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Recently researchers use skyline techniques to optimize service selection procedure, where they can filter those low-quality web services from the large amount of candidates and return a much smaller high-quality service set. The skycube concept is adopted for quickly responding to the skyline queries with different combinations of Quality of Web Service (QoWS) parameters. As the skycube computation is quite time-consuming, it is a compelling challenge to accelerate this procedure. However, the current solutions usually have a number of redundant computations which will significantly affect the efficiency. To address such drawbacks, after an in-depth analysis of skycube computation procedure, we introduce a partial skycube, which only consists of the skylines with frequently used combinations of QoWS. Then the computational relationships between the skyline on one subspace and its parent-space are studied. Based on the relationships, we develop ${\sf {ParCube}}$ ParCube algorithm to speedup partial skycube computation by reusing the intermediate comparison results. Meanwhile, at the execution phase, ${\sf {ParCube}}$ ParCube can be further optimized with parallel execution mode and optimized scheduling strategy. Finally, we evaluate the efficiency and scalability of ${\sf {ParCube}}$ ParCube on both single machine and cluster environment. The results show that ${\sf {ParCube}}$ ParCube can efficiently compute partial skycube and scale well in cluster environment.

Billions of lightweight Internet of Things (IoT) devices have been deployed for various applications nowadays. Most of them first collect interested data and then process them in some degree according to application requirements. Transparent computing (TC) is a promising technique that makes such lightweight devices suitable to process even large-size applications. The advantage of TC is to separate code storage from its execution, allowing IoT devices to load code blocks from nearby TC storage server on demand. Distinct from existing work, this paper allows the TC IoT devices to offload some tasks to servers, since wireless IoT devices are usually powered by batteries, having limited energy resources. If a task is offloaded, a challenging problem is that its input data collected by the IoT device must be transferred as well, which incurs additional transmission time and energy. This paper proposes a two-step approach aiming at minimizing the energy consumption of the IoT device while satisfies the delay constraint. This approach first studies the offloading decision problem that determines for each task whether to offload task data or load task code blocks, while loading code indicates code receiving and executing energy cost. Second, the transmission power scheduling problem is investigated to further reduce offloading energy for a given delay constrained offloading task set. Heuristic decision making algorithms and optimal power scheduling algorithm are proposed, respectively. Such two-step approach is shown by extensive simulation to be near optimal for the original problem thanks to the optimal design of the power scheduling algorithm.

For large-scale online social networks such as Facebook and Twitter, network analysis often uses graph queries to extract network information. Because of the work and memory required, usually such queries are performed in a distributed manner. However, most existing distributed graph computation systems optimize for offline graph analysis rather than online graph queries. The problem with this approach is that graph query tasks then must transfer a large volume of data and interactively answer queries within a short time frame. To resolve this, we propose a novel data-locality-aware task scheduling algorithm that optimizes interactive distributed graph queries. The scheduling algorithm jointly considers data placement and graph topology to reduce data transfer costs. After implementing the scheduling algorithm in a real-world distributed graph computation system, we evaluate the task scheduler’s effectiveness through simulations and real-life social graph queries. Results show that our scheduler reduces the querying time by one order of magnitude.

Electrocardiogram (ECG) data classification is a hot research area for its application in medical information processing. However, insufficient data, privacy preserve, and local deployment are still challenging difficulties. To address these problems, a novel personalized federated learning method for ECG classification is proposed in this paper. First, a global model is trained with federated learning framework on multiple local data clients. Then, we use the global model and private data to train the local model. To reduce the feature inconsistency between global and private local data and for better fitting the private local data, a novel ”feature alignment” module is devised to guarantee the uniformity, which contains two parts, global alignment and local alignment, respectively. For global alignment, the graph metric of batch data is used to constrain the dissimilarity between features generated by the global model and local model. For local alignment, triplet loss is adopted to increase discriminative ability for local private data. Comprehensive experiments on our collected dataset are evaluated. The results show that the proposed method can be better adapted to local data and exhibit superior ability of generalization.

Massive knowledge graphs, such as Linked Open Data or Freebase, contain billions of labeled entities and relationships. Star queries aim to identify an entity given a set of related entities, and they are common with massive knowledge graphs. It is important to find the best way to answer star queries, and we can do this by treating it as a graph pattern-matching problem. Because knowledge graphs are noisy and incomplete in nature, we must find answers that match the star pattern closely, and extract a precise match if possible. Thus, here we propose GStar, a framework to identify the top-k best answers for a star query. GStar effectively and efficiently answers top-k star queries on billion-node graphs through a novel query model, an index-free query algorithm, and a distributed query system. We evaluate GStar through experiments on real-world knowledge graphs. Experimental results show that our query model effectively answers real-life star-pattern queries; our query algorithm can answer top-k queries in a near-real-time manner without requiring expensive graph indices; and the distributed system scales well with both the graph size and number of machines used for computation.

TC is a promising network computing paradigm that offers an efficient way to make lightweight terminals more powerful, convenient, and secure. TC's execution model separates data storage and application execution, letting terminals load applications from TC servers on demand via the Internet. With this approach, the network's performance significantly affects the TC applications' performance. To enhance TC applications' performance, existing research typically deploys many cache servers on the Internet. However, such caching techniques are not ideal in a mobile environment, where the wireless networks that mobile terminals use for Internet access are expensive and have limited bandwidth. To address this problem, we propose COAST, a cooperative storage framework for MTC. Based on a deviceto- device data-sharing technique, COAST enables a mobile terminal to fetch applications from nearby terminals without accessing the Internet. In this article, we introduce COAST's design, explore the opportunities and challenges of cooperative storage in MTC environments, and identify future research directions.

Recent developments in networking have spurred the emergence of time-sensitive applications such as Web search, social networks, and the Industrial Internet. TCP, as the core networking protocol, implements reliable packet delivery for these applications. However, TCP isn't specifically optimized for timely packet delivery, so it has poor performance for time-sensitive scenarios. To counter this, the network research community has worked extensively on standards for reducing TCP's latency. Explicit Congestion Notification (ECN) is an extension to TCP/IP that's recommended for realizing low-latency TCP. This article surveys IETF standardization efforts for ECN-based solutions, and also proposes suggestions and future directions on this topic.

© 2008-2012 IEEE. Bandwidth allocation to virtual machines (VMs) has a significant impact on the performance of communication-intensive big data applications hosted in VMs. It is crucial to accurately determine how much bandwidth to be reserved for VMs and when to adjust it. Past approaches typically resort to predicting the long-term network demands of applications for bandwidth allocation. However, lacking of prediction accuracy, these methods lead to the unpredictable application performance. Recently, it is conceded that the network demands of applications can only be accurately derived right before each of their execution phases. Hence, it is challenging to timely allocate the bandwidth to VMs with limited information. In this paper, we design and implement AppBag, an Application-aware Bandwidth guarantee framework, which allocates the accurate bandwidth to VMs with one-step-ahead traffic information. We propose an algorithm to allocate the bandwidth to VMs and map them onto feasible hosts. To reduce the overhead when adjusting the allocation, an efficient Lazy Migration (LM) algorithm is proposed with bounded performance. We conduct extensive evaluations using real-world applications, showing that AppBag can handle the bandwidth requests at run-time, while reducing the execution time of applications by 47.3 percent and the global traffic by 36.7 percent, compared to the state-of-the-art methods.