Fifth Generation (5G) networks and beyond are envisioned to provide user-focused communications, supporting diverse services with enhanced Quality of Service (QoS). Pivotal to this evolution is user equipment, which is increasingly performing advanced computational tasks beyond the edge of the network, known as the Extreme-Edge. Seamless integration of Extreme-Edge devices (EEDs) into the 5 G framework is however hindered, due to challenges in terms of device management, resource restrictions and interoperability issues. To address these barriers, we realize the Extreme-Edge Orchestrator (EEO), a management and orchestration framework enabling the extension of the 5 G cloud-to-edge continuum towards the Extreme-Edge. The EEO enables real-time resource monitoring and lifecycle management of network applications, including Artificial Intelligence/Machine Learning (AI/ML) tasks, deployed on EEDs. Unlike existing theoretical studies, our solution is deployed on an operational research-center-wide 5 G testbed and evaluated using an AI/ML-based network QoS prediction application, in the automotive domain. Our results show that the EEO supports efficient resource utilization, dynamic EED selection under device mobility scenarios and maintains robust service performance under computational stress-demonstrating its capability to support next-generation network services.

Telecommunications are undergoing a transformation due to the introduction of 5 G and the upcoming 6G technologies, which promise previously unheardof speeds, extremely low latency, and widespread access. The resource management of edge, fog, and cloud infrastructures is facing new difficulties as a result of these improvements. In order to optimize performance, cost-efficiency, and energy consumption, this paper investigates how machine learning (ML) might be used to improve resource allocation to services across this continuum, specifically in the context of B5G and 6G networks. In this work, we propose a reinforcement learning based algorithm using Q-learning for determining the best locations of services across the 6 G edge to cloud continuum. The aim is to ensure better QoS to the users and efficient use of network resources. To evaluate the performance of the proposed approach we compare it with a traditional state of the art algorithm in the context of content placement in CCNs, taking into consideration network related metrics such overall network traffic and delay. The experimental analysis shows that the proposed algorithm performs better than the traditional state of the art algorithm in most cases and reveals some insights on how network topological configurations affect the performance.

The advent of 5G and beyond systems is expected to shape the automotive vertical, as safety-critical vehicular applications rely on the network to meet their stringent Quality of Service (QoS) requirements. Predictive QoS (pQoS) has been proposed as a mechanism that allows automotive applications to proactively adapt in view of forthcoming QoS changes. Although pQoS is typically facilitated via classical (centralized) Machine Learning (ML) methods, the demand for data privacy has led to the emergence of distributed ML schemes. Efficient training of ML models however requires large volumes of (kinematic-state and connectivity) QoS data, so as to capture the involved spatio-temporal effects.To that end we hereby present and publicly share NordicDat, a QoS dataset collected during a two-week measurement campaign, driving across three European countries. NordicDat contains over 90K samples of physical layer, network and mobility-related features. Contrary to prior works, it includes multiple instances of cross-boarder roaming, diverse vehicle speed profiles and radio access technologies (generations). Further, we provide a thorough NordicDat data analysis, highlighting the dependencies between the NordicDat’s features and the resulting QoS values (throughput, delay). To showcase its broad usability, we train pQoS ML models over NordicDat in classical and distributed fashion. Our results demonstrate for the first time the viability of distributed pQoS with real-word data, which achieves similar (within a margin of 10%) accuracy to that of classical ML, cropping privacy-preserving benefits.

Edge computing is a recent paradigm where the processing takes place close to the data sources. It therefore reduces latency and saves bandwidth compared to traditional cloud computing. The latter can continue to play a supportive role. Edge-cloud computing provides benefits in many use cases including distributed computation algorithms, where the processing is divided into a number of tasks that are executed in parallel on different equipment. An important relevant challenge is to allocate the appropriate resources to process the data that are continuously generated from user devices. The issue becomes more complicated when we take into account the variations in the volume of the generated data as a function of time. In this paper we present a resource allocation algorithm for distributed computation with emphasis on machine learning algorithms. We consider that the resource requirements vary with time in a semi-static way that exhibits some daily pattern. We distinguish between periodic (expected) variations that occur during the day, and sporadic variations due to unexpected events. We propose an Integer Linear Programming algorithm to allocate the periodic resource requirements. To handle the non-periodic requirements, we consider a suitable prediction algorithm coupled with a reconfiguration algorithm that allocates the predicted required resources. Our results indicate that our proposal outperforms traditional allocation algorithms in terms of resource utilization, monetary cost and achieved accuracy.

Edge computing has emerged as a computing paradigm where the application and data processing takes place close to the end devices. It decreases the distances over which data transfers are made, offering reduced delay and fast speed of action for general data processing and store/retrieve jobs. The benefits of edge computing can also be reaped for distributed computation algorithms, where the cloud also plays an assistive role. In this context, an important challenge is to allocate the required resources at both edge and cloud to carry out the processing of data that are generated over a continuous (“infinite”) time horizon. This is a complex problem due to the variety of requirements (resource needs, accuracy, delay, etc.) that may be posed by each computation algorithm, as well as the heterogeneous resources’ features (e.g., processing, bandwidth). In this work, we develop a solution for serving weakly coupled general distributed algorithms, with emphasis on machine learning algorithms, at the edge and/or the cloud. We present a dual-objective Integer Linear Programming formulation that optimizes monetary cost and computation accuracy. We also introduce efficient heuristics to perform the resource allocation. We examine various distributed ML allocation scenarios using realistic parameters from actual vendors. We quantify trade-offs related to accuracy, performance and cost of edge/cloud bandwidth and processing resources. Our results indicate that among the many parameters of interest, the processing costs seem to play the most important role for the allocation decisions. Finally, we explore interesting interactions between target accuracy, monetary cost and delay.

Edge computing has emerged as a paradigm for local computing/processing tasks, reducing the distances over which data transfers are made. Thus, an opportunity is presented for data transfer-intensive, distributed machine learning. In this paper we develop a solution for serving distributed Machine Learning (ML) training jobs at the edge– cloud continuum. We model the specific requirements of each ML job, and the features of the edge and cloud resources. Next, we develop an Integer Linear Programming algorithm to perform the resource allocation. We examine different scenarios (different processing and bandwidth costs) and quantify tradeoffs related to performance and cost of edge/cloud bandwidth and processing resources. Our simulations indicate that even though there are many parameters that determine the allocation, the processing costs seem to play on average the most important role. The cloud b/w costs can be significant in certain scenarios. Finally, in certain examined cases, significant monetary benefits can be achieved through the collaboration of both edge and cloud resources when compared to using exclusively edge or cloud resources.

Edge computing has emerged as a new paradigm to bring cloud applications closer to users for increased performance. Unlike back-end cloud systems which consolidate their resources in a centralized data center location with virtually unlimited capacity, edge-clouds comprise distributed resources at various “computation spots”, each with very limited capacity. In this article, we consider Function-as-a-Service (FaaS) edge-clouds whereapplication providersdeploy their latency-critical functions to process user requests with strict response time deadlines. In this setting, we investigate the problem ofresource provisioningandallocation. After formulating the optimal solution, we propose resource allocation and provisioning algorithms across the spectrum of fully-centralized to fully-decentralized. We evaluate the performance of these algorithms in terms of their ability to utilize CPU resources and meet request deadlines under various system parameters. Our results indicate that practical decentralized strategies, which require no coordination among computation spots, achieve performance that is close to the optimal fully-centralized strategy with coordination overheads.

The convergence of Machine Learning (ML) with the edge computing paradigm has paved the way for distributing processing-heavy ML tasks to the network's extremes. As the edge deployment details still remain an open issue, distributed ML schemes tend to be network-agnostic; thus, their effect on the underlying network's resource consumption is largely ignored.In our work, assuming a network tree structure of varying size and edge computing characteristics, we introduce an analytical system model based on credible real-world measurements to capture the end-to-end consumption of ML schemes. In this context, we employ an edge-based (EL) and a federated (FL) ML scheme and in-depth compare their bandwidth needs and energy footprint against a cloud-based (CL) baseline approach. Our numerical evaluation suggests that EL exhibits a minimum of 25% bandwidth-efficiency compared to CL and FL, if employed by a few nodes higher in the edge network, while halving the network's energy costs.

The proliferation of machine learning (ML) applications has lately witnessed a considerable shift to more distributed settings, even reaching hand-held mobile devices; there, contrary to typical Centralized learning (CL) whereby the involved (large amounts of) training data are centrally gathered to train models, the load of training tasks is distributed across a set of capable mobile learners at the expense of their own energy. The idea of Federated learning (FL) has emerged as a privacy-preserving mechanism suggesting that the ML model parameters rather than data, are sent over the network to a central point of aggregation. However, when relaxing the privacy concerns, the debate strongly relates to the available network resources. Interestingly, the sofar theoretical or even experimental comparison of the two approaches overlooks network conditions and remains of low realism. In this work we rely on past measurement studies to introduce a realistic system model that accounts for all involved mobile network conditions such as bandwidth and data availability (af-fecting training accuracy and model aggregation) as well as user mobility patterns (affecting data loss). A dedicated simulation framework we have developed replays rich mobile-traces allowing for a comprehensive comparison of the two ML approaches over a large set of training data shedding light on network-resources utilization, energy efficiency and training convergence. Intuitively, our results suggest that the ratio between the employed raw data and the corresponding ML model shapes the conditions under which FL acts as a network-efficient alternative to CL. Interestingly enough, asymmetry in data availability across users as well as their varying number are shown to hardly affect the FL approach in traffic and energy needs, pointing both to its promising potential and the need for further research.

The proliferation of mobile devices with ubiquitous Internet access has made content access patterns highly volatile and spatio- temporally varying. At the same time, virtualization techniques have enabled the emergence of virtual Content Delivery Network (vCDN) providers, that bundle together a virtual infrastructure by utilizing storage resources anywhere along the path between the access network and the corresponding data center, where the requested content actually resides. In this context, efficient content placement in the various storage layers depends on accurately estimating content access patterns from the different access networks at any given time. At the same time, different vCDN providers compete against each other to provide low content retrieval latency to their users. However, there are some interesting synergies that might emerge among otherwise competing vCDN providers: (i) host content of another vCDN provider and, (ii) provide own content to the users/customers of other vCDN providers. In this paper, we formulate the overall problem of content placement for multiple vCDN providers that employ collaboration as an overall social-welfare maximization problem. The solution to this problem gives the optimal placement, achievable only in the case of full information. Alleviating the need for full information and considering vCDN providers separately as profit seekers, we also devise a distributed algorithm for content placement by exchanging limited information among them. Extensive simulation experiments show that business collaboration among competing vCDN providers is beneficial, as compared to isolated offerings, and allows them to adapt faster to content pattern changes.

When a disastrous event occurs and an urban area suffers from collapsed buildings, Urban Search and Rescue (USaR) teams require a fast and thorough building damage assessment, to focus their rescue efforts accordingly. After the evaluation process, their observations/results have to be transferred to their base (center of operation - COP) for further assessment and orchestration of the survival extraction actions. In this work, a communication scheme that can be used in emergency situations is introduced. The proposed solution doesn't depend on existing functional cellular networks such as GSM, 3G, 4G, etc., neither assumes Wi-Fi connectivity to the Internet, since in disaster scenarios they could be unavailable or they could have large interruptions and delays. On the contrary, we propose a low-cost solution that is easy to build and deploy and is modular to expand from a small area to a large part of an urban setup, based on the needs of the USaR activities. Particularly, a wireless mesh-network is designed to cover the needs of the first responders in the actual disaster area, whereas a long-range wireless network is designed to transfer the data securely back and forth to the center of operation.

We argue that data communications in dynamic and potentially fragmented networks should not and cannot rely on network-centric resilience schemes, as is the case in today's networks, but should take advantage of techniques that focus on information-centric resilience. We make the case that management and control in disruptive environments should take advantage of information-centricity, rather than focus on node-oriented path recovery routing. This is also essential in the information-centric networking (ICN) paradigm, which is by nature oblivious to network locations. In this context, we build on ICN and enhance the named data networking (NDN) architecture with extra functionality in order to make it resilient to network failures. We introduce an extra interest management routing table, which we call the “satisfied interest table” (SIT) and which points to the direction of already satisfied interests. This way, upon failure of links/nodes toward the content origin, the SIT table can redirect interests toward caches and end-users that have recently received the requested content. Our extensive performance evaluation shows that our simple, yet efficient information resilience scheme can serve most requests made after disruptive effects, e.g., natural disasters, where users are interested in latest updates, dissemination of warnings from first responders and evacuation plans. More generally, we believe that our proposed approach should become part of the main NDN architecture as it can support service resilience in the case of network failures.

Edge computing has emerged as a new paradigm to bring cloud applications closer to users for increased performance. ISPs have the opportunity to deploy private edge-clouds in their infrastructure to generate additional revenue by providing ultra-low latency applications to local users. We envision a rapid increase in the number of such applications for “edge” networks in the near future with virtual/augmented reality (VR/AR), networked gaming, wearable cognitive assistance, autonomous driving and IoT analytics having already been proposed for edge- clouds instead of the central clouds to improve performance. This raises new challenges as the complexity of the resource allocation problem for multiple services with latency deadlines (i.e., which service to place at which node of the edge-cloud in order to satisfy the latency constraints) becomes significant. In this paper, we propose a set of practical, uncoordinated strategies for service placement in edge-clouds. Through extensive simulations using both synthetic and real-world trace data, we demonstrate that uncoordinated strategies can perform comparatively well with the optimal placement solution, which satisfies the maximum amount of user requests.

The vast majority of mobile data transfers today follow the traditional client-server model. Although in the fixed network P2P approaches have been exploited and shown to be very efficient, in the mobile domain there has been limited attempt to leverage on P2P (D2D) for large-scale content distribution (i.e., not DTN-like, point-to-point message transfers). In this paper, we explore the potential of a user-operated, smartphone-centric content distribution model for smartphone applications. In particular, we assume source nodes that are updated directly from the content provider (e.g., BBC, CNN), whenever updates are available; destination nodes are then directly updated by source nodes in a D2D manner. We leverage on sophisticated information-aware and application-centric connectivity techniques to distribute content between mobile devices in densely-populated urban environments. Our target is to investigate the feasibility of an opportunistic content distribution network in an attempt to achieve widespread distribution of heavy content (e.g., video files) to the majority of the destination nodes. We propose ubiCDN as a ubiquitous, user-operated and distributed CDN for mobile applications.

Recent research in Information-Centric Networks has considered various approaches for discovering content in the cache-enabled nodes of the network. Such approaches include scoped flooding and deploying a control plane protocol to disseminate the cache contents in the network, to name a few. In this work, we consider an opportunistic approach that uses trails left behind by data packets from the content origin to the sources in order to discover off-path cached content. We evaluate our approach using an ISP topology for various system parameters. We propose two new forwarding strategies built on top of our approach. Our results indicate that the opportunistic discovery mechanism can significantly increase cache hit rate compared to NDN's default forwarding strategy, while limiting the overhead at acceptable levels.

Content delivery networks (CDNs) have been the prevalent method for the efficient delivery of content across the Internet. Management operations performed by CDNs are usually applied only based on limited information about Internet Service Provider (ISP) networks, which can have a negative impact on the utilization of ISP resources. To overcome these issues, previous research efforts have been investigating ISP-operated content delivery services, by which an ISP can deploy its own in-network caching infrastructure and implement its own cache management strategies. In this paper, we extend our previous work on ISP-operated content distribution and develop a novel scalable and efficient distributed approach to control the placement of content in the available caching points. The proposed approach relies on parallelizing the decision-making process and the use of network partitioning to cluster the distributed decision-making points, which enables fast reconfiguration and limits the volume of information required to take reconfiguration decisions. We evaluate the performance of our approach based on a wide range of parameters. The results demonstrate that the proposed solution can outperform previous approaches in terms of management overhead and complexity while offering similar network and caching performance.

The landscape toward 5G wireless communication is currently unclear, and, despite the efforts of academia and industry in evolving traditional cellular networks, the enabling technology for 5G is still obscure. This paper puts forward a network paradigm toward next-generation cellular networks, targeting to satisfy the explosive demand for mobile data while minimizing energy expenditures. The paradigm builds on two principles; namely caching and multicast. On one hand, caching policies disperse popular content files at the wireless edge, e.g., pico-cells and femto-cells, hence shortening the distance between content and requester. On other hand, due to the broadcast nature of wireless medium, requests for identical files occurring at nearby times are aggregated and served through a common multicast stream. To better exploit the available cache space, caching policies are optimized based on multicast transmissions. We show that the multicast-aware caching problem is NP-hard and develop solutions with performance guarantees using randomized-rounding techniques. Trace-driven numerical results show that in the presence of massive demand for delay tolerant content, combining caching and multicast can indeed reduce energy costs. The gains over existing caching schemes are 19% when users tolerate delay of three minutes, increasing further with the steepness of content access pattern.

Caching at the network edge is considered a promising solution for addressing the ever-increasing traffic demand of mobile devices. The problem of proactive content replication in hierarchical cache networks, which consist of both network edge and core network caches, is considered in this paper. This problem arises because network service providers wish to efficiently distribute content so that user-perceived performance is maximized. Nevertheless, current high-complexity replication algorithms are impractical due to the vast number of involved content items. Clustering algorithms inspired from machine learning can be leveraged to simplify content replication and reduce its complexity. Specifically, similar items could be clustered together, e.g., according to their popularity in space and time. Replication on a cluster-level is a problem of substantially smaller dimensionality, but it may result in suboptimal decisions compared to item-level replication. The factors that cause performance loss are identified and a clustering scheme that addresses the specific challenges of content replication is devised. Extensive numerical evaluations, based on realistic traffic data, demonstrate that for reasonable cluster sizes the impact on actual performance is negligible.

We investigate an information-resilience scheme in the context of Content-Centric Networks (CCN) for the retrieval of content in disruptive, fragmented networks cases. To resolve and fetch content when the origin is not available due to fragmentation, we exploit content cached both in in-network caches and in end-users' devices. Initially, we present the required modifications in the CCN architecture to support the proposed resilience scheme. We also present the family of policies that enable the retrieval of cached content and we derive an analytical expression/lower bound of the probability that an information item will disappear from the network (be absorbed) and the time to absorption when the origin of the item is not reachable. Extensive simulations indicate that the proposed resilience scheme is a valid tool for the retrieval of cached content in disruptive scenarios, since it allows the retrieval of content for a long period after the fragmentation of the network and the “disappearance” of the content origin.

Cloud service provisioning on top of virtual infrastructures is of major importance in modern ICT, since it is directly correlated to the way business models are designed and revenue is generated from the cloud service providers. In this work we examine an end-to-end content replication problem over cloud-based multi-technology infrastructures. We extend the classical model where every network node is a potential replica carrier and the link weights represent hops/delay and we examine replication schemes for content that a) is requested by customers belonging in different virtual networks and b) depending on the requester there is different impact on the system operational cost. We examine both centralized and distributed content replication management policies and we evaluate their performance through extended simulations, by means of total cost, the number of object replacements and the number of iterations required.

Recently, network coding has emerged as an effective way to increase the efficiency of the content placement in the caching networks and thus boost the content delivery to the requesters. Its superiority compared to network coding-agnostic caching schemes lies on the increased availability of the content, which can be extracted by the requesters after they receive and decode a sufficiently large amount of encoded data. Although the topics surrounding network coding and caching have been already studied in the previous literature, their potential on enhancing mobile content delivery has not been fully explored yet. Namely, most of the existing works restrict the encoded data combinations to involve only parts of the same file, since this guarantees a low number of choices and thus simplifies the analysis. In this work, we study the problem of caching linear combinations of different files in a small-cell network. Our goal is to mitigate the pressure on the macrocellular base station by serving as many as possible content requests by the cache-endowed small-cell base stations that are deployed in the cell. Because of the NP-hardness of this problem, we propose a heuristic algorithm that gradually increases the performance of the obtained solution. Numerical results for typical popularity distributions reveal the performance benefits of our approach.

Content distribution in the Internet places content providers in a dominant position, with delivery happening directly between two end-points, that is, from content providers to consumers. Information-Centrism has been proposed as a paradigm shift from the host-to-host Internet to a host-to-content one, or in other words from an end-to-end communication system to a native distribution network. This trend has attracted the attention of the research community, which has argued that content, instead of end-points, must be at the center stage of attention. Given this emergence of information-centric solutions, the relevant management needs in terms of performance have not been adequately addressed, yet they are absolutely essential for relevant network operations and crucial for the information-centric approaches to succeed. Performance management and traffic engineering approaches are also required to control routing, to configure the logic for replacement policies in caches and to control decisions where to cache, for instance. Therefore, there is an urgent need to manage information-centric resources and in fact to constitute their missing management and control plane which is essential for their success as clean-slate technologies. In this thesis we aim to provide solutions to crucial problems that remain, such as the management of information-centric approaches which has not yet been addressed, focusing on the key aspect of route and cache management.

The deployment of small cells is expected to gain huge momentum in the near future, as a solution for managing the skyrocketing mobile data demand growth. Local caching of popular files at the small cell base stations has been recently proposed, aiming at reducing the traffic incurred when transferring the requested content from the core network to the users. In this paper, we propose and analyze a novel caching approach that can achieve significantly lower traffic compared to the traditional caching schemes. Our cache design policy carefully takes into account the fact that an operator can serve the requests for the same file that happen at nearby times via a single multicast transmission. The latter incurs less traffic as the requested file is transmitted to the users only once, rather than with many unicast transmissions. Systematic experiments demonstrate the effectiveness of our approach, as compared to the existing caching schemes.

The main promise of current research efforts in the area of Information-Centric Networking (ICN) architectures is to optimize the dissemination of information within transient communication relationships of endpoints. Efficient caching of information is key to delivering on this promise. In this paper, we look into achieving this promise from the angle of managed replication of information. Management decisions are made in order to efficiently place replicas of information in dedicated storage devices attached to nodes of the network. In contrast to traditional off-line external management systems we adopt a distributed autonomic management architecture where management intelligence is placed inside the network. Particularly, we present an autonomic cache management approach for ICNs, where distributed managers residing in cache-enabled nodes decide on which information items to cache. We propose four on-line intra-domain cache management algorithms with different level of autonomicity and compare them with respect to performance, complexity, execution time and message exchange overhead. Additionally, we derive a lower bound of the overall network traffic cost for a certain category of network topologies. Our extensive simulations, using realistic network topologies and synthetic workload generators, signify the importance of network wide knowledge and cooperation.

Recent research efforts in the area of future networks indicate Information-Centric Networking (ICN) as the dominant architecture for the Future Internet. The main promise of ICN is that of shifting the communication paradigm of the internetworking layer from machine endpoints to information access and delivery. Optimized content dissemination and efficient caching of information is key to delivering on this promise. Moreover, current trends in management of future networks adopt a more distributed autonomic management architecture where management intelligence is placed inside the network with respect to traditional off-line external management systems. In this paper, we present an autonomic cache management approach for ICNs, where distributed managers residing in cache-enabled nodes decide on which items to cache. We propose three online cache management algorithms with different level of autonomicity and compare them with respect to performance, complexity, execution time and message exchange overhead. Our extensive simulation-based experimentation signifies the importance of network wide knowledge and cooperation.

Abstract-This work is concerned with scaling decentralized content-based publish/subscribe (CBPS) networks for Internet-wide content distribution. A fundamental step for CBPS networks to reach the Internet-scale is to move from the exhaustive filtering service model, where a subscription selects every relevant publication, to a service model capturing the quantitative and qualitative heterogeneity of information consumers' requirements. In previous work, we described a service model allowing information consumers to express the maximum number of publications they would like to receive per service period and how to take advantage of such knowledge to pace the dissemination process. This paper extends by introducing a generic service model that seamlessly supports content-based information retrieval and dissemination and investigates through extensive simulations the performances of six caching policies in terms of consumers satisfaction and bandwidth usage.

In a publish/subscribe (pub/sub) network, message delivery is guaranteed for all connected subscribers at publish time. However, in a dynamic mobile scenario where users join and leave the network, it is important that content published at the time they are disconnected is still delivered when they reconnect from a different point. In this paper, we enhance the caching mechanisms in pub/sub networks to enable client mobility. We build our mobility support with minor changes in the caching scheme while preserving the main principles of loose coupled and asynchronous communication of the pub/sub communication model. We also present a new proactive mechanism to reduce the overhead of duplicate responses. The evaluation of our proposed scheme is performed via simulations and testbed measurements.

In current publish/subscribe networks messages are not stored and only active subscribers receive published messages. However, in a dynamic scenario a user may be interested in content published before the subscription time. In this paper, we introduce a mechanism that enables storing in such networks, while maintaining the main principle of loose-coupled and asynchronous communication. Furthermore, we propose a new storage placement and replication algorithm which differentiates classes of content and minimize the clients response latency. The performance of our proposed placement and replication algorithm and the proposed storing mechanism is evaluated via simulations and insights are given for future work.

In a publish/subscribe network, message delivery is guaranteed for all active subscribers at publish time. However, in a dynamic scenario where users join and leave the network, a user may be interested in content published before the subscription time. In this paper, we introduce mechanisms that enable caching in such networks, while maintaining the main principle of loose-coupled and asynchronous communication. Furthermore we investigate two caching policies; caching in all candidate brokers (basic caching) which yields high survivability and low delay and caching in leaf brokers (leaf caching) which maintains low overhead and querying complexity. The comparison is performed via simulations and testbed measurements and insights are given for future work.

A key problem in networks that support advance reservations is the routing and time scheduling of connections with flexible starting time. In this paper we present a multicost routing and scheduling algorithm for selecting the path to be followed by such a connection and the time the data should start so as to minimize the reception time at the destination, or some other QoS requirement. The utilization profiles of the network links, the link propagation delays, and the parameters of the connection to be scheduled form the inputs to the algorithm. We initially present a scheme of non-polynomial complexity to compute a set of so-called non-dominated candidate paths, from which the optimal path can be found. By appropriately pruning the set of candidate paths using path pseudo-domination relationships, we also find multicost routing and scheduling algorithms of polynomial complexity. We examine the performance of the algorithms in the special case of an Optical Burst Switched network. Our results indicate that the proposed polynomial time algorithms have performance that it is very close to that of the optimal algorithm.