Cooperative perception facilitated by vehicle-to-vehicle (V2V) data sharing has emerged as a crucial enabler for safe and efficient autonomous driving. However, the current state-of-the-art algorithms are unable to resolve severe performance degradation caused by communication impairments in realistic V2V scenarios. This paper models the V2V communication quality and confirms their fragile robustness under loss conditions. To this end, we propose a robust and efficient cooperative perception framework. Specifically, we propose RoCooper, a robust fusion algorithm. It leverages the lossless ego feature as an anchoring foundation, then utilizes multi-dimensional feature correlations and dynamic regional selective cross-learning. This allows it to perform multi-scale feature recovery and judiciously fuse multi-view features from neighboring vehicles. In addition, we design ReduAdapt, an efficient redundancy-aware scheduling scheme that first constructs hierarchical metadata to decompose 3D perception space, then applies dynamic multi-factor thresholds for region-specific cropping, and finally performs retention-driven adaptive compression, enabling connect vehicles to prioritize critical data streams while dynamically suppressing redundant transmissions, thereby maximizing effective throughput. Extensive evaluations of real-world datasets demonstrate that our method achieves state-of-the-art performance in varying impairment scenarios, while delivering more efficient compression-transmission at comparable levels.

With the large-scale deployment of IPv6, it is becoming more and more important to probe active IPv6 addresses on the global Internet. However, the vast address space and the random distribution of active addresses make the probing process full of challenges, especially for the probing of IPv6 prefixes without seed addresses. Furthermore, the widespread existence of IPv6 aliased prefixes also causes significant trouble for probing. In this paper, we presentAddrProbe, an active IPv6 address probing system, which dynamically probes all global routing prefixes based on learned fine-grained address patterns from limited seed addresses and quickly detects aliased prefixes during probing. The evaluation results show thatAddrProbeachieves a hit rate of 23%-45% with all routing prefixes announced by the BGP system, which is 6.6-13× that of current state-of-the-art approaches (no more than 4%). Moreover, we find 1.2×1033aliased addresses characterized by the detected aliased prefixes, covering 6,412 routing prefixes, which is a 107× and 5.9× improvement over existing methods, respectively. Finally, an IPv6 Hitlist is constructed based on the long-term probing results, which contains 562M addresses covering 190K routing prefixes and 29K ASes. These widely distributed addresses are meaningful for analyzing IPv6 address assignments and some other IPv6 measurement activities.

We introduce TopoHunter, an efficient IPv6 Internet topology discovery system. The central concept of TopoHunter is to allocate more probing resources to target prefix spaces that yield greater topological benefits, as well as to their surrounding areas. To achieve this, we design a feedback-based target generation module comprised of a Target Prefix Probing Value Forest that maintains the estimated probing values of hierarchical target prefix spaces. Our system has successfully discovered the most extensive and complete IPv6 topology map to date, comprising over 144 million router interfaces and 251 million edges, covering 72.83% of autonomous systems and 43.36% of routing prefixes announced by the BGP system.

Short video streaming platforms widely employ video prefetching to ensure users' quality of experience (QoE), but frequent user swipes lead to massive data wastage, creating a significant financial burden for vendors. Existing academic and industrial solutions fail to strike a balance, often sacrificing either data savings or the authentic user-perceived QoE. We introduce a framework that intelligently reduces streaming data consumption without compromising user experience. The core idea is to make prefetching decisions adaptive to both user swiping behavior and dynamic network conditions. Real-world evaluations show our framework significantly outperforms the state-of-the-art solutions on data wastage as well as user-perceived QoE.

Mobile live-streaming applications with stringent latency and bandwidth requirements have gained tremendous attention in recent years. Encountered with bandwidth insufficiency and congestion instability of the wireless uplinks, multi-access networking provides opportunities to achieve fast and robust connectivity. However, the state-of-the-art multi-path transmission solutions are lack of adaptivity to the heterogeneous and dynamic nature of wireless networks. Meanwhile, the indispensable video coding and transformation bring about extra latency and make the video delivery vulnerable to network throughput fluctuation. This paper presents AggDeliv, a framework that provides efficient and robust multi-path transmission for mobile live video delivery. The key idea is to relate multi-path packet scheduling to congestion control optimization over diverse wireless links and adapt it to the mobile video characteristics. This is achieved by probabilistic packet allocation based on links’ congestion windows, wireless-oriented delay and loss aware congestion control, as well as lightweight video frame coding and network-adaptive frame-packet transformation. Real-world evaluations demonstrate that our framework significantly outperforms the state-of-the-art solutions on aggregate goodput and streaming video bitrate.

With the desired functionality of moving object tracking, wireless pan-tilt cameras are able to play critical roles in a growing diversity of surveillance environments. However, today's pan-tilt cameras oftentimes underperform when tracking frequently moving objects like humans – they are prone to lose sight of objects and bring about excessive mechanical rotations that are especially detrimental to those energy-constrained outdoor scenarios. The ineffectiveness and high cost of all state-of-the-art tracking approaches are rooted in their adherence to the industry's simplicity principle, which leads to their stateless nature, performing gimbal rotations based only on the latest object detection. To address the issues, we design and implement WiseCam that wisely tunes the pan-tilt cameras to minimize mechanical rotation costs while maintaining long-term object tracking. This systematic tracking approach also tackles issues of motion-rotation speed gap and scattered moving objects, which is universally applicable to complex tracking scenarios. We examine the performance of WiseCam by experiments on two types of pan-tilt cameras with different motors. Results show that it significantly outperforms the state-of-the-art tracking approaches on both tracking duration and power consumption.

On-device AI is taking over our daily lives by moving closer to mobile devices as perception applications. A data stream perception application generally has three essential requirements: timeliness, smoothness, and orderliness. Most researchers’ efforts to date have proposed various offloading approaches to accelerate compute-intensive AI algorithms in perception applications, thereby fulfilling the requirement of timeliness. However, the lack of concern about the smoothness and orderliness of the data stream will result in fluctuation and commotion anomalies that greatly impair the user experience. In this paper, we propose an enhanced Offloading System with sMoothness and Orderliness (OSMO) to guarantee perception applications’ smooth refresh rates while processing data streams in proper orders with low overhead. OSMO takes advantage of heterogeneous computing devices and data-level parallelism in the offloading process. A scheduling strategy is further devised that dynamically tunes a set of parameters to achieve the best trade-offs among the three requirements of perception applications. We implement a prototype system based on TensorFlow and its typical Android demos. Real-world evaluations demonstrate that our solution can effectively address the fluctuation and commotion issues while providing a high data processing rate with multi-device collaboration.

As a proper supplement to traditional wireless communication, opportunistic network provides a feasible and inexpensive way to achieve message delivery, especially in extreme environments. However, the gossip dissemination problem severely influences the network performance, and is hardly tackled due to the network characteristics of more transmission delay and higher mobility. To address this problem, we propose a robust and efficient framework named SCON, which contains a flexible region-based cluster routing algorithm to relieve the gossip impacts, crowd-sourcing prosecution and attacker judgment schemes and node reward and punishment mechanisms to discover and eliminate attackers that disseminate gossips, as well as several buffer maintenance mechanisms to further improve the network performance. Comprehensive evaluations demonstrate the high performance and robustness of our frame-work compared with the state-of-the-art approaches when gossip dissemination occurs.

While urban rail transit systems are playing an increasingly important role in meeting the transportation demands of people, precise awareness of how the human crowd is distributed within such a system is highly necessary, which serves a range of important applications including emergency response, transit recommendation, and commercial valuation. Most rail transit systems are closed systems where once entered the passengers are free to move around all stations and are difficult to track. In this article, we attempt to estimate the crowd distribution based only on the tap-in and tap-out records of all the rail riders. Specifically, we study Singapore MRT (Mass Rapid Transit) as a vehicle and leverage EZ-Link transit card records to estimate the crowd distribution. Guided by a key observation that the passenger inflows and arrival flows at different MRT stations and time are spatio-temporally correlated due to behavioral consistency of MRT riders, we design and implement a machine learning-based solution, CrowdAtlas, that captures MRT riders’ transition probabilities among stations and across time, and based on that accurately estimates the crowd distribution within the MRT system. Our comprehensive performance evaluations with both trace-driven studies and real-world experiments in MRT disruption cases demonstrate the effectiveness of CrowdAtlas.

While the urban rail transit systems are playing an increasingly important role in meeting the transportation demands of people, the precise awareness of how the human crowd is distributed within the urban rail transit system is highly necessary, which serves to a range of important applications including emergency response, transit recommendation, commercial valuation, etc. Most urban rail transit systems are closed systems where once entered the travelers are free to move around all stations that are connected into the system and are difficult to track. In this paper, we attempt to estimate the crowd distribution within the urban rail transit system based only on the entrance and exit records of all the rail riders. Specifically, we study Singapore MRT (Mass Rapid Transit) as a vehicle and leverage the tap-in and tap-out records of the EZ-Link transit cards to estimate the crowd distribution. Guided by a key observation that the passenger inflows and arrival flows at various MRT stations are spatio-temporally correlated due to behavioral consistence of MRT riders, we design and implement a machine learning based solution, CrowdAtlas, that accurately estimates the crowd distribution within the MRT system. Our trace-driven performance evaluation demonstrates the effectiveness of CrowdAtlas.

Today's cloud storage infrastructures typically provide two distinct types of services for hosting files: object storage like Amazon S3 and filesystem storage like Amazon EFS. In practice, a cloud storage user often desires the advantages of both-efficient filesystem operations with a low unit storage price. An intuitive approach to achieving this goal is to combine the two types of services, e.g., by hosting large files in S3 and small files together with directory structures in EFS. Unfortunately, our benchmark experiments indicate that the clients' download performance for large files becomes a severe system bottleneck. In this article, we attempt to address the bottleneck with little overhead by carefully tweaking the usages of S3 and EFS. Guided by two key observations, we design and implement an open-source system called HyCloud. It automatically invokes the data APIs of S3 and EFS on behalf of users, and intelligently schedules the data transfer among S3, EFS and the clients in a distributed manner. Real-world evaluations demonstrate that the unit storage price of HyCloud is close to that of S3, and the filesystem operations are executed as quickly as in EFS in most times (sometimes even more quickly than in EFS).

Today's cloud storage infrastructures typically provide two distinct types of services for hosting files: object storage like Amazon S3 and filesystem storage like Amazon EFS. The former supports simple, flat object operations with a low unit storage price, while the latter supports complex, hierarchical filesystem operations with a high unit storage price. In practice, however, a cloud storage user often desires the advantages of both-efficient filesystem operations with a low unit storage price. An intuitive approach to achieving this goal is to combine the two types of services, e.g., by hosting large files in S3 and small files together with directory structures in EFS. Unfortunately, our benchmark experiments indicate that the clients' download performance for large files becomes a severe system bottleneck. In this paper, we attempt to address the bottleneck with little overhead by carefully tweaking the usages of S3 and EFS. This attempt is enabled by two key observations. First, since S3 and EFS have the same unit network-traffic price and the data transfer between S3 and EFS is free of charge, we can employ EFS as a relay for the clients' quickly downloading large files. Second, noticing that significant similarity exists between the files hosted at the cloud and its users, in most times we can convert large-size file downloads into small-size file synchronizations (through delta encoding and data compression). Guided by the observations, we design and implement an open-source system called HyCloud. It automatically invokes the data APIs of S3 and EFS on behalf of users, and handles the data transfer among S3, EFS and the clients. Real-world evaluations demonstrate that the unit storage price of HyCloud is close to that of S3, and the filesystem operations are executed as quickly as in EFS in most times (sometimes even more quickly than in EFS).

Cloud storage services such as Dropbox have been widely used for file collaboration among multiple users. However, this desirable functionality is yet restricted to the “walled-garden” of each service. At present, the only feasible approach to cross-cloud file collaboration seems to be using web APIs, whose performance is known to be highly unstable and unpredictable. Now that using inefficient web APIs is inevitable, in this paper we attempt to achieve sound user-perceived performance for cross-cloud file collaboration. This attempt is enabled by two key observations from real-world measurements. First, for each cloud, we are always able to deploy one or several nearby (client) proxies which can efficiently access the web APIs. Second, during file collaboration, significant similarity exists among different versions of a file. This can be exploited to substantially reduce inter-proxy traffic and thus shorten the data sync time. Guided by the observations, we design and implement an open-source prototype system called CoCloud. Currently, it supports file collaboration among four popular cloud storage services in the US and China. Its performance is well acceptable to users under representative workloads, even approaching or exceeding that of intra-cloud collaboration in many cases.

As one type of the most popular cloud storage services, OpenStack Swift and its follow-up systems replicate each object across multiple storage nodes and leverageobject sync protocolsto achieve high reliability andeventual consistency. The performance of object sync protocols heavily relies on two key parameters:$r$(number of replicas for each object) and$n$(number of objects hosted by each storage node). In existing tutorials and demos, the configurations are usually$r=3$and$n<1,000$by default, and the sync process seems to perform well. However, we discover in data-intensive scenarios, e.g., when$r>3$and$n\gg 1,000$, the sync process is significantly delayed and produces massive network overhead, referred to as thesync bottleneck problem. By reviewing the source code of OpenStack Swift, we find that its object sync protocol utilizes a fairly simple and network-intensive approach to check the consistency among replicas of objects. Hence in a sync round, the number of exchanged hash values per node is$\Theta (n\times r)$. To tackle the problem, we propose a lightweight and practical object sync protocol,LightSync, which not only remarkably reduces the sync overhead, but also preserves high reliability and eventual consistency. LightSync derives this capability from three novel building blocks: 1)Hashing of Hashes, which aggregates all the$h$hash values of each data partition into a single but representative hash value with the Merkle tree; 2)Circular Hash Checking, which checks the consistency of different partition replicas by only sending the aggregated hash value to the clockwise neighbor; and 3)Failed Neighbor Handling, which properly detects and handles node failures with moderate overhead to effectively strengthen the robustness of LightSync. The design of LightSync offers provable guarantee on reducing the per-node network overhead from$\Theta (n\times r)$to$\Theta (\frac{n}{h})$. Furthermore, we have implemented LightSync as an open-source patch and adopted it to OpenStack Swift, thus reducing the sync delay by up to 879$\times$and the network overhead by up to 47.5$\times$.

Cloud storage services such as Dropbox have been widely used for file collaboration among multiple users. However, this desirable functionality is yet restricted to the “walled-garden” of each service. At present, the only effective approach to cross-cloud file collaboration seems to be using web APIs, whose performance is known to be highly unstable and unpredictable. Now that using inefficient web APIs is inevitable, in this paper we attempt to achieve sound user-perceived performance for cross-cloud file collaboration. This attempt is enabled by two key observations from real-world measurements. First, for each cloud, we are always able to deploy one or several nearby (client) proxies which can efficiently access the web APIs. Second, during file collaboration, significant similarity exists among different versions of a file. This can be exploited to substantially reduce inter-proxy traffic and thus shorten the data sync time. Guided by the observations, we design and implement an open-source prototype system called CoCloud. Currently, it supports file collaboration among four popular cloud storage services in the US and China. Its performance is well acceptable to users under representative workloads, even approaching or exceeding intra-cloud performance in many cases.

In this paper, we investigate the scheduling problem with multi-resource allocation in cloud computing environments. In contrast to existing work that focuses on flow-level scheduling, which treats flows in isolation, we consider dependency among subtasks of applications that imposes a partial order relationship in execution. We formulate the problem of Multi-Resource Partial-Ordered Task Scheduling (MR-POTS) to minimize the makespan. In the first stage, the proposed Dominant Resource Priority (DRP) algorithm decides the collection of subtasks for resource allocation by taking into account the partial order relationship and characteristics of subtasks. In the second stage, the proposed Maximum Utilization Allocation (MUA) algorithm partitions multiple resources among selected subtasks with the objective to maximize the overall utilization. Both theoretical analysis and experimental evaluation demonstrate the proposed algorithms can approximately achieve the minimal makespan with high resource utilization. Specifically, a reduction of 50% in makespan can be achieved compared with existing scheduling schemes.