Multi-channel data broadcast attracts increasingly focus in recent years. For the complex and large multimedia data like images, audios, and videos etc, multi-channel data broadcast is a promising approach to mitigate various limitations of data dissemination in mobile environment, such as narrow bandwidth, unreliable connections, and battery limitation. However, existing data broadcast schemes are inefficient for employing multiple channels. In this paper, we present five novel multimedia data broadcast schemes (SDAA, ISDAA, LDAA, AEA, and COA) specifically designed for wireless multichannel communications. The key idea behind those schemes is to utilize SVC (Scalable Video Coding) to generate data segments with different qualities and implement indexing and channel assignment to minimize the expected waiting time for clients. In terms of waiting time minimization, we prove theoretically that SDAA is a 4/3-approximation algorithm and ISDAA is a 6/5-approximation algorithm for global allocation, and AEA can achieve locally optimal solution. We show that integrating SDAA and AEA form a best schedule for practical applications. We provide numerical experiments by using a SVC dataset and a YouTube dataset to evaluate the system performance and prove the efficiency of our schemes. We also propose a multiple video broadcast framework and analyze its performance.
Aggressively provisioned data centers achieve great cost savings by over-committing the very expensive power distribution infrastructure. However, existing proposals for managing load power demand in such a data center are largely utilization-driven, overlooking power-related interferences among users. An important observation is that some tasks can impact existing power budget management framework and disrupt normal operation by taking away the precious public power capacity. This vulnerability exposes data centers to a new type of risk that we call power grab, which is essentially hostile power resource competition. It could worsen the performance-utilization tradeoff in a power-constrained computing environment. Anticipating a growing case for power-oriented com-petition, we propose CFP, a resilient power capacity management frame-work for improving the fairness and service quality in scale-out data centers. Our solution features a market-based power re-source allocation and billing scheme that involves users in the loop. It allows the data center to bypass the formidable task of identifying malicious users and defend against power grab with reward and punishment incentives. We build a proof-of-concept system and also evaluate our design with realistic Google cluster traces. Compared to prior arts, CFP can increase the average performance-cost ratio by 1.8X. It can boost the total throughput in an APDC by 15% under severe power contention. Our design allows scale-out data centers to safely exploit the benefits that power over-subscription may provide, with minor overhead.
To scale the Internet of Things (IoT) beyond a single home or enterprise, we need an effective mechanism to manage the growth of data, facilitate resource discovery and name resolution, encourage data sharing, and foster cross-domain services. To address these needs, we propose a GlObaL Directory for Internet of Everything (GOLDIE). GOLDIE is a hierarchical location-based IoT directory architecture featuring diverse user-oriented modules and federated identity management. IoT-specific features include discoverability, aggregation and geospatial queries, and support for global access. We implement and evaluate the prototype on a Raspberry Pi and Intel mini servers. We show that a global implementation of GOLDIE could decrease service access latency by 87% compared to a centralized-server solution.
To improve in-band network control and enable the rapid deployment of new protocols, we devise a re-framing scheme that we call unified Network Protocol Encapsulation (uNPE). uNPE maps control information that previously resided in the nested headers of many Internet protocols onto a set of data units unified in a single layer and logically divided into three control groups: connection, network function, and application function. By liberating the control information from the rigid framing of the nested protocol headers, uNPE increases transparency and flexibility for future networks, empowering network control and simplifying the design of new protocols. Using the P4 language for programmable data planes we prototype uNPE and show that it can substantially improve essential network functions such as routing and congestion control.
Crowdsourcing has been considered as one of the most promising services in recent years. More and more crowdsourcing platforms allocate tasks over the social network due to its pervasiveness. Although most research focuses on direct contribution-based task allocation with some budget constraints, a robust task allocation scheme should also consider the task allocation in the word-of-mouth (WoM) mode, in which tasks are delivered from workers to workers. In this paper, we discuss an NP-Complete problem, cost-effective and budget-balanced task allocation (CBTA) problem, specially for the WoM mode crowdsourcing over social network, which aims to minimize the overall budget consumption as well as balance the budgets among target social groups. Furthermore, we propose two heuristic algorithms CB-greedy and CB-local based on greedy strategy and local search technique respectively to construct a spanning tree for task allocation. We prove that the running time of CB-greedy is O(m 2 log m) while CB-local utilizing disjoint-set achieves O(mna(m, n)), where m is the number of edges indicating interactions of social groups, n is the number of social groups, and α is the inverse Ackerman function. Extensive simulations show that the proposed algorithms guarantee the criteria to a large extent. To the best of our knowledge, it is the first work jointly optimizing cost effectiveness and budget balance in the WoM mode crowdsourcing systems.
Facing the current situation of the shortage of high-skilled talents in China’s construction industry,constructing a high-skilled talent training model in the construction industry that meets China’s national conditions is a key way to solve the shortage of talents. "Qian Xuesen’s question" not only exists in the cultivation of high-tech talents, but also in the cultivation of high-skilled talents. In general basic education and vocational edu-cation, the theory-based test-oriented education in the teaching process may lead to a lack of autonomy and ineffi-cient reproductive learning. Therefore, there must also be productive learning provided by work learning. In order to promote the cultivation of high-skilled talents in the construction industry, this paper proposes a high-skilled construction talent cultivation model that includes the four main bodies of government, schools, enterprises and individuals, and combines the three cultivation methods of general basic education, vocational education and work study to find out for the shortage of high-skilled talents in the construction industry.
In 2006, Miranda et al. proposed an anonymity scheme to achieve peers' anonymity in Peer-to-Peer. (P2P) reputation systems. In this paper, we show that this scheme can not achieve peers' anonymity in two cases. We also propose an improvement which solves the problem and improves the degree of anonymity.
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