uses Internet data centers to host applications and data storage. Cloud computing resources and services are offered to customers on pay-per-use model while the quality of the offered resources and services are defined using service level agreements also known as SLAs. Unfortunately, there is no standard mechanism to verify and assure that services delivered by the cloud provider satisfy the SLA agreement in an automatic way. To fill this gap we propose a framework for SLA assurance, which can be used by both cloud providers and cloud users. The proposed framework assesses performance of cloud applications with and without introducing system and component failures and then helps to resolve or mitigate failures to assure the required quality of cloud applications. The evaluation results obtained through simulations and using testbed experiments demonstrate good agreement with the design objectives.

Cloud services providers deliver cloud services to cloud customers on pay-per-use model while the quality of the provided services are defined using service level agreements also known as SLAs. Unfortunately, there is no standard mechanism which exists to verify and assure that delivered services satisfy the signed SLA agreement in an automatic way. There is no guarantee in terms of quality. Those applications have many performance metrics. In this doctoral thesis, we propose a framework for SLA assurance, which can be used by both cloud providers and cloud users. Inside the proposed framework, we will define the performance metrics for the different applications. We will assess the applications performance in different testing environment to assure good services quality as mentioned in SLA. The proposed framework will be evaluated through simulations and using testbed experiments. After testing the applications performance by measuring the performance metrics, we will review the time correlations between those metrics.

The Internet of Things (IoT) paradigm makes the Internet more pervasive. IoT devices are objects equipped with computing, storage and sensing capabilities and they are interconnected with communication technologies. Smart cities exploit the most advanced information technologies to improve public services. For being effective, smart cities require a massive amount of data, typically gathered from sensors. The application of the IoT paradigm to smart cities is an excellent solution to build sustainable Information and Communication Technology (ICT) platforms and to produce a large amount of data following Sensing as a Service (S2aaS) business models. Having citizens involved in the process through mobile crowdsensing (MCS) techniques unleashes potential benefits as MCS augments the capabilities of existing sensing platforms. To this date, it remains an open challenge to quantify the costs the users sustain to contribute data with IoT devices such as the energy from the batteries and the amount of data generated at city-level. In this paper, we analyze existing solutions, we provide guidelines to design a large-scale urban level simulator and we present preliminary results from a prototype.

Saving power becomes one of the main objectives in information technology industry and research. Companies consume a lot of money in the shape of power consuming. Virtual Desktop Infrastructure (VDI) is a new shape of delivering operating systems remotely. Operating systems are executing in a cloud data center. Users desktops and applications can be accessed by using thin client devices. Thin client device is consisting of screen attached with small CPU. VDI has benefits in terms of cost reduction and energy saving. In this paper, we increase the power saved by Grinfy system. Without VDI, Grinfy can save at least 30% of energy consumption to its users companies. By integrating VDI in computing systems and using Grinfy, the power efficiency and saving can be improved and save more than 30%. The improving and increasing of energy saving features of VDI are also illustrated by experiment and will be integrated to Grinfy system to increase percentage of energy saved.

The Internet of Things (IoT) paradigm makes the Internet more pervasive, interconnecting objects of everyday life, and is a promising solution for the development of next-generation services. Smart cities exploit the most advanced information technologies to improve and add value to existing public services. Applying the IoT paradigm to smart cities is fundamental to build sustainable Information and Communication Technology (ICT) platforms. Having citizens involved in the process through mobile crowdsensing (MCS) techniques unleashes potential benefits as MCS augments the capabilities of the platform without additional costs. Recruitment of participants is a key challenge when MCS systems assign sensing tasks to the users. Proper recruitment both minimizes the cost and maximizes the return, such as the number and the accuracy of accomplished tasks. In this paper, we propose a novel user recruitment policy for data acquisition in mobile crowdsensing systems. The policy can be employed in two modes, namely sociability-driven mode and distance-based mode. Sociability stands for the willingness of users in contributing to sensing tasks. %Furthermore, we propose a novel metric to assess the efficiency of any recruitment policy in terms of the number of users contacted and the ones actually recruited. Performance evaluation, conducted in a real urban environment for a large number of participants, reveals the effectiveness of sociability-driven user recruitment as the average number of recruited users improves by at least a factor of two.

Incorporating energy efficiency into the design of modern communication systems has become an important area of research. However, while most of the proposed solutions are devoted to making network hardware energy efficient, very few works focus on energy efficiency as a fundamental design parameter of network protocols. This paper proposes an analytical model for energy consumption of TCP which relates energy consumption to protocol operation cycles. Based on this model a number of optimization techniques are proposed to reduce energy consumption of TCP. The experiments, performed using NS2 simulations, demonstrate that energy savings can be as high as 93% for multiple TCP flows.

Mobile cloud applications have become extremely popular in the last years. Location-based services, navigation, online gaming and social networking are a representative set of “always on” cloud applications in which the same or partially overlapping content is delivered to multiple users. Network coding is a well matching solution to improve content delivery. In this paper we propose the vNC-CELL technique, which uses network coding to combine information flows carrying the same or overlapping content that has to be delivered to co-located users. vNC-CELL executes coding functionalities in a mobile cloud through virtualization as these operations are computationally intensive if performed locally at the base station. Performance evaluation obtained from NS-3 simulations confirms vNC-CELL ability to improve network throughput and reduce download times for the users.

Power consumption is a primary concern for cloud computing data centers. Being the network one of the non-negligible contributors to energy consumption in data centers, several architectures have been designed with the goal of improving network performance and energy-efficiency. In this paper, we provide a comparison study of data center architectures, covering both classical two- and three-tier design and state-of-art ones as Jupiter, recently disclosed by Google. Specifically, we analyze the combined effect on the overall system performance of different power consumption profiles for the IT equipment and of different resource allocation policies. Our experiments, performed in small and large scale scenarios, unveil the ability of network-aware allocation policies in loading the the data center in a energy-proportional manner and the robustness of classical two- and three-tier design under network-oblivious allocation strategies.

The prosperous growth of the Internet-of-Things industry attracts numerous interests in employing edge clouds (a.k.a. cloudlets) to enhance the performance of mobile services and applications. Most existing research has been focused on offloading computational tasks from mobile devices to a single cloudlet or a central location, yet overlooked the issue of jointly coordinating the offloaded tasks in a system of multiple cloudlets. In this paper, we fill this gap by investigating the assignment and the scheduling of mobile computational tasks over multiple cloudlets, while optimizing the overall cost efficiency by leveraging the heterogeneity of cloudlets. We model both data transfer and computation in terms of monetary and time costs, with task deadlines guaranteed. We formulate the problem as a mixed integer program and prove its NP-hardness. By introducing admission control for the cloudlet provider to shape the system workload, we transform our problem into maximizing the task admission rate over the two coupled phases: data transfer and computation. We propose an efficient two-phase scheduling algorithm, and demonstrate that, compared with the conventional approach of always selecting the closest cloudlet, our approach achieves significantly higher admission rate with up to 20% reduction in the average cost of all offloaded tasks.

This work presents an on-line, energy- and communication-aware scheduling strategy for SaaS applications in data centers. The applications are composed of various services and represented as workflows. Each workflow consists of tasks related to each other by precedence constraints and represented by Directed Acyclic Graphs (DAGs). The proposed scheduling strategy combines advantages of state-of-the-art workflow scheduling strategies with energy-aware independent task scheduling approaches. The process of scheduling consists of two phases. In the first phase, virtual deadlines of individual tasks are set in the central scheduler. These deadlines are determined using a novel strategy that favors tasks which are less dependent on other tasks. During the second phase, tasks are dynamically assigned to computing servers based on the current load of network links and servers in a data center. The proposed approach, called Minimum Dependencies Energy-efficient DAG (MinD+ED) scheduling, has been implemented in the GreenCloud simulator. It outperforms other approaches in terms of energy efficiency, while keeping a satisfiable level of tardiness.

Cloud computing has become a de facto approach for service provisioning over the Internet. It operates relying on a pool of shared computing resources available on demand and usually hosted in data centers. Assessing performance and energy efficiency of data centers becomes fundamental. Industries use a number of metrics to assess efficiency and energy consumption of cloud computing systems, focusing mainly on the efficiency of IT equipment, cooling and power distribution systems. However, none of the existing metrics is precise enough to distinguish and analyze the performance of data center communication systems from IT equipment. This paper proposes a framework of new metrics able to assess performance and energy efficiency of cloud computing communication systems, processes and protocols. The proposed metrics have been evaluated for the most common data center architectures including fat-tree three-tier, BCube and DCell.

Heterogeneous architectures have become more popular and widespread in the recent years with the growing popularity of general-purpose processing on graphics processing units, low-power systems on a chip, multi- and many-core architectures, asymmetric cores, coprocessors, and solid-state drives. The design and management of cloud computing data-centers must adapt to these changes while targeting objectives of improving system performance, energy efficiency and reliability. This paper presents HEROS, a novel load balancing algorithm for energy-efficient resource allocation in heterogeneous systems. HEROS takes into account the heterogeneity of a system during the decision-making process and uses a holistic representation of the system. As a result, servers that contain resources of multiple types (computing, memory, storage and networking) and have varying internal structures of their components can be utilized more efficiently.

Wearable devices are becoming increasingly popular and are expected to become essential in our everyday life. Despite continuous improvement of hardware, the lifetime of mobile devices and their capabilities still remain a concern. Small size of batteries of smart watches, glasses, helmets and gloves limits the amount of computing, storage and communication resources. Mobile cloud computing can augment the capabilities of wearable devices by helping to execute some of the computing tasks in the cloud. Such computational offloading helps to preserve battery power at the cost of more intensive communications with the cloud. In this paper, we present a model and comprehensive analysis for computational offloading between wearable devices and clouds in realistic setups.

Cloud computing is a computing model where users access ICT services and resources without regard to where the services are hosted. Communication resources often become a bottleneck in service provisioning for many cloud applications. Therefore, data replication which brings data (e.g., databases) closer to data consumers (e.g., cloud applications) is seen as a promising solution. In this paper, we present models for energy consumption and bandwidth demand of database access in cloud computing datacenter. In addition, we propose an energy efficient replication strategy based on the proposed models, which results in improved Quality of Service (QoS) with reduced communication delays. The evaluation results obtained with extensive simulations help to unveil performance and energy efficiency tradeoffs and guide the design of future data replication solutions.

Data traffic from mobile devices experiences unprecedented growth, which current cellular network capacities cannot sustain. Traffic offloading to other type of networks, such as WiFi, can be used to reduce load in cellular networks. In this paper, we propose a novel solution, which unlike other existing methodologies, implements tight cooperation with the cellular network to optimize traffic offloading. The cellular network provides information about channel usage statistics, user mobility patterns, available resources and other parameters. The offloading decisions aim at optimizing the balance between user application requirements and availability of network resources. The validation results, obtained from NS-3 simulations, confirm effectiveness of the proposed solution in balancing cellular traffic load while ensuring QoS.

This paper analyzes the performance of a duty-cycled polling-based access mechanism that exploits the Transmission Opportunity Power Save Mode (TXOP PSM) defined in the IEEE 802.11ac to improve the energy efficiency of Wireless Local Area Networks (WLANs) based on the IEEE 802.11. The basic idea behind the proposed approach, named GreenPoll, is to enable contention free periods, based on polling with beacons, during which wireless stations can save energy by turning off their radio transceivers after exchanging data with the access point. The closed expression of energy efficiency of GreenPoll is formulated in this paper and is used to evaluate the performance of GreenPoll considering important parameters like the traffic load, packet length, data rate, and number of stations in the network. Both analytical and simulation results show the high energy efficiency of GreenPoll with gains of up to 330% and 110% when compared to the legacy Distributed Coordination Function (DCF) and the Point Coordination Function (PCF) defined in the IEEE 802.11, respectively.

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The popularity of cloud applications surged in the last years. Billions of mobile devices remain always connected. Location services, online games, social networking and navigation are a just few examples of "always on" cloud applications in which the same or partially overlapping content is delivered to multiple users. In this paper, we propose a technique, called NC-CELL, which uses network coding to foster content distribution in mobile cellular networks. Specifically, NC-CELL implements a software module at mobile base stations (or eNodeBs) which scans in transit traffic and looks for opportunities to code packets destined to different mobile users together. The proposed approach can significantly improve cell throughput and is particularly relevant for delay tolerant content distribution.

The review of the requirements of different cloud applications identified the need to consider communication processes explicitly and equally to the computing tasks. Following this observation, we propose a new communication-aware model for cloud computing applications, called CA-DAG. This model is based on Directed Acyclic Graphs (DAGs) that in addition to computing vertices include separate vertices to represent communications. Such a representation allows making separate resource allocation decisions, assigning processors to handle computing jobs and network resources for information transmissions, such as application database requests.

Management and optimization of cloud infrastructures combine multiple challenges. The optimization of data centers targets such objectives as performance, reliability, energy consumption, and security. To achieve these goals, multiple actions can be taken, for example, task and virtual machine allocation or infrastructure management. In this work we propose a model for representation of computing, memory, storage, and communication resources in cloud computing data centers. This model is relevant for the characterization of cloud applications, virtual machines, as well as physical servers. The performance evaluation and validation of the proposed model is carried out using the Green Cloud simulator. The obtained results show good agreement with the design objectives and confirm validity of the assumptions.

The IEEE 802.11 Distributed Coordination Function (DCF) is the fundamental access method providing asynchronous best-effort services in Wireless Local Area Networks (WLAN). In this standard, the currently employed Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) and the Binary Exponential Backoff (BEB) mechanism represent major sources of energy consumption at both the access point and mobile stations of a WLAN. To improve energy efficiency in WLANs, this paper introduces an enhanced DCF protocol incorporating bidirectional transmissions in combination with sleep periods, called Bidirectional Sleep DCF (BDSL-DCF). By following this new scheme, every successfully established connection between a sender and its intended destination can be used to exchange data, hence reducing control overhead and channel contention. Furthermore, this functionality allows those mobile stations not participating in data transmission to activate the sleep mode to conserve energy. Simulation results show that BDSL-DCF can outperform DCF in terms of energy efficiency and throughput, with negligible impact on packet transmission delay.

The energy consumption in data centers is drastically increasing and becoming a significant portion in the data center operating expenses. Enabling a sleep mode in the idle computing servers and network hardware is the most efficient method to avoid unnecessary power consumption. However, changes in the power modes introduce considerable delays. Moreover, inability to wake up a sleeping server immediately requires an availability of a pool of idle servers able to accommodate incoming load in the short term to prevent QoS degradation. In this paper we investigate the amount of computing servers and network hardware needed to accommodate different incoming load patters in the data centers. Furthermore, we propose to build these servers on energy efficient hardware, which is costly but can scale its power consumption with the offered load levels. The evaluation results show that the proposed methodology can save up to $750 per server per year on average.

The Point Coordination Function (PCF) of the IEEE 802.11 standard represents a well-known Medium Access Control (MAC) protocol providing Quality-of Service guarantees in Wireless Local Area Networks (WLANs). However, with the currently employed polling mechanism WLANs consume a significant amount of the energy resources from battery-powered user devices. To provide energy saving, an improved MAC protocol is presented in this paper, where bidirectional transmissions of fixed duration are incorporated into PCF in order to enable dynamic scheduling of real-time traffic. Based on this new strategy, wireless access points (APs) can estimate the proper duration of the Contention Free Period (CFP), in order to allow mobile stations to acknowledge any received data packet with a data packet equal to the received packet in size. Having this information, a mobile station, following the data exchange with the AP, can determine its wake-up timer and activate the sleep mode for the rest of the CFP interval. Comprehensive computer-based simulations demonstrate the feasibility of the proposed MAC improvements to achieve energy efficiency with negligible impact on packet delivery delay.

Future Internet will be highly heterogeneous supporting a multitude of access technologies with overlapping coverages. The automation and optimization of network operations like resource, mobility or QoS management in such a multi-access and multi-operator environment becomes a very challenging but vital task in order to ensure smooth network operation and user satisfaction. Cognitive network management is seen as the solution for this. However, any self* mechanism designed for small-scale networks and requiring an accurate view of the whole network status for decision-making will not be able to meet the future needs. In this paper, we propose a novel multi-access network management architecture targeted for large heterogeneous multi- access and multi-operator networks. The architecture introduces hierarchy to network management to ensure scalability. We also present results obtained from a case example of the proposed decision-making solution implemented to our cognitive network testbed.

Cloud computing data centers are becoming increasingly popular for the provisioning of computing resources. The cost and operating expenses of data centers have skyrocketed with the increase in computing capacity. Several governmental, industrial, and academic surveys indicate that the energy utilized by computing and communication units within a data center contributes to a considerable slice of the data center operational costs. In this paper, we present a simulation environment for energy-aware cloud computing data centers. Along with the workload distribution, the simulator is designed to capture details of the energy consumed by data center components (servers, switches, and links) as well as packet-level communication patterns in realistic setups. The simulation results obtained for two-tier, three- tier, and three-tier high-speed data center architectures demonstrate the effectiveness of the simulator in utilizing power management schema, such as voltage scaling, frequency scaling, and dynamic shutdown that are applied to the computing and networking components.

The evolution of network technologies brought increasing management complexity of networking infrastructure and protocols. Cognitive networking was introduced to deal with such complexity. This work presents a cognitive algorithm for cross-layer performance management which is the core of a decentralized framework for self-configuration of communication protocols. We illustrate the proposed solution for the joint reconfiguration of protocol parameters at different layers. The cognitive joint adaptation of TCP congestion window and MAC layer data rate is carried out as a proof of concept. Simulation results show performance improvement given by the proposed approach under changing network conditions.

Cognitive networks are becoming extremely popular in the network domain. This paper proposes a novel concept in cognitive network management and protocol configuration, where any protocol of the TCP/IP protocol reference model can be extended to dynamically tune its configuration parameters based on "immediate past" performance. The approach is focused on inter-node cognitive adaptation which is fostered by the proposed Cognitive Information Service (CIS). Performance evaluation results are obtained for cognitive adaptation of main TCP flow control parameters and show good agreement with design objectives.

In this paper, we introduce a novel queue management technique for the buffer space at the base station of infrastructure 802.11 networks that considers mobile user's receiving characteristics. The maximum amount of the base station buffer that can be used by a given flow is updated proportionally to RTT, measured at the mobile nodes and sent to the base station my the mean of link layer acknowledgements. In this way, the proposed scheme remains transparent to high-level protocols. The proposed approach makes possible the implementation of algorithms able to provide RTT-fairness in wired-cum-wireless networks. Results show the advantage of the proposed queue management scheme when compared to that of traditional drop-tail queue management.

Cooperative spectrum sensing for cognitive radio networks is recently being studied to simultaneously minimize uncertainty in primary user detection and solve hidden terminal problem. Sensing wideband spectrum is another challenging task for a single cognitive radio due to large sensing time required. In this paper, we introduce a technique to tackle both wideband and cooperative spectrum sensing tasks. We divide the wideband spectrum into several subbands. Then a group of cognitive radios is assigned for sensing of a particular narrow subband. A cognitive base station is used for collecting the results and making the final decision over the full spectrum. Our proposed algorithm minimizes time and amount of energy spent for wideband spectrum scanning by a cognitive radio, and effectively detects the primary users in the wideband spectrum thanks to cooperative shared spectrum sensing.

Automatic repeat request (ARQ) techniques employed by leading wireless technologies aim at compensating the high error rates due to radio impairments, but do not offer any differentiated levels of protection. In this work, we propose to enable per-packet differentiation of link layer ARQ protection (in terms of no. of retransmissions) driven by requirements of the end applications as well as of communication protocols implemented on the mobile terminal. Experimental results demonstrate the potential benefits deriving from the proposed strategy, both on TCP data flows and MPEG-4 video streams.

WiMAX is one of the most promising emerging broadband wireless technologies. As a consequence, data transfer performance optimization represents a crucial issue due to TCP limitations in wireless environment. In this study, we focus on the overhead deriving from the multilayer ARQ employed at the link and transport layers. To the aim of reducing unnecessary burden on the wireless link, we propose a cross-layer ARQ approach, called ARQ proxy, which substitutes the transmission of TCP ACK packet with a short MAC layer request on the radio link. Packet identification is achieved through the use of hash functions applied to the packet headers. Performance of the ARQ proxy is evaluated using an IEEE 802.16e system level simulator which includes realistic physical layer implementation. The results demonstrate good agreement with the design objectives and achieve the expected levels of system capacity increase, reduction of round trip time, and higher error rate tolerance.

3G long-term evolution (LTE) is a recent effort taken by cellular industries to step into wireless broadband market. The key enhancements target an introduction of new all- IP architecture, enhanced link layer and radio access with OFDM modulation and multiple antenna techniques. In this study, we focus on the overhead deriving from the multilayer ARQ employed at the link and transport layers. To the aim of reducing unnecessary burden on the wireless link, we propose a cross-layer ARQ approach, called ARQ Proxy, which substitutes the transmission of TCP ACK packet with a short MAC layer request on the radio link. Packet identification is achieved through association of a hash function to the raw packet data. Performance of the ARQ Proxy is evaluated using EURAE extensions for ns2 simulator. Results demonstrate significant improvements in terms of system capacity, TCP throughput performance, and higher tolerance to transmission errors.

This paper introduces a procedure called traffic engineering for grids for enabling grid networks to self-adjust to resource availability. The proposal is based on monitoring the state of resources and on task migration. It involves several layers of the Internet architecture. Experiments executed in NS-2 are used to illustrate the efficacy of the procedure proposed.

In this paper, mechanisms to enhance the performance of TCP Westwood+ in the presence of a large Bandwidth-Delay Product are studied. In particular, the employment of a logarithmic function for congestion window increase in the absence of packet losses is proposed. Extensive numerical simulations, carried out by using ns-2, show that the use of such logarithmic function can lead to relevant performance improvements with respect to standard Westwood+ protocol.

This paper presents a novel solution of bidirectional high speed communications for IP traffic transport over WDM networks. Based on the observation of the bidirectional nature of traffic in the Internet bidirectional light-trails are proposed as an extension of the natively unidirectional light-trail concept. Specification of a synchronous protocol enables fairness in medium access and data delivery improving reliability of communications and enabling QoS support in WDM networks

Wireless networks are becoming increasingly popular in the world of telecommunications. IEEE 802.11 standard provides reliable data delivery in wireless LANs. The cost for such reliability is the overhead related to the data transmission at the link layer. In this paper, the application of a packet concatenation algorithm at the IP layer is proposed to the purpose of overhead reduction together with increase of fairness in medium access at the link layer. The performance of the proposed algorithm is evaluated through simulations as well as real experiments. Results underline significant performance enhancements deriving from the use of the proposed packet concatenation algorithm in wireless LANs.

The growing importance of wireless networks requires an effort from the research community for the optimization of the transmission protocols, in order to achieve the best performance. This paper presents a novel approach for the performance enhancement of the most commonly used transport protocol (TCP) in a wireless environment. Cross-layer collaboration, achieved through the introduction of an ARQ snoop agent within the protocol stack of a wireless host, creates the possibility for the exploitation of link layer ARQ messages within the TCP acknowledgement scheme, with the aim of enhancement of the level of achievable throughput.