Recommender systems are designed to provide users with personalized suggestions and promotions by filtering vast amounts of information. However, these systems often face significant privacy and data sparsity challenges. Point-of-interest (POI) recommender systems, which focus on suggesting physical locations to users, are particularly affected by these issues due to location data's sensitive and scattered nature. This paper presents an examination of privacy-preserving approaches through data synthesis techniques, specifically employing Conditional Tabular Generative Adversarial Networks (CTGAN) and clustering. We investigate the performance of recommender systems trained solely with synthetic data generated by CTGAN or clustering for physical users. Our study compares the effectiveness of recommending by using original data, CTGAN-generated data, and clustering-generated centroids. Experimental results on real-world datasets demonstrate that integrating synthetic data can maintain system accuracy while resolving privacy issues such as data transportation and user anonymization, demonstrating the potential of GAN-based methods to advance POI recommendation systems.

Cloud computing has transformed data storage and management, offering scalability, flexibility, and cost-efficiency for applications. However, ensuring data security and maintaining performance in distributed cloud environments remain a significant challenge. Sensitive data stored in the cloud is vulnerable to unauthorized access, raising concerns about confidentiality and integrity. One particular vulnerability is caused by file deletion operations in computer systems, which usually only remove index entries, with the content of the files remaining on the disk. This can pose a potential threat to sensitive information in the cloud storage. This paper addresses the challenge by proposing a shredding-based approach for effective file management. The proposed approach proactively shreds files into independently meaningless shreds when storing a file in the cloud, and then distributes these shreds across multiple cloud sites. When the file is deleted, it is much harder to recover the content of the original file. A mathematical model is developed to perform shredding and reconstruction efficiently. It is further enhanced by randomizing the shredding process and adding modifiers to improve security. The method addresses the security problem of files stored in the cloud from a different perspective, compared to traditional encryption-based methods. Performance evaluation demonstrated that the proposed method achieves a balance between security and efficiency. The method is further evaluated using FABRIC infrastructure, and the results show that the shredding overhead is insignificant, and the approach can be used in practical cloud-based systems.

FABRIC is a national and international scale research infrastructure built to enable cutting-edge research in a wide range of networking and computing areas and domain sciences that depend on advanced networking and computing capabilities. Since its full operation in October 2023, FABRIC has seen rapidly emerging experiments in different research topics. This paper provides a description of FABRIC’s hardware resources, software services, and an analysis of the characteristics of the emerging experiments that are leveraging FABRIC’s unique capabilities. The analysis showed a wide spectrum of topic areas, frequent emphasis on at-scale and high performance experiments that interact with real facilities and real people, and growing complexity and rigor in tools, data and measurement. Researchers are seen sharing well-developed artifacts with others to enable larger experiments and reproducible research.

Network security policies provide high-level directives regarding acceptable and unacceptable use of the network. Organizations specify these high-level directives in policy documents written using human-readable natural language. The challenge is to convert these natural language policies to the network configurations/specifications needed to enforce the policy. Network administrators, who are responsible for enforcing the policies, typically translate the policies manually, which is a challenging and error-prone process. As a result, network operators (as well as the policy authors) often want to verify that network policies are being correctly enforced. In this paper, we propose Network Policy Conversation Engine (NPCE), a system designed to help network operators (or policy writers) interact with the network using natural language (similar to the language used in the network policy statements themselves) to understand whether policies are being correctly enforced. The system leverages emerging big data collection and analysis techniques to record flow and packet level activity throughout the network that can be used to answer users policy questions. The system also takes advantage of recent advances in Natural Language Processing (NLP) to translate natural language policy questions into the corresponding network queries. To evaluate our system, we demonstrate a wide range of policy questions – inspired by actual networks policies posted on university websites – that can be asked of the system to determine if a policy violation has occurred.

Network policies govern the use of an institution's networks, and are usually written in a high-level human-readable natural language. Normally these policies are enforced by low-level, technically detailed network configurations. The translation from network policies into network configurations is a tedious, manual and error-prone process. To address this issue, we propose a new intermediate language called POlicy LANguage for Campus Operations (POLANCO), which is a human-readable network policy definition language intended to approximate natural language. Because POLANCO is a high-level language, the translation from natural language policies to POLANCO is straightforward. Despite being a high-level human readable language, POLANCO can be used to express network policies in a technically precise way so that policies written in POLANCO can be automatically translated into a set of software defined networking (SDN) rules and actions that enforce the policies. Moreover, POLANCO is capable of incorporating information about the current network state, reacting to changes in the network and adjusting SDN rules to ensure network policies continue to be enforced correctly. We present policy examples found on various public university websites and show how they can be written as simplified human-readable statements using POLANCO and how they can be automatically translated into SDN rules that correctly enforce these policies.

A key concept of software-defined networking (SDN) is separation of the control and data plane. This idea provides several benefits, including fine-grained network control and monitoring, and the ability to deploy new services in a limited scope. Unfortunately, it is often cost-prohibitive for enterprises (and universities in particular) to upgrade their existing networks to wholly SDN-capable networks all at once. A compromise solution is to deploy SDN capabilities incrementally in the network. The challenge then is to take full advantage of SDN-based services throughout the network, in an integrated fashion rather than in a few "islands" of SDN support. At the University of Kentucky, SDN has been integrated into the campus network for several years. In this paper, we describe two aspects of this challenge, along with our solution approaches. One is the general reluctance of campus network administrations to allow novel or experimental (SDN-based) services in the production network. The other is how to extend such services throughout the legacy part of the network. For the former, we lay out a set of principles designed to ensure that the production service is not harmed. For the latter, we use policy based routing and a graph database to extend our previously-described VIP Lanes service. Our simulation results in a campus-like topology testbed show that we can provide a host with custom path service even if it is connected to a legacy router.

Campus networks and enterprise networks increasingly depend on middleboxes (e.g., firewalls, NAT, load balancers, IDS/IDP) to provide essential services or enforce network policies. These middleboxes often limit the performance of network applications, especially those involved in big data transfer. To address this problem, we propose a Software Defined Networking (SDN) campus network architecture, called VIP Lanes, that provides the ability for pre-authorized, trusted users to create flows that bypass middleboxes, thereby enabling those users to achieve substantially better performance while maintaining security and policy compliance for other network traffic. In this paper, we present the VIP Lanes abstraction and describe an authorization and policy-enforcement service used to establish trusted VIP Lanes. We describe an initial prototype implementation that not only demonstrates the viability of the VIP Lanes approach, but also gives an indication of the types of performance improvements that are possible - in some cases approaching a two order of magnitude reduction in transmission times.

Cloud computing is a new paradigm that provides computing, storage and networking services to end users. Data distribution for cloud computing is different from that in traditional content distribution networks in that it has a direct implication on efficiency of using cloud resources. In this paper we propose a new block-based data distribution mechanism for cloud computing. Instead of using the whole file as a unit of delivery, we divide a file into blocks to improve the efficiency. We design a scheduling algorithm to manage the delivery of blocks to all receivers. The novel aspect of the algorithm is that it can achieve constant distribution time no matter how many receivers need to get the file. We further analyze the overhead caused by dividing a file into blocks. The performance results show that our mechanism can significantly reduce the distribution time, compared with the traditional method and a non-block-based method.

Most cellular network communication strategies are focused on data traffic scenarios rather than energy balance and efficient utilization. Thus mobile users(cell phones) in hot cells may suffer from low throughput due to energy loading imbalance problem. In state-of-art cellular network technologies, relay stations extend cell coverage and enhance signal strength for mobile users. However, busy traffic makes the relay stations in hot area run out of energy quickly. In this paper, we propose an energy balancing strategy in which the mobile nodes are able to dynamically select and hand over to the relay station with the highest potential energy capacity to resume communication. Key to the strategy is that each relay station merely maintains two parameters that contains the trend of its previous energy consumption and then predicts its future quantity of energy, which is defined as the relay station's potential energy capacity. Then each mobile node can select the relay station with the highest potential energy capacity. Simulations demonstrate that our approach significantly increase the aggregate throughput and the average life time of relay stations in cellular network environment.

Much of the GENI effort in developing network testbeds has been focused on building the control frameworks needed to allocate and initialize the network resources that make up an experiment. We argue that building the instrumentation and measurement system to monitor and capture the behavior of the network is just as important and challenging as setting up the network itself, especially in a virtualized and federated environment where getting information from experimental nodes is too complicated and too much to handle for a typical user. In this paper, we describe the design of an instrumentation and measurement infrastructure that allows users to monitor their experiments. The challenge that virtualization and federation of GENI testbeds bring to instrumentation and monitoring is how to hide the details of instrumentation setup from users so that users do not need to be experts in system administration or network management of virtualized and federated systems, but are still able to “see” what is going on with their experiments. Our instrumentation tool sets up experiment-specific monitoring infrastructure that is tailored to capture, record, and display only information associated with that experiment. Our tools are currently available in GENI, and we present a simple example of how to use them to instrument an experiment.

Geographic routing is an effective approach for finding a path to forward packets from a source to a destination in wireless ad hoc networks when nodes have location information. The basic strategy is to let each node select one of its neighbors that is closest to the destination as the next hop to forward the packet greedily. However, when there are void areas (holes), greedy routing does not work due to the local minimum problem. Existing geographic routing algorithms use perimeter routing strategies to find a long detour path when such a situation occurs. In this paper, we propose a new approach called Intermediate Target Based Geographic Routing (ITGR) to avoid such long detour paths. The source determines destination areas which are shaded by the holes based on previous forwarding experience. The novelty of the approach is that a single forwarding path can be used to determine a shaded area that may cover many destination nodes. We design an efficient method for the source to find out whether a destination node belongs to a shaded area. The source then selects an intermediate node as the tentative target and greedily forwards packets to it, which in turn forwards the packet to the final destination by greedy routing. ITGR can be recursively called, if necessary, to generate a detour-free path with multiple tentative targets. Simulation results show that ITGR significantly reduces the routing path length, compared with existing geographic routing protocols.

Geographic routing for wireless ad-hoc networks has a well known "local minimum" problem, which is caused by a "hole" that blocks the greedy forwarding process. Existing geographic routing algorithms use perimeter routing strategies to find a long detour path when such a situation occurs. In this paper, we propose a heuristic hole detecting algorithm which can identify the hole in advance and advertise the hole information to those nodes that may be affected. Our work differs from existing hole detection algorithms in its simplicity and efficiency. We study the tradeoff between the area of hole annoucement and the routing path improvement. In addition, we propose a simple representation of hole information and develop a routing scheme based on it. Simulation results illustrate that our approach can reduce the average length and number of hops of routing paths, and is computationally more efficient than other hole detection algorithms.

Emulabs, network testbeds on which experiments can be carried out through a web interface, are widely recognized as useful research environments. We believe that Emulabs are also excellent platforms for teaching courses in operating systems and networking. Moreover, equipment costs have fallen, and Emulab software has evolved, to the point where any school could conceivably afford their own. This raises the obvious question: "Should institutions build and operate their own Emulab?". To get a handle on this question, we describe our experiences building and operating Edulabs--Emulabs specifically designed for education--at Calvin College and the University of Kentucky. We argue that with the right technical support, owning and operating one's own Emulab is a worthwhile endeavor that has many benefits. We acknowledge, however, that in the absence of good technical support, deploying one's own Emulab can be a difficult enterprise. We also describe how we enhanced Emulab to improve its utility in an educational setting.

The decentralized peer-to-peer (P2P) file sharing systems organize nodes into structured or unstructured networks. The advantages of the unstructured P2P systems are that they have lower maintenance overhead and can better adapt to node heterogeneity as well as network dynamics. However, the search process in unstructured systems is not as efficient as in structured P2P systems because the same search message may go through a node multiple times. To facilitate the complex search and improve the search efficiency, we propose a novel approach of assigning identifications to nodes in an unstructured system. Our method can prevent a node from receiving duplicate search messages and retain the low maintenance overhead for the system. The performance evaluations demonstrate that the proposed approach can improve the search efficiency of unstructured P2P systems while keeping the maintenance overhead at a comparable or even lower level, compared with the traditional unstructured systems.

Routing mechanisms in delay tolerant networks (DTNs) usually rely on nodes' mobilities to bridge gaps in space and time. Though they may depend on the movements of ordinary nodes, it is more desirable to introduce special mobile nodes called ferries dedicated to communication in DTNs. In this paper, we concentrate on the route design problem for multiple ferries. We introduce an analytically tractable model to describe message delivery delay in three different schemes for multiple ferry routing. We give analytical results that reveal the tradeoff between the cost of using more ferries and the performance gains. Driven by the analysis, we propose routing algorithms that can match our theoretical results well. The algorithms are evaluated under extensive network settings via simulations. The results show that our algorithms can reduce the message delivery delay over existing routing algorithms, especially when the nodes are not uniformly distributed in space.

Delivering large media files over the Internet is a challenging task because it has some unique features that are different from delivering conventional Web documents. In this paper, we propose a fine-grained peer sharing technique for dealing with the problem in the context of content distribution networks. The key difference of the technique from conventional peer-to-peer systems is that the unit of peer sharing is not a complete media file, but at a finer granularity. By doing so, we improve the flexibility of replica servers for handling client requests. We analyze the storage requirement at replica servers and design a scheduling algorithm to coordinate the delivery process from multiple replica servers to a client. Our simulations show that the fine-grained peer sharing approach can reduce the initial latency of clients and the rejection rate of the system significantly over a simple peer sharing method.

Overlay multicast is an efficient and employable approach for delivering multimedia content to a large number of receivers. A key problem in overlay multicast is how to deal with node failures and ungraceful leavings. When a non-leaf end host fails or leaves the multicast session, all downstream nodes will be affected. In this paper, we adopt the proactive approach, which pre-calculates a candidate node (called parent-to-be) for each node to connect to in case its current parent dies. The goal is to recover the multicast tree quickly so that the disruption of service to those affected nodes is minimized. We combine the local parent-to-be locating and global parent-to-be locating schemes together, in order to take advantage of less interference among affected nodes in the local scheme and the flexibility of the global scheme. The quality of the recovered tree is improved while the responsiveness of the proactive approach is maintained.

Multicast delivery is an efficient approach to the provision of a video-on-demand (VoD) service. Interacting with the video stream is a desirable feature for users. However, it is a challenging task to provide the functionality in the multicast environment because a lot of users share multicast delivery channels. In this paper, we propose an active buffer management technique to provide interactive functions in broadcast VoD systems. In our scheme, the client can selectively prefetch segments from broadcast channels based on the observation of the play point in its local buffer. The content of the buffer is adjusted in such a way that the relative position of the play point is kept in the middle part of the buffer. Our simulations show that the active buffer management scheme can implement interactive actions through buffering with a high probability in a wide range of user interaction levels.

In this paper we propose a novel segmentation-based approach for delivering multimedia files in content distribution networks. Each media file is divided into segments and the number of copies distributed to replica servers for each segment is designed in such a way that we can reduce the storage requirement at the replica servers, accommodate the bursty arrival of client requests, and at the same time achieve a small latency for the clients. We analyze the effect of the size of segments on the total storage requirement and give a lower bound. We design a scheduling algorithm that can take advantage of the increased capacity of access links of clients. Our simulations show that the segmentation-based approach can reduce the latency and the rejection rate significantly over those approaches that deliver the whole or the prefix part of media files to replica servers.

Overlay multicast constructs a multicast delivery tree among end hosts. Unlike traditional IP multicast, the non-leaf nodes in the tree are normal end hosts, which are potentially more susceptible to failures than routers and may leave the multicast group voluntarily. In these cases, all downstream nodes are affected. Thus an important problem in overlay multicast is how to recover from node departures in order to minimize the disruption of service to those affected nodes. In this paper, we propose a proactive approach to restore overlay multicast trees. Rather than letting downstream nodes try to find a new parent after a node departure, each non-leaf node pre-calculates a parent-to-be for each of its children. When this non-leaf node is gone, all its children can find their respective new parents immediately. The salient feature of the approach is that each non-leaf node can compute a rescue plan for its children independently, and in most cases, rescue plans from multiple non-leaf nodes can work together for their children when they fail or leave at the same time. We develop a protocol for nodes to communicate with new parents so that the delivery tree can be quickly restored. Extensive simulations demonstrate that our proactive approach can recover from node departures 5 times faster than reactive methods in some cases, and 2 times faster on average.

The distribution of multimedia files brings new challenges to the problem of replica placement in content distribution networks (CDN) and invalidates several assumptions underlying the existing solutions. In this paper we formulate a new model for the problem of replica placement to accommodate these new characteristics. We perform a theoretical analysis of the cost of distributing multimedia files over CDNs and find out that, contrary to the intuition, deploying as many replicas as possible is not always a good strategy. We then propose several replica placement algorithms that can determine the optimal number of replicas we should select from a given set of potential sites. By simulation we demonstrate that the performance of clients may degrade if we choose too many sites for replica placement.

We formulate and investigate fundamental problems that arise when multicast servers, that deliver content to multiple clients simultaneously, are replicated to enhance scalability and performance. Our study consists of two parts. First, we consider the problem under the assumption that the multicast clients are static for the duration of the multicast content distribution session. In this context, we examine two models for server behavior: fixed-rate servers, which transmit at a constant rate, and rate-adaptive servers, which adapt their transmission rate based on network conditions and/or feedback from clients. In both cases, we show that general versions of the client assignment problems are NP-hard. We then develop and evaluate efficient algorithms for interesting special cases, as well as heuristics for general cases. Second, we consider the case in which the set of clients changes dynamically during the multicast content distribution session. We again consider both fixed-rate and rate-adaptive servers. We formulate the problem as a Markov decision process, capturing the costs associated with trees, as well as the transition costs to dynamically change the trees. We use the properties of optimal solutions for small examples to develop a set of dynamic server selection heuristics.

Multicast communication enables a server to send content to multiple clients at the same time through a multicast tree. To deal with the heterogeneity of client capacities, multiple multicast groups can be used to allocate clients with similar capacity to the same group, so that the performance perceived by clients can be improved. We investigate the problem of allocating clients to constrained multicast servers, which, similar to clients, have different capacities. We explore some interesting issues raised by the constraints and propose an optimal solution to the allocation problem. We evaluate the solution and show substantial performance gain for our algorithm over those considering the server constraints separately.

Server replication improves the ability of a service to handle a large number of clients. One of the important factors in the efficient utilization of replicated servers is the ability to direct client requests to the "best" server, according to some optimality criteria. In the anycasting communication paradigm, a sender communicates with a receiver chosen from an anycast group of equivalent receivers. As such, anycasting is well suited to the problem of directing clients to replicated servers. This paper examines the definition and support of the anycasting paradigm at the application-layer, providing a service that uses an anycast resolver to map an anycast domain name and a selection criteria into an IP address. By realizing anycasting in the application-layer, we achieve flexibility in the optimization criteria and ease the deployment of the service. As a case study, we examine the performance of our system for a key service: replicated Web servers. To this end, we develop an approach for estimating the response time that a client will experience when accessing given servers. Such information is maintained in the anycast resolver that clients query to obtain the identity of the server with the best estimated response time. Our performance collection technique combines server push with resolver probes to estimate the expected response time without undue overhead. Our experiments show that selecting a server using our architecture and estimation technique can improve the client response time by a factor of two over nearest server selection and by a factor of four over random server selection.

In this paper we investigate multicast server selection problems. First we give a definition of the static multicast server selection problem, in which we assume a set of static clients and multicast servers and consider how one might produce an optimal allocation of the clients to the servers. We use a transformation method for deriving multicast server selection algorithms from traditional multicast routing algorithms. To investigate the dynamic behavior of client join and leave and the cost incurred during the process, we next define the dynamic multicast server selection problem, in which the clients join and leave the multicast session dynamically. The goal is to produce an optimal allocation of clients to servers with an emphasis on how this allocation behaves over time. We formulate the problem as a Markovian decision process (MDP). Our analysis of the problem leads to two heuristics which we use to propose a simple selection algorithm. Our simulation compares the performance of our proposed algorithm with other multicast server selection algorithms.

The World Wide Web (WWW) has gained tremendously in popularity. In this work we explore the use of UDP, best-effort multicast as a delivery option. Reliability is achieved through repetitive, cyclic transmission of a requested page. This solution is expected to be most efficient when used for highly requested pages. We view this cyclic multicast technique as a delivery option that can be integrated with the traditional reliable unicast and reliable multicast options. We first describe the architecture of an integrated Web server employing all three delivery options. We then describe the cyclic multicast technique and consider the various procedures needed for its successful operation. We characterize the gains in performance achieved by our proposal through an extensive performance analysis and simulation of our technique by itself, and when integrated with the other delivery options. We also describe our experience with an implementation of a prototype cyclic multicast server and its performance over the multicast backbone (MBone).

Server replication is an approach often used to improve the ability of a service to handle a large number of clients. One of the important factors in the efficient utilization of replicated servers is the ability to direct client requests to the best server, according to some optimality criteria. In this paper we target an environment in which servers are distributed across the Internet, and clients identify servers using our application-layer any-casting service. Our goal is to allocate servers to clients in a way that minimizes a client's response time. To that end, we develop an approach for estimating the performance that a client would experience when accessing particular servers. Such information is maintained in a resolver that clients can query to obtain the identity of the server with the best response time. Our performance collection technique combines server push with client probes to estimate the expected response time. A set of experiments is used to demonstrate the properties of our performance determination approach and to show its advantages when used within the application-layer anycasting architecture.

This article discusses our experience in using a World Wide Web-based shotgun measurement approach for mining and characterizing large software systems. The approach recognizes that measurement information is essentially management information, that different levels and functions of the organizational hierarchy require different information to make decisions, and that a measurement program is typically a discovery process about an organization's current modes of operations. What we found was the usefulness of a measurement program that also allows managers to dynamically formulate new goals and get answers to questions not specifically related to original goals but raised nonetheless by metric data. We describe three specific cases of decisions that were made using this approach and data collected from one large system and accessed using the company's intranet over the past two years.

The anycasting communication paradigm is designed to support server replication by allowing applications to easily select and communicate with the "best" server, according to some performance or policy criteria, in a group of content-equivalent servers. We examine the definition and support of the anycasting paradigm at the application layer, providing a service that maps anycast domain names into one or more IP addresses using anycast resolvers. In addition to being independent from network-layer support, our definition includes the notion of filters, functions that are applied to groups of addresses to affect the selection process. We consider both metric-based filters (e.g., server response time) and policy-based filters.