Allocating the right amount of resources to each service in any of the datacenters in a cloud environment is a very difficult task. This task becomes much harder due to the dynamic nature of the workload and the fact that while long term statistics about the demand may be known, it is impossible to predict the exact demand in each point in time. As a result, service providers either over allocate resources

Many important cloud services require replicating massive data from one datacenter (DC) to multiple DCs. While the performance of pair-wise inter-DC data transfers has been much improved, prior solutions are insufficient to optimize bulk-data multicast, as they fail to explore the rich inter-DC overlay paths that exist in geo-distributed DCs, as well as the remaining bandwidth reserved for online traffic

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Edge computing applications typically require generated data to be preprocessed at the source and then transmitted to an edge server. In such cases, transmission time and preprocessing time are coupled, yielding a tradeoff between them to achieve the targeted objective. This paper presents analysis of such a system with the objective of optimizing freshness of received data at the edge server. We model

Mobile edge computing provides the opportunity for wireless users to exploit the power of cloud computing without a large communication delay. To serve data-intensive applications (e.g., video analytics, machine learning tasks) from the edge, we need, in addition to computation resources, storage resources for storing server code and data as well as network bandwidth for receiving user-provided data

We investigate competitive online algorithms for online convex optimization (OCO) problems with linear in-stage costs, switching costs and ramp constraints. While OCO problems have been extensively studied in the literature, there are limited results on the corresponding online solutions that can attain small competitive ratios. We first develop a powerful computational framework that can compute an

With the exponentially increasing number of Internet of Things (IoT) devices and the huge volume of data generated by these devices, there is a pressing need to investigate a more efficient communication method in both frequency and time domains at the edge of IoT networks. In this paper, we present Amphista, a novel cross-layer design for IoT communication and data forwarding that can more efficiently

To meet the ever increasing mobile traffic demand in 5G era, base stations (BSs) have been densely deployed in radio access networks (RANs) to increase the network coverage and capacity. However, as the high density of BSs is designed to accommodate peak traffic, it would consume an unnecessarily large amount of energy if BSs are on during off-peak time. To save the energy consumption of cellular networks

Named Data Networking (NDN) has many forwarding behaviors, strategies, and protocols to enable the benefits of Information-Centric Networking. This additional functionality introduces complexity, motivating the need for a tool to help reason about and verify that basic properties of an NDN data plane are guaranteed. This paper proposes Name Space Analysis (NSA), a network verification framework to

Caching networks can reduce the routing costs of accessing contents by caching contents closer to users. However, cache nodes may belong to different entities and behave selfishly to maximize their own benefits, which often lead to performance degradation for the overall network. While there has been extensive literature on allocating contents to caches to maximize the social welfare, the analysis

As network architecture becomes complex and the user requirement gets diverse, the role of efficient network resource management becomes more important. However, existing throughput-optimal scheduling algorithms such as the max-weight algorithm suffer from poor delay performance. In this paper, we present reinforcement learning-based network scheduling algorithms for a single-hop downlink scenario

Load balance is an important issue in datacenter networks. The flowlet-based algorithms can balance the traffic with fine granularity and does not suffer the packet mis-sequencing problem. But their performances are rather limited or require extra communication overhead. In this paper, we propose a local load-aware algorithm called Dynamic Roulette Wheel (DRW). In DRW, the roulette wheel is adopted

We propose to study mobile object tracing, which allows a mobile system to report the shape, location, and trajectory of the mobile objects appearing in a video camera and identifies each of them with its cyber-identity (ID), even if the appearances of the objects are not known to the system. Existing tracking methods either cannot match objects with their cyber-IDs or rely on complex vision modules

Channel state information (CSI) based Wi-Fi localization can achieve admirable decimeter-level accuracy; however, such systems require labor-intensive site survey to calibrate the AP position and the antenna array direction, which hinders practical large-scale deployment. In this article, we reveal an interesting finding that the calibration efforts for deploying the CSI localization system can be

Modern data analytics systems use long-running executors to run an application’s entire DAG. Executors exhibit salient time-varying resource requirements. Yet, existing schedulers simply reserve resources for executors statically, and use the peak resource demand to guide executor placement. This leads to low utilization and poor application performance. We present Elasecutor, a novel executor scheduler

Major commercial client-side video players employ adaptive bitrate (ABR) algorithms to improve the user quality of experience (QoE). With the evolvement of ABR algorithms, increasingly complex methods such as neural networks have been adopted to pursue better performance. However, these complex methods are too heavyweight to be directly deployed in client devices with limited resources, such as mobile

In this article, we develop a probabilistic framework for analyzing coded random access. Our framework is based on a new abstract receiver (decoder), called a Poisson receiver, that is characterized by a success probability function of a tagged packet subject to a Poisson offered load. We show that various coded slotted ALOHA (CSA) systems are Poisson receivers. Moreover, Poisson receivers have two

Accountability and privacy are considered valuable but conflicting properties in the Internet, which at present does not provide native support for either. Past efforts to balance accountability and privacy in the Internet have unsatisfactory deployability due to the introduction of new communication identifiers, and because of large-scale modifications to fully deployed infrastructures and protocols

Due to the openness of wireless medium, robotic networks that consist of many miniaturized robots are susceptible to Sybil attackers, who can fabricate myriads of fictitious robots. Such detrimental attacks can overturn the fundamental trust assumption in robotic collaboration and thus impede widespread deployments of robotic networks in many collaborative tasks. Existing solutions rely on bulky multi-antenna

We consider an underlay coexistence scenario where secondary users (SUs) must keep their interference to the primary users (PUs) under control. However, the channel gains from the PUs to the SUs are uncertain due to a lack of cooperation between the PUs and the SUs. Under this circumstance, it is preferable to allow the interference threshold of each PU to be violated occasionally as long as such violation

The maximum possible throughput (or the rate of job completion) of a multi-server system is typically the sum of the service rates of individual servers. Recent work shows that launching multiple replicas of a job and canceling them as soon as one copy finishes can boost the throughput, especially when the service time distribution has high variability. This means that redundancy can, in fact, create

Highly dependable communication networks usually rely on some kind of Fast Re-Route (FRR) mechanism which allows to quickly re-route traffic upon failures, entirely in the data plane. This paper studies the design of FRR mechanisms for emerging reconfigurable switches. Our main contribution is an FRR primitive for programmable data planes, PURR, which provides low failover latency and high switch throughput

Network resource reservation systems are being developed and deployed, driven by the demand and substantial benefits of providing performance predictability for modern distributed applications. However, existing systems suffer limitations: They either are inefficient in finding the optimal resource reservation, or cause private information ( e.g. , from the network infrastructure) to be exposed ( e

With the rapid growth of congestion-sensitive and data-intensive applications, traditional settlement-free peering agreements with best-effort delivery often do not meet the QoS requirements of content providers (CPs). Meanwhile, Internet access providers (IAPs) feel that revenues from end-users are not sufficient to recoup the upgrade costs of network infrastructures. Consequently, some IAPs have

Having multiple data stores that can potentially serve content is common in modern networked applications. Data stores often publish approximate summaries of their content to enable effective utilization. Since these summaries are not entirely accurate, forming an efficient access strategy to multiple data stores becomes a complex risk management problem. This paper formally models this problem as

Recent studies into streaming media delivery suggest that performance gains from ubiquitous caching in Information-Centric Networks (ICN) may be negated by Dynamic Adaptive Streaming (DAS), the de facto method for retrieving multimedia content. Bitrate adaptation mechanisms, that drive video streaming, clash with caching mechanisms in ways that affect users’ Quality of Experience (QoE). Cache performance

In transitioning to 5G, the high infrastructure cost, the need for fast rollout of new services, and the frequent technology/system upgrades triggered wireless operators to consider adopting the cost-effective network infrastructure sharing (NIS), even among competitors, to gain technology and market access. NIS is a bargaining mechanism whose terms and conditions must be carefully determined based

List-based caches can offer lower miss rates than single-list caches, but their analysis is challenging due to state space explosion. In this setting, we propose novel methods to analyze performance for a general class of list-based caches with tree structure, non-uniform access to items and lists, and random or first-in first-out replacement policies. Even though the underlying Markov process is shown

Recent advances in artificial intelligence have driven increasing intelligent applications at the network edge, such as smart home, smart factory, and smart city. To deploy computationally intensive Deep Neural Networks (DNNs) on resource-constrained edge devices, traditional approaches have relied on either offloading workload to the remote cloud or optimizing computation at the end device locally

The link speed in production datacenters is growing fast, from 1 Gbps to 40 Gbps or even 100 Gbps. However, the buffer size of commodity switches increases slowly, e.g., from 4 MB at 1 Gbps to 16 MB at 100 Gbps, thus significantly outpaced by the link speed. In such extremely shallow-buffered networks, today’s TCP/ECN solutions, such as DCTCP, suffer from either excessive packet losses or significant

Network QoS, fairness and resource sharing control are not completely solved problems. Available solutions lack scalability due to maintaining flow state, require re-tuning if traffic changes, focus on a limited set of networking scenarios or require complex, centralized controllers and feedback loops. In this paper, we propose a core-stateless solution for closed network domains like access, enterprise

Consistent hashing is a central building block in many networking applications, such as maintaining connection affinity of TCP flows. However, current consistent hashing solutions do not ensure full consistency under arbitrary changes or scale poorly in terms of memory footprint, update time and key lookup complexity. We present AnchorHash, a scalable and fully-consistent hashing algorithm. AnchorHash

In this work, we provide a comprehensive analysis of stability properties and delay gains that wireless multicasting capabilities, as opposed to more traditional unicast transmissions, can provide for content distribution in mobile networks. In particular, we propose a model and characterize the average queue-length (and hence average delay) performance of unicasting and various multicasting strategies

As the size of available networks is continuously increasing (even with millions of nodes), large-scale complex networks are receiving significant attention. While existing overlapping-community detection algorithms are quite effective in analyzing complex networks, most of these algorithms suffer from scalability issues when applied to large-scale complex networks, which can have more than 1,000,000

Software-Defined Networking (SDN) continues to be deployed spanning from enterprise data centers to cloud computing with the proliferation of various SDN-enabled hardware switches and dynamic control plane applications. However, state-of-the-art SDN-enabled hardware switches have rather limited downlink message processing capability, especially for Flow-Mod and Statistic Query , which may not suffice

The Age-of-Information is an important metric for investigating the timeliness performance in information-update systems. In this paper, we study the AoI minimization problem under a new Pull model with replication schemes, where a user proactively sends a replicated request to multiple servers to “pull” the information of interest. Interestingly, we find that under this new Pull model, replication

Hierarchical Modulation (HM) is quite efficient for multimedia broadcast in wireless networks. It exploits the wireless broadcast advantage and allows a transmission to reach different users with various qualities. However, existing HM related schemes are based on specially designed hardware, leading to extra hardware and deployment cost in practice. As a remedy for this, we design and implement a

Experience-driven networking has emerged as a new and highly effective approach for resource allocation in complex communication networks. Deep Reinforcement Learning (DRL) has been shown to be a useful technique for enabling experience-driven networking. In this paper, we focus on a practical and fundamental problem for experience-driven networking: when network configurations are changed, how to

Middleboxes are crucial for improving network security and performance, but only if the right traffic goes through the right middleboxes at the right time. Existing traffic-steering techniques rely on a central controller to install fine-grained forwarding rules in network elements—at the expense of a large number of rules, a central point of failure, challenges in ensuring all packets of a session

Reducing Coflow Completion Time (CCT) has a significant impact on application performance in data-parallel frameworks. Most existing works assume that the endpoints of constituent flows in each coflow are predetermined. We argue that CCT can be further optimized by treating flows’ destinations as an additional optimization dimension via reducer placement. In this article, we propose and implement RPC

There is an increasing interest in a fast-growing machine learning technique called Federated Learning (FL), in which the model training is distributed over mobile user equipment (UEs), exploiting UEs’ local computation and training data. Despite its advantages such as preserving data privacy, FL still has challenges of heterogeneity across UEs’ data and physical resources. To address these challenges

Unrelenting traffic growth, device heterogeneity, and load unevenness create scalability challenges for traffic monitoring. In this paper, we propose Robust Distributed Computation (RoDiC), a new approach that addresses these challenges by shifting a portion of the monitoring-task execution from an overloaded network element to another element that has spare resources. Moving the entire execution of

Fingerprinting indoor localization systems exploit wireless signal propagation features to estimate the location of wireless devices, where the major challenge in practice is the all-consuming training process: it requires site survey to establish the mapping between the signal feature and the location where the feature is observed. In this paper, we present a Wi-Fi localization scheme based on channel

Power-efficient Data Center Networks (DCNs) have been proposed to save power of DCNs using OpenFlow. In these DCNs, the OpenFlow controller adaptively turns on/off links and OpenFlow switches to form a minimum-power subnet that satisfies the traffic demand. As the subnet changes, flows are dynamically routed and rerouted to the routes composed of active switches and links. However, existing flow scheduling

Autonomous driving is a safety critical application of sensing and decision-making technologies. Communication technologies extend the awareness capabilities of vehicles, beyond what is achievable with the on-board systems only. Nonetheless, issues typically related to wireless networking must be taken into account when designing safe and reliable autonomous systems. The aim of this work is to present

This paper describes HighwayNoC, a Network-on-chip (NoC) that approaches ideal network performance using a Dual Data Rate (DDR) datapath. Based on the observation that routers datapath is faster than control, a DDR NoC allows flits to be routed at DDR improving throughput to rates defined solely by the datapath, rather than by the control. DDR NoCs can use pipeline bypassing to reduce packet latency

Due to limited energy supply on many Internet of Things (IoT) devices, asynchronous duty cycle radio management is widely adopted to save energy. Flooding is a critical way to disseminate messages through the whole network. Capture effect enabled concurrent broadcast is appealing to accelerate network flooding in asynchronous duty cycle networks. However, when the flooding payload’s size is large,

In this paper, we analyse how a peer-to-peer sharing platform should price its service to maximize profit, when user participation increases the value of the service to others by causing positive externalities. Modelling the service as an excludable public good, we propose a bounded utility model to capture many infrastructure sharing applications with bounded network value, in which complete coverage

We consider a natural scheduling problem which arises in many distributed computing frameworks. Jobs with diverse resource demands (e.g. memory requirements) arrive over time and must be served by a cluster of servers. To improve throughput and delay, the scheduler can pack as many jobs as possible in each server, however the sum of the jobs’ resource demands cannot exceed the server’s capacity. Motivated

A crucial requirement for Software Defined Network (SDN) is that data plane forwarding behaviors should always agree with control plane policies. Such requirement cannot be met when there are forwarding anomalies , where packets deviate from the paths specified by the controller. Most anomaly detection methods for SDN install dedicated rules to collect statistics of each flow, and check whether the

We consider a scenario of broadcasting information over a network of nodes connected by noiseless communication links. A source node in the network has some data packets to broadcast. It encodes these data packets into $n$ coded packets in such a way that any node in the network that receives any $k$ out of the $n$ coded packets will be able to retrieve all the original data packets. The source transmits

In recent years, connection-based slotted-Aloha (CS-Aloha) has been proposed to improve the performance of random access networks. In this protocol, each node attempts to send a request to the access point (AP) before packet transmission. Once this attempt is successful, the node can transmit up to $M$ packets to the AP. Previous works indicated that the CS-Aloha can achieve a higher throughput than

Adaptive bitrate (ABR) algorithms are critical techniques for high quality-of-experience (QoE) Internet video delivery. Early ABR algorithms conducting the overall QoE function of fixed parameters are limited by the fact that the QoE of end-users are diverse such that the video bitrate is often chosen in a misleading way. State-of-the-art ABR algorithms like MPC and Pensieve utilize offline modeling

Motivated by the potentially high downlink traffic demands of commuters in future autonomous vehicles, we study a network architecture where vehicles use Vehicle-to-Vehicle (V2V) links to form relay network clusters, which in turn use Vehicle-to-Infrastructure (V2I) links to connect to one or more Road Side Units (RSUs). Such cluster-based multihoming offers improved performance, e.g., in coverage

Online social networks provide a convenient platform for the spread of rumors, which could lead to serious aftermaths such as economic losses and public panic. The classical rumor blocking problem aims to launch a set of nodes as a positive cascade to compete with misinformation in order to limit the spread of rumors. However, most of the related researches were based on a one-dimensional diffusion

With the advent of network function virtualization (NFV), outsourcing network functions (NFs) to the cloud is becoming increasingly popular for enterprises since it brings significant benefits for NF deployment and maintenance, such as improved scalability and reduced overhead. However, NF outsourcing limits the control of customer enterprises over NF deployment and management, consequently raising