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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.

In a cloud environment, the primary way to optimize physical resources is to reuse a physical machine (PM) by consolidating complementary multiple virtual machines (VMs) on it. When considering VMs' dynamically changing resource demands, one hot research topic revolves around reusing VM migration resources more efficiently. The challenge here is finding the best tradeoff between the VM migration optimization performance and complexity. On one hand, to improve the migration efficiency, VM migration planning is adopted to achieve efficient resource reuse while minimizing the number of VM migrations. On the other hand, the huge number of PMs and VMs in a cloud datacenter often adds considerable complexity to migration planning, which hampers the decision-making process in VM migration. To address these issues, this paper proposes a hybrid resource pool model to reduce the complexity of VM migration planning by limiting the scope of VM migration decisions. Then, based on this model, we use our novel resource-reuse optimization mechanism (called HirePool) to improve efficiency by maximizing resource usage with only a few VM migrations. Finally, we perform simulation tests and actual experiments running on a real cloud platform to evaluate HirePool. Results show that HirePool improves average resource usage by 13%, saves the number of PMs used by 12%, and reduces the average number of migrations (compared with contrast mechanisms) by 31%.

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.

Web-scale knowledge graphs containing billions of entities are common nowadays. Querying these graphs can be modeled as a subgraph matching problem. Since knowledge graphs are incomplete and noisy in nature, it is important to discover answers matching exactly as well as answers similar to queries. Existing graph matching algorithms usually use graph indices to accelerate query processing. For billion-node graphs, it may be infeasible to build the graph indices due to the amount of work and the memory/storage required. In this paper, we propose an efficient algorithm for finding the best $k$ answers for a given query without precomputing graph indices. An answer’s quality is measured by a matching score that is computed online. To accelerate query processing, we propose a novel technique for bounding the matching scores during the computation. By using bounds, the low quality answers can be efficiently pruned. The bounding technique can be implemented in a distributed environment, allowing our approach to efficiently query web-scale knowledge graphs. We evaluate the effectiveness and the efficiency of our approach on real-world datasets. The result shows that our bounding technique can reduce the running time up to two orders of magnitude comparing to an approach without using bounds.

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.