This tutorial offers a practical introduction to the fascinating world of quantum computation and its application to optimization and machine learning problems. The participants will acquire hands-on experience in developing hybrid "Variational Quantum Algorithms", which combine classical and quantum computation, and in running them both on simulators and real quantum hardware provided by leading ICT companies. As a concrete use-case, of specific interest for the HPDC community, the tutorial will discuss the optimal assignment and scheduling of resources on the different nodes and layers of a Cloud/Edge architecture, a problem that is known to have NP-hard complexity.

Modern Cloud/Edge architectures are composed of computing nodes belonging to multiple layers, including Cloud facilities, Edge/Fog nodes and sensors/actuators. In this paper, we present an architecture that includes also quantum computing devices, in two ways:

The management of thermal comfort in a building is a challenging and multifaced problem, because the use of objective parameters, for example, the energy consumption, should be combined with subjective requirements, related to human profile and preferences. This article exploits cognitive technologies, based on deep reinforcement learning (DRL), for the automatic control of the heating, ventilation, and air conditioning system in an office. The learning process is driven by a reward that includes multiple components, related to energy consumption, indoor temperature, and user perceptions, which are inferred by the human interactions with the system. This approach is inspired by the human-in-the-loop paradigm, which in our case helps the DRL controller to learn the requirements of users and readily adapt to them. Experimental results show that the appropriate balance of the reward components can be efficiently exploited to give the desired importance to the different objectives.

In a parallel computing scenario, the synchronization overhead, needed to coordinate the execution on the parallel computing nodes, can significantly impair the overall execution performance. Typically, synchronization is achieved by adopting a global synchronization schema involving all the nodes. In many application domains, though, a looser synchronization schema, namely, local synchronization, can be exploited, in which each node needs to synchronize only with a subset of the other nodes. In this work, we compare the performance of global and local synchronization using the efficiency, i.e., the ratio between the useful computing time and the total computing time, including the synchronization overhead, as a key performance indicator. We present an analytical study of the asymptotic behavior of the efficiency when the number of nodes increases. As an original contribution, we prove, using the Max-Plus algebra, that there is a non-zero lower bound on the efficiency in the case of local synchronization and we present a statistical procedure to find a value of this bound. This outcome marks a significant advantage of local synchronization with respect to global synchronization, for which the efficiency tends to zero when increasing the number of nodes.

The recent advances regarding the decentralization of renewable energy production, the new technologies involved in the management of smart grids, and the opening of national energy markets, enriched with the use of demand-response strategies, have led to a notable diffusion of local energy markets. A local energy market is defined as an aggregation of energy producers, consumers, and prosumers that are located in a restricted area and see an interest in joining together to form a so-called “energy district.” In this paper, we present a two-stage approach that enables sharing renewable energy within a district and minimizes the costs and/or maximizes the revenues deriving from the provision and the sale of energy, both for single prosumers and for the district as a whole. The main novelty with respect to the state-of-the-art is the introduction in the optimization process of a second stage that, starting from the energy exchanges determined in the first stage, redistributes to the prosumers the surplus energy, i.e., the energy produced locally that exceeds the demand of the prosumers. The two-stage approach benefits have been assessed in a real-life testbed deployed on an Italian university campus.

Power supply of big infrastructures is today a tremendous operational cost for providers and the expected growth of Internet traffic and services will lead to a further expansion of the computing and networking infrastructures and this, in its turn, raises also concerns in terms of sustainability. In this context, renewable energy generators can help to both reduce costs and alleviate the concerns of sustainability of big infrastructures. In this paper, we consider the case of Data Centers (DCs) composed of a few sites located in different geographical positions and powered with solar energy. Due to the intermittent nature of solar energy, different time zones and price of electricity in different locations, load management strategies are fundamental. We consider predictions of the solar energy production performed through Artificial Neural Networks and we assess the impact of predictions on load management decisions and, ultimately, on the DC performance.

This paper presents a novel approach for the management of buildings by leveraging cognitive technologies. The proposed approach exploits the Deep Reinforcement Learning paradigm to learn from both a physical and a simulated environment so as to optimize people comfort and energy consumption.

In the last few years, the use of Cloud and Internet of Things (IoT) technologies is dramatically growing, opening new application possibilities spanning from smart cities to the prevention of natural disasters. A flexible integration of the Cloud and IoT environments allows users and applications to combine the benefits of the two worlds. Specifically, robustness, versatility and high computation power are better supported by the Cloud, while better real-time adaptation and local control are peculiar characteristics of the IoT infrastructure. Even higher flexibility can emerge when the cloud layer combines and integrates a public cloud component, offered by an external provider, and a private cloud, managed internally to the organization, thus giving rise to the so-called hybrid cloud. The integration of a hybrid cloud with an IoT layer opens a wide range of new possibilities and applications but also presents heterogeneity and complexity issues that must be carefully addressed. This paper offers a contribution in this field by presenting a novel threelayered IoT architecture for the Hybrid Cloud, namely IoT-HC, which is devoted to enabling distributed sensing and actuation, controlling IoT networks, elaborating the collected data both at edge and cloud level, and deciding which data has to be sent to private or public cloud components for storage and high-level elaboration.

Privacy-preserving big data management and analytics is gaining the momentum within the research community, and several current research efforts aim to provide solutions to the challenges that emerge when models, techniques and algorithms must be delivered on top of massive, distributed big data repositories, especially with regards to emerging distributed settings such as Clouds and social networks. In this paper, at the convergence of the contexts of static and dynamic distributed environments, we provide a general overview of models, issues and approaches, along with some reference frameworks. Indeed, both static and dynamic distributed environments are relevant cases of settings where the privacy of big data turns to be critical. Finally, we discuss emerging research directions.

Summary Recent research efforts in the field of urban computing aim to develop innovative services for citizens through the application of ubiquitous and pervasive computing paradigms in urban spaces. Smart city applications need to cope with a large number of involved users and devices. Since data and objects are strictly related to the territory on which they are defined and used, it is preferable, when possible, to perform computation locally through the adoption of dispersed computing nodes such as CPU‐equipped sensors. In this context, the computation related to smart city applications can be profitably and efficiently parallelized by partitioning the territory into regions and assigning the computation related to each single region to a local node. Nevertheless, the adoption of parallel computing models poses several communication and synchronization issues, especially when the number of nodes is large and the time constraints of applications are compelling. This paper presents and analyzes a parallel computing model for smart city applications in which each node needs to exchange information only with a subset of neighbor nodes, allowing the synchronization overhead to be significantly reduced. As sample application, we consider the analysis and prediction of internet traffic generated by vehicle and pedestrian devices moving on a smart avenue equipped with distributed computing nodes. This work offers a detailed performance evaluation in a number of scenarios, including uniform and nonuniform user distribution and different types of user mobility behavior. The results show that the presented computation model offers notable advantages in terms of computation efficiency and speedup, with respect to a classical all–to‐all synchronization paradigm, in which the nodes need to coordinate with a central entity.

The performance and scalability of cellular automata, when executed on parallel/distributed machines, are limited by the necessity of synchronizing all the nodes at each time step, i.e., a node can execute its code only after all the other nodes have executed the previous step. However, if the code is parallelized by partitioning the space of the automata, these synchronization requirements can be relaxed: indeed, a node that manages a given portion of the cellular automata can execute a new step after synchronizing only with the nodes that manage the adjacent portions, while the remaining nodes can execute different time steps. This can be a notable advantage in many novel and increasingly popular applications of cellular automata, such as smart city applications, simulation of natural phenomena, etc., in which the execution times can be different and variable, due to the heterogeneity of machines and/or of the data and/or of the different functions. Indeed, a longer execution time at a node does not slow down the execution at all the other nodes but only at the neighboring nodes. This is particularly advantageous when the nodes that act as a bottleneck can vary during the execution. The goal of the paper is to analyze the benefits that can be achieved with the described approach when different space partitioning strategies are taken into account: i.e., the mono-and the two-dimensional partitioning. Experiments referred to a well-known cellular automata, namely the SciddicaS3-hex model for landslide simulation, exhibit good scalability and prove that the partitioning scheme adopted can result crucial for improving the overall computational performances.

The papers in this special section focus on green and energy efficient cloud computing. Cloud computing has had a huge commercial impact and has attracted the interest of the research community. Public clouds allow their customers to outsource the management of physical resources, and rent a variable amount of resources in accordance to their specific needs. Private clouds allow companies to manage on-premises resources, exploiting the capabilities offered by the cloud technologies, such as using virtualization to improve resource utilization and cloud software for resource management automation. Hybrid clouds, where private infrastructures are integrated and complemented by external resources, are becoming a common scenario as well, for example to manage load peaks. Cloud applications are hosted by data centers whose size ranges from tens to tens of thousands of servers, which raises significant challenges related to energy and cost management. It has been estimated that the Information and Communication Technology (ICT) industry alone is responsible for 2-3 percent of the global greenhouse gas emissions. Therefore, we must find innovative methods and tools to manage the energy efficiency and carbon footprint of data centers, so that they can operate and scale in a cost-effective and environmentally sustainable manner. These methods and tools are often categorized as Data Center Infrastructure Management (DCIM) to monitor, control, and optimize data centers with extensive automation. DCIM must also effectively manage the quality of service provided by the data center, since cloud customers require high reliability, availability, usability, and low response times.

Privacy and security of big data is emerging as one among the most relevant research challenges of recent years, also stirred-up by a wide family of critical applications ranging from scientific computing to social network analysis and mining, from data stream management to smart cities, and so forth. Traditionally, the issue of making (even very-large) databases private and secure has a long history in the context of encrypted databases but, when specifically considered in the Cloud setting, it poses new requirements and challenges to deal with, with particular regard to the scalability of solutions. In line with this emerging research trend, this paper focuses the attention on state-of-the-art proposals in the area of private databases over Clouds, and proposes critical comments about pros and cons of actual research efforts along with future research directions to be considered in future years.

This paper analyzes and evaluates the strategies and implications related to the execution of parallel algorithms on a distributed Cloud infrastructure, with the focus on an important class of applications for which the execution is performed on spatial data, dislocated on a bidimensional territory. Applications of interest cover a wide spectrum ranging from Internet of Things to social sciences, geology, swarm-inspired computation etc. The territory is partitioned into regions, and regions are assigned to parallel computational nodes to speed up the execution. Parallel nodes are aligned through the exchange of messages in order to ensure a coherent and efficient execution. The paper offers an analysis of the parallelization cost in this context, especially in terms of communication overhead, which is essential to estimate the impact of porting the computation onto a Cloud environment. More in particular, the paper evaluates two different strategies for space partitioning, i.e., linear partitioning and bidimensional partitioning, with a specific focus on scalability analysis, and compares the two strategies when both options are exploitable.

This article presents an approach for the efficient and transparent parallelization of a large class of swarm algorithms, specifically those where the multiagent paradigm is used to implement the functionalities of bioinspired entities, such as ants and birds. Parallelization is achieved by partitioning the space on which agents operate onto multiple regions and assigning each region to a different computing node. Data consistency and conflict issues, which can arise when several agents concurrently access shared data, are handled using a purposely developed notion of logical time. This approach enables a transparent porting onto parallel/distributed architectures, as the developer is only in charge of defining the behavior of the agents, without having to cope with issues related to parallel programming and performance optimization. The approach has been evaluated for a very popular swarm algorithm, the ant-based spatial clustering and sorting of items, and results show good performance and scalability.

The papers in this special section focus on green and energy efficient cloud computing. Cloud computing has had a huge commercial impact and has attracted the interest of the research community. Public clouds allow their customers to outsource the management of physical resources, and rent a variable amount of resources in accordance to their specific needs. Private clouds allow companies to manage on-premises resources, exploiting the capabilities offered by the cloud technologies, such as using virtualization to improve resource utilization and cloud software for resource management automation. Hybrid clouds, where private infrastructures are integrated and complemented by external resources, are becoming a common scenario as well, for example to manage load peaks.

Swarm intelligence algorithms, based on multi-agent systems, are often used to solve complex problems that are not affordable through classical centralized/deterministic solutions. In many cases, to enhance the performance of such algorithms, the computation can be distributed to parallel/distributed nodes, in accordance with different strategies. Specifically, parallelization can be achieved either by partitioning the space in which agents operate among the nodes, or by assigning the entire space to each node but distributing input data through a sampling approach. Another choice is whether or not the management of conflicts is needed to prevent possible loss of data consistency. This paper discusses such issues, while referring to two well-known types of swarm intelligence algorithms -- ants and flocking -- and compares the mentioned strategies, evaluating the performance results in terms of speedup.

Power efficiency is one of the main issues that will drive the design of data centers, especially of those devoted to provide Cloud computing services. In virtualized data centers, consolidation of Virtual Machines (VMs) on the minimum number of physical servers has been recognized as a very efficient approach, as this allows unloaded servers to be switched off or used to accommodate more load, which is clearly a cheaper alternative to buy more resources. The consolidation problem must be solved on multiple dimensions, since in modern data centers CPU is not the only critical resource: depending on the characteristics of the workload other resources, for example, RAM and bandwidth, can become the bottleneck. The problem is so complex that centralized and deterministic solutions are practically useless in large data centers with hundreds or thousands of servers. This paper presents ecoCloud, a self-organizing and adaptive approach for the consolidation of VMs on two resources, namely CPU and RAM. Decisions on the assignment and migration of VMs are driven by probabilistic processes and are based exclusively on local information, which makes the approach very simple to implement. Both a fluid-like mathematical model and experiments on a real data center show that the approach rapidly consolidates the workload, and CPU-bound and RAM-bound VMs are balanced, so that both resources are exploited efficiently.

This article presents an ant-based approach that enhances the flexibility, robustness and load balancing characteristics of structured P2P systems. Most notably, the approach allows peer indexes and resource keys to be defined on different and independent spaces, so that it overcomes the main limitation of standard structured P2P systems, that is, the need to assign each key to a peer having a specified index. This helps to improve load balancing, especially when the popularity distribution of resource keys is nonuniform, and enables the efficient execution of complex and range queries, which are essential in important types of distributed systems, for example, in Grids and Clouds. Beyond describing the general approach, this article focuses on the specific case of Self-CAN, a self-organizing P2P system that, while relying on the multidimensional structured organization of peers provided by CAN, exploits the operations of ant-based mobile agents to sort the resource keys and distribute them to peers. This system is particularly useful for the management and discovery of the resources that can be conveniently characterized by the values of several independent attributes.

This paper presents the design and the implementation of an architecture for the analysis of data streams in distributed environments. In particular, data stream analysis has been carried out for the computation of items and item sets that exceed a frequency threshold. The mining approach is hybrid, that is, frequent items are calculated with a single pass, using a sketch algorithm, while frequent item sets are calculated by a further multi-pass analysis. The architecture combines parallel and distributed processing to keep the pace with the rate of distributed data streams. In order to keep computation close to data, miners are distributed among the domains where data streams are generated. The paper also reports the experimental results obtained with a prototype of the architecture, tested on a Grid composed of two domains handling two different data streams.

The ICT market is experiencing an important shift from the request/provisioning of products toward a service-oriented view where everything (computing, storage, applications) is provided as a network-enabled service. It often happens that a solution to a problem cannot be offered by a single service, but by composing multiple basic services in a workflow. Service composition is indeed an important research topic that involves issues such as the design and execution of a workflow and the discovery of the component services on the network. This paper deals with the latter issue and presents an ant-inspired framework that facilitates collective discovery requests, issued to search a network for all the basic services that will compose a specific workflow. The idea is to reorganize the services so that the descriptors of services that are often used together are placed in neighbor peers. This helps a single query to find multiple basic services, which decreases the number of necessary queries and, consequently, lowers the search time and the network load.

Abstract Several classes of scientific and commercial applications require the execution of a large number of independent tasks. One highly successful and low‐cost mechanism for acquiring the necessary computing power for these applications is the ‘public‐resource computing’, or ‘desktop Grid’ paradigm, which exploits the computational power of private computers. So far, this paradigm has not been applied to data mining applications for two main reasons. First, it is not straightforward to decompose a data mining algorithm into truly independent sub‐tasks. Second, the large volume of the involved data makes it difficult to handle the communication costs of a parallel paradigm. This paper introduces a general framework for distributed data mining applications called Mining@home. In particular, we focus on one of the main data mining problems: the extraction of closed frequent itemsets from transactional databases. We show that it is possible to decompose this problem into independent tasks, which however need to share a large volume of the data. We thus introduce a data‐intensive computing network, which adopts a P2P topology based on super peers with caching capabilities, aiming to support the dissemination of large amounts of information. Finally, we evaluate the execution of a pattern extraction task on such network. Copyright © 2009 John Wiley & Sons, Ltd.

This paper presents “Self-Chord,” a peer-to-peer (P2P) system that inherits the ability of Chord-like structured systems for the construction and maintenance of an overlay of peers, but features enhanced functionalities deriving from ant-inspired algorithms, such as autonomous behavior, self-organization, and capacity to adapt to a changing environment. As opposed to the structured P2P systems deployed so far, resource indexing and placement is uncorrelated with network structure and topology, and resource keys are organized and managed by self-organizing mobile agents through simple local operations driven by probabilistic choices. Self-Chord has three main features that are particularly advantageous in Grid and Cloud Computing: 1) it is possible to give a semantic meaning to keys, which enables the execution of range queries; 2) the keys are fairly distributed over the peers, thus improving the balancing of storage responsibilities; 3) maintenance load is also limited because it is not necessary to reassign keys when new peers or resources are added to the system-the mobile agents will spontaneously reorganize the keys. The efficiency and effectiveness of Self-Chord were assessed both with a simulation framework and with an analytical model inspired by fluid dynamics.

This paper presents ldquoSelf-Chordrdquo, a bio-inspired P2P algorithm that can be profitably adopted to build the information service of distributed systems, in particular Computational Grids and Clouds. Self-Chord inherits the ability of Chord-like structured systems for the construction and maintenance of an overlay of peers, but features enhanced functionalities deriving from the activity of ant-inspired mobile agents, such as autonomy behavior, self-organization and capacity to adapt to a changing environment. Self-Chord features three main benefits with respect to classical P2P structured systems: (i) it is possible to give a semantic meaning to keys, which enables the execution of "class" queries, often issued in Grids and Clouds; (ii) the keys are fairly distributed over the peers, thus improving the balancing of storage responsibilities; (iii) maintenance load is reduced because, as new peers join the ring, the mobile agents will spontaneously reorganize the keys in logarithmic time.

Service-oriented Grid frameworks offer resources and facilities to support the design and execution of distributed applications in different domains, ranging from scientific applications and public computing projects to commercial and industrial applications. A critical issue in such a context is the management of the heterogeneity of resources and services offered by a Grid, including computers, data, and software tools provided by different organizations. This paper presents a general architecture of a service-oriented information system, which exploits the characteristics of a multi-domain and semantically enriched metadata model. The main objective of the information system is to uniformly manage service-oriented applications and basic resources by assuring metadata persistence through an XML distributed database, without merely relying on the functionalities of persistent Grid services. The information system has been implemented on the basic services of the WSRF-based Globus Toolkit 4 and its performance has been evaluated in a testbed.

This paper introduces Antares, which is a bio-inspired algorithm for the construction of a decentralized and self-organized P2P information system in computational grids. This algorithm exploits the properties ofantsystems, in which a number of entities/agents perform simple operations at the local level but together engender an advanced form of ldquoswarm intelligencerdquo at the global level. Here, the work of ant-inspired agents is tailored to the controlled replication and relocation of ldquodescriptors,rdquo that is, documents that contain metadata information about grid resources. Agents travel the grid through P2P interconnections, and replicate and spatially sort descriptors so as to accumulate those represented by identical or similar indexes into neighbor grid hosts. The resulting information system is here referred to asself-structured, because it exploits the self-organizing characteristics of ant-inspired agents, and the association of descriptors with hosts is not predetermined but adapts to the varying conditions of the grid. This self-structured organization combines the benefits of bothunstructuredandstructuredP2P information systems. Indeed, being basically unstructured, Antares is easy to maintain in a dynamic grid, in which joins and departs of hosts can be frequent events. On the other hand, the aggregation and spatial ordering of descriptors can improve the rapidity and effectiveness of discovery operations, which is a beneficial feature typical of structured systems. Performance analysis proves that ant operations allow the information system to be efficiently reorganized, thus improving the efficacy of both simple and range queries.

This article presents So-Grid, a set of bio-inspired algorithms tailored to the decentralized construction of a Grid information system that features adaptive and self-organization characteristics. Such algorithms exploit the properties of swarm systems, in which a number of entities/agents perform simple operations at the local level, but together engender an advanced form of swarm intelligence at the global level. In particular, So-Grid provides two main functionalities: logical reorganization of resources, inspired by the behavior of some species of ants and termites that move and collect items within their environment, and resource discovery, inspired by the mechanisms through which ants searching for food sources are able to follow the pheromone traces left by other ants. These functionalities are correlated, since an intelligent dissemination can facilitate discovery. In the Grid environment, a number of ant-like agents autonomously travel the Grid through P2P interconnections and use biased probability functions to: (i) replicate resource descriptors in order to favor resource discovery; (ii) collect resource descriptors with similar characteristics in nearby Grid hosts; (iii) foster the dissemination of descriptors corresponding to fresh (recently updated) resources and to resources having high quality of service (QoS) characteristics. Simulation analysis shows that the So-Grid replication algorithm is capable of reducing the entropy of the system and efficiently disseminating content. Moreover, as descriptors are progressively reorganized and replicated, the So-Grid discovery algorithm allows users to reach Grid hosts that store information about a larger number of useful resources in a shorter amount of time. The proposed approach features characteristics, including self-organization, scalability and adaptivity, which make it useful for a dynamic and partially unreliable distributed system.

Most currently deployed grids adopt a hierarchical model for their information system. However, nowadays the research and development community is heading towards the use of scalable models of information services based on decentralized approaches such as the peer-to-peer paradigm. This is mainly due to the poor scalability, resiliency and load-balancing features of the hierarchical model. This paper evaluates a resource discovery protocol exploitable in a hierarchical grid and compares it with a super-peer based model which has recently been introduced. Performance analysis, carried out through simulation, shows that the hierarchical model is valuable for small and medium sized grids, while the super-peer model is better suited for very large grids

This work proposes the modeling and the analysis through simulation of a multi-policy, application-level multicast protocol for the cooperative control of media streams transmitted by a multimedia server. The proposed protocol basically relies on a lower-level protocol incorporating a multicast-based coordination mechanism that reduces competition among clients for accessing a shared resource. Performance evaluation of the lower-level protocol was carried out on multicast control trees using a discrete-event simulator. Simulation results showed that the lower-level protocol provides higher performances than random floor control protocols which do not exploit coordination among clients.

This paper proposes the modeling and the performance analysis of a high-level control protocol - COCOP, which enables cooperative groups of clients to control a shared server delivering time-dependant data services. Several synchronous multimedia systems such as media on-demand, web casting, and networks of real/virtual sensors, can beneficially exploit COCOP to furnish cooperative control sessions. The protocol can be mapped onto a multicast transport support based either on IP-multicast or on an application level multicast infrastructure. An event-driven simulation framework is purposely customized and exploited to analyze the protocol performance and thus the dynamics of a cooperative control session over simple, yet representative topologies.

Knowledge management-based Web systems (KMbWS) are a novel class of Web information systems whose main goal is to adapt contents and presentations with respect to user needs and backgrounds through the execution of knowledge management processes. KMbWS involve complex issues such as knowledge representation, classification and clustering, reasoning, and, more recently, ontologies and semantic Web. In this paper we present a methodology for the designing and developing of KM-bWS, starting from the application domain analysis. We also provide a reference multi-layer architecture for KM-bWS. In our opinion, a KM-bWS can be considered as an "intelligent knowledge hub" because it makes distributed Web resources available by means of knowledge management techniques such as classification and clustering. Finally, we present a reference model for developing a KM-bMS authoring tool able to support our methodology.

In this paper, a new way to manage users having different mobility in a two-tier cellular system, through the use of the techniques of overlapping coverage and channel rearrangement, is provided. Overlapping coverage areas of nearby base stations arise in cellular communications systems, especially in small cell high-capacity microcellular configurations. With overlap, some users may have access to channels at more that one base station. This enhanced access can be used to improve teletraffic performance characteristics. Channel rearrangements are used to benefit users who are in range of only one base station. An analytical model is developed to determine performance measures.

This paper illustrates the development of an approximate analytical model for a communication network providing integrated services to a population of mobile users, and presents performance results to both validate the analytical approach, and assess the quality of the services offered to the end users. The analytical model is based on continuous-time multidimensional birth-death processes, and it is focused on just one of the cells in the network. The cellular system is assumed to provide three classes of service: the basic voice service, a data service with bit rate higher than the voice service and a multimedia service with one voice and one data component. In order to improve the overall network performance, some channels can be reserved to handovers, and multimedia calls that cannot complete a handover are decoupled, by transferring to the target cell only the voice component and suspending the data connection until a sufficient number of channels becomes free. Numerical results demonstrate the accuracy of the approximate model, as well as the effectiveness of the newly proposed multimedia call decoupling approach.

Future cellular systems are expected to use differently sized and shaped cells, depending on the area to be covered (urban or rural) and the user density. This paper proposes a cellular system that uses a two-tier cellular scheme, with macrocells and microcells. Mobile users are grouped into two classes, "slow" and "fast" users: to estimate user speed, a procedure is implemented, based on the evaluation of the amount of time a mobile station resides in a microcell. A channel assignment strategy, which tries to allocate "slow" users to the low tier (microcells) and "fast" users to the high tier (macrocells), is proposed: in this way, low mobility users undergo handovers at microcell boundaries, and high mobility users undergo handovers at macrocell boundaries. A simulation study is carried out to show that the handover rate, as well as the system signaling load, are kept at an acceptable level.

Future cellular systems are expected to use multilayered, multisized cells to cover non-homogeneous populated areas. An example in literature is given by a 2 level hierarchical architecture in which an overlaying macrocell provides a group of overflow channels utilized when a microcell, which covers a densely populated area, is not able to accommodate a new call, or a handover from another microcell. The macrocell has the higher hierarchical position, meaning that it can receive handover requests from microcells, lower in the hierarchy, as well as from other macrocells. On the contrary, a call served by the macrocell cannot handover to a microcell. This paper proposes a reversible hierarchical scheme characterized by the presence of handover attempts from macrocells to microcells. The scheme is conceived so that the microcells are given the majority of the traffic load as they are able to operate with very high capacity, while the macrocells, having lower channel utilization, can better carry out their support task. An analytical study is carried out showing that the system performance can be improved, at the expense of relatively little increase of network control overhead, when compared with the classical, i.e. nonreversible hierarchical scheme.