In latency-sensitive file caching systems such as Content Delivery Networks (CDNs) and Mobile Edge Computing (MEC), the latency of fetching a missing file to the local cache can be significant. Recent studies have revealed that successive requests for the same missing file before the fetching process completes could still suffer latency (so-called delayed hits). Motivated by the practical scenarios, we study the online general file caching problem with delayed hits and bypassing,i.e., a request may be bypassed and processed directly at the remote data center. The objective is to minimize the total request latency. We present a general reduction that turns a traditional file caching algorithm into one that can handle delayed hits. Based on this reduction, we propose an efficient online file caching algorithm, calledCaLa, with an asymptotically tight competitive ratio as$O(Z \log K)$, whereZis the maximum fetching latency of any file andKis the cache size. Extensive simulations on the production data trace from Google and the Yahoo benchmark illustrate thatCaLacan reduce the latency by up to 8.48% compared with the state-of-the-art schemes dealing with delayed hits without bypassing, and this improvement increases to 26.00% if bypassing is allowed. Furthermore, by upgrading the method for estimating files’ weights inCaLa, we proposeCaLa+, which further reduces the total latency by more than 5%.

Bus service is the most important function of public transportation. Besides the major goal of carrying passengers around, providing a comfortable travel experience for passengers is also a key business consideration. To provide a comfortable travel experience, effective bus scheduling is essential. Traditional approaches are based on fixed timetables. The wide adoptions of smart card fare collection systems and GPS tracing systems in public transportation provide new opportunities for using the data-driven approaches to fit the demand of passengers. In this paper, we associate these two independent data sets to derive the passengers' origin and destination. As the data are real time, we build a system to forecast the passenger flow in real time. To the best of our knowledge, this is the first paper, which implements a system utilizing smart card data and GPS data to forecast the passenger flow in real time.

Electric vehicle (EV) taxis have been introduced into the public transportation systems to increase EV market penetration. Different from regular taxis that can refuel in minutes, EV taxis' recharging cycles can be as long as one hour. Due to the long cycle, the bad decision on the charging station, i.e., choosing one without empty charging piles, may lead to a long waiting time of more than an hour in the worst case. Therefore, choosing the right charging station is very important to reduce the overall waiting time. Considering that the waiting time can be a nonnegligible portion to the total work hours, the decision will naturally affect the revenue of individual EV taxis. The current practice of a taxi driver is to choose a station heuristically without a global knowledge. However, the heuristical choice can be a bad one that leads to more waiting time. Such cases can be easily observed in current collected taxi data in Shenzhen, China. Our analysis shows that there exists a large room for improvement in the extra waiting time as large as 30 min/driver. In this paper, we provide a real-time charging station recommendation system for EV taxis via large-scale GPS data mining. By combining each EV taxi's historical recharging events and real-time GPS trajectories, the current operational state of each taxi is predicted. Based on this information, for an EV taxi requesting a recommendation, we can recommend a charging station that leads to the minimal total time before its recharging starts. Extensive experiments verified that our predicted time is relatively accurate and can reduce the cost time of EV taxis by 50% in Shenzhen.

Video-on-demand (VoD) services historically rely on commercial content distribution networks (CDNs) for on-demand capacity provisioning. Content providers gradually prefer a self-managed content infrastructure because of its full control and customization. However, such a dedicated physical infrastructure could be costly in initial capital investment, and complex in management. It has become a promising alternative to host VoD services on pay-as-you-go cloud platforms, on which using dynamic server provisioning to reduce server rental cost is the key objective of content providers. In this paper we address two major challenges to reducing cost: to minimize content reorganization and to tolerate imperfect workload prediction. We first present a practical VoD servicing system design based on a pay-as-you-go cloud. We prove that previous works, focusing exclusively on cost savings, cause significant content reorganization and are vulnerable to imperfect workload prediction. To address such issues, we propose a novel idea called workload absorber, and design a provisioning algorithm called Absorb Window based on the idea. Workload absorbers eliminate the bandwidth wastage and significantly reduce content reorganization. We conduct extensive evaluations with real VoD access traces, and demonstrate the superior scalability of the proposed algorithm by producing highly optimized provisioning in seconds for thousands of servers.

This paper presents SHARP: a scalable framework for Dynamic Joint Replica Placement and Request Routing (DJRPRR) scheduling in content delivery networks. After grouping similar proxies and modeling them by a single section, we propose a hierarchical scheduling framework to greatly reduce the dimensions of the mathematical formulation. In every phase the obtained shaped formulation has an easy-solvable form and the complete optimization process is highly scalable. To verify the scalability and effectiveness of our approach, SHARP is evaluated by comprehensive experiment settings which are derived from realistic data/topology of an operational commercial CDN.

Existing terrestrial synchronization protocols including RBS, FTSP, TPSN, LTS and TSHL have already achieved high precision in radio networks, but none of them perform well in high latency networks like acoustic sensor networks. In this paper, we present tri-message: a lightweight time synchronization protocol for high latency and resource-constrained networks. As its name suggests, only three message exchanges are required in one synchronization process. Meanwhile, tri-message utilizes very simple mathematical operations to calculate the clock skew and offset. Specially, tri-message is feasible for many extremely long latency applications such as space exploration because it has an increasing synchronization precision with the increasement of distance.

Current implementations of BitTorrent-like P2P applications ignore the underlying Internet topology hence incur a large amount of traffic both inside an Internet service provider (ISP)' national backbone networks and over cross-ISP Internet working links. These traffics not only occupy costly bandwidth, but also increase user perceived response latency. ISP-biased neighbor selection proposes to exploit peers' topological locality by biased neighbor selection, in which a peer chooses the majority of its neighbors from peers within the same ISP. In this paper, we propose to further exploit peers' geographic locality. First we improved ISP-biased neighbor selection (ISP-Biased+) to take into consideration network locality (or, city locations) within the same ISP. When required neighbor number is relatively much less than seeds available, ISP-biased neighbor selection+ performs much better than original approach, proved by simulations. Next, we propose that a peer could also choose its neighbors from peers of different ISPs within the same city with priority: assist by a well-know Chinese operator's unique ISP-internetworking content distribution network (CDN), these local cross-ISP traffics can be routed through local CDN cite. Using simulations, we show that cross-ISP traffic burden can be completely shifted to CDN local links and backbone traffic. At the same time, user perceived delay can be significantly reduced.

Controlled-flooding algorithms are widely used in unstructured networks. Expanding ring (ER) achieves low response delay, while its traffic cost is huge; dynamic querying (DQ) is known for its desirable behavior in traffic control, but it achieves lower search cost at the price of an undesirable latency performance; Enhanced dynamic querying (DQ+) can reduce the search latency too, while it is hard to determine a general optimum parameters set. In this paper, a novel algorithm named selective dynamic query (SDQ) is proposed. Unlike previous works that awkwardly processing floating TTL values, SDQ properly select an integer TTL value and a set of neighbors to narrow the scope of next query. Our experiments demonstrate that SDQ provides finer-grained control than other algorithms: its latency is close to the well-known minimum one via ER; in the mean time its traffic cost also close to the minimum. To our best knowledge, this is the first work capable of achieving best performance in terms of both response latency and traffic cost. In addition, our experiments also demonstrate that SDQ works well in various network topologies.