Proactive scheduling in distributed computing—A reinforcement learning approach

In distributed computing such as grid computing, online users submit their tasks anytime and anywhere to dynamic resources. Task arrival and execution processes are stochastic. How to adapt to the consequent uncertainties, as well as scheduling overhead and response time, are the main concern in dynamic scheduling. Based on the decision theory, scheduling is formulated as a Markov decision process (MDP). To address this problem, an approach from machine learning is used to learn task arrival and execution patterns online. The proposed algorithm can automatically acquire such knowledge without any aforehand modeling, and proactively allocate tasks on account of the forthcoming tasks and their execution dynamics. Under comparison with four classic algorithms such as Min–Min, Min–Max, Suffrage, and ECT, the proposed algorithm has much less scheduling overhead. The experiments over both synthetic and practical environments reveal that the proposed algorithm outperforms other algorithms in terms of the average response time. The smaller variance of average response time further validates the robustness of our algorithm.     

Peer-to-peer bichromatic reverse nearest neighbours in mobile ad-hoc networks

The increasing use of mobile communications has raised many issues of decision support and resource allocation. A crucial problem is how to solve queries of Reverse Nearest Neighbour (RNN). An RNN query returns all objects that consider the query object as their nearest neighbour. Existing methods mostly rely on a centralised base station. However, mobile P2P systems offer many benefits, including self-organisation, fault-tolerance and load-balancing. In this study, we propose and evaluate 3 distinct P2P algorithms focusing on bichromatic RNN queries, in which mobile query peers and static objects of interest are of two different categories, based on a time-out mechanism and a boundary polygon around the mobile query peers. The Brute-Force Search Algorithm provides a naive approach to exploit shared information among peers whereas two other Boundary Search Algorithms filter a number of peers involved in query processing. The algorithms are evaluated in the MiXiM simulation framework with both real and synthetic datasets. The results show the practical feasibility of the P2P approach for solving bichromatic RNN queries for mobile networks.     

Rescheduling policies for large-scale task allocation of autonomous straddle carriers under uncertainty at automated container terminals

This paper investigates replanning strategies for container-transportation task allocation of autonomous Straddle Carriers (SC) at automated container terminals. The strategies address the problem of large-scale scheduling in the context of uncertainty (especially uncertainty associated with unexpected events such as the arrival of a new task). Two rescheduling policies–Rescheduling New arrival Jobs (RNJ) policy and Rescheduling Combination of new and unexecuted Jobs (RCJ) policy–are presented and compared for long-term Autonomous SC Scheduling (ASCS) under the uncertainty of new job arrival. The long-term performance of the two rescheduling policies is evaluated using a multi-objective cost function (i.e., the sum of the costs of SC travelling, SC waiting, and delay of finishing high-priority jobs). This evaluation is conducted based on two different ASCS solving algorithms–an exact algorithm (i.e., branch-and-bound with column generation (BBCG) algorithm) and an approximate algorithm (i.e., auction algorithm)–to get the schedule of each short-term planning for the policy. Based on the map of an actual fully-automated container terminal, simulation and comparative results demonstrate the quality advantage of the RCJ policy compared with the RNJ policy for task allocation of autonomous straddle carriers under uncertainty. Long-term testing results also show that although the auction algorithm is much more efficient than the BBCG algorithm for practical applications, it is not effective enough, even when employed by the superior RCJ policy, to achieve high-quality scheduling of autonomous SCs at the container terminals.     

Multiple targets enclosure by robotic swarm

Target enclosure by autonomous robots is useful for many practical applications, for example, surveillance of disaster sites. Scalability is important for autonomous robots because a larger group is more robust against breakdown, accidents, and failure. However, since the traditional models have discussed only the cases in which minimum number of robots enclose a single target, there has been no study on the utilization of the redundant number of robots. In this paper, to achieve a highly scalable target enclosure model about the number of target to enclose, we introduce swarm based task assignment capability to Takayama’s enclosure model. The original model discussed only single target environment but it is well suited for applying to the environments with multiple targets. We show the robots can enclose the targets without predefined position assignment by analytic discussion based on switched systems and a series of computer simulations. As a consequence of this property, the proposed robots can change their target according to the criterion about robot density while they enclose multiple targets.     

基于《ASP．NET程序设计》课程的教学改革初探

将案例运用于教学,可以提高学生的学习兴趣,教学效果良好。但很难将要求掌握的知识点放置于某个案例中．或者说一个案例不可能囊括所有的知识点。所以,如何选用某些典型案例,并将案例分解成一个个的“任务”,成了案例教学中的难点。以Web新闻发布系统为例,对案例任务教学进行一些尝试。     

Association-Based Active Access Control models with balanced scalability and flexibility

In existing Active Access Control (AAC) models, the scalability and flexibility of security policy specification should be well balanced, especially: (1) authorizations to plenty of tasks should be simplified; (2) team workflows should be enabled; (3) fine-grained constraints should be enforced. To address this issue, a family of Association-Based Active Access Control (ABAAC) models is proposed. In the minimal model ABAAC₀, users are assigned to roles while permissions are assigned to task-role associations. In a workflow case, to execute such an association some users assigned to its component role will be allocated. The association's assigned permissions can be performed by them during the task is running in the case. In ABAAC₁, a generalized association is employed to extract common authorizations from multiple associations. In ABAAC₂, a fine-grained separation of duty (SoD) is enforced among associations. In the maximal model ABAAC₃, all these features are integrated, and similar constraints can be specified more concisely. Using a software workflow, case validation is performed. Comparison with a representative association based AAC model and the most scalable AAC model so far indicates that: (1) enough scalability is achieved; (2) without decomposition of a task, different permissions can be authorized to multiple roles in it; (3) separation of more fine-grained duties than roles and tasks can be enforced.     

Caching and optimized request routing in cloud-based content delivery systems

Geographically distributed cloud platforms enable an attractive approach to large-scale content delivery. Storage at various sites can be dynamically acquired from (and released back to) the cloud provider so as to support content caching, according to the current demands for the content from the different geographic regions. When storage is sufficiently expensive that not all content should be cached at all sites, two issues must be addressed: how should requests for content be routed to the cloud provider sites, and what policy should be used for caching content using the elastic storage resources obtained from the cloud provider. Existing approaches are typically designed for non-elastic storage and little is known about the optimal policies when minimizing the delivery costs for distributed elastic storage.In this paper, we propose an approach in which elastic storage resources are exploited using a simple dynamic caching policy, while request routing is updated periodically according to the solution of an optimization model. Use of pull-based dynamic caching, rather than push-based placement, provides robustness to unpredicted changes in request rates. We show that this robustness is provided at low cost. Even with fixed request rates, use of the dynamic caching policy typically yields content delivery cost within 10% of that with the optimal static placement. We compare request routing according to our optimization model to simpler baseline routing policies, and find that the baseline policies can yield greatly increased delivery cost relative to optimized routing. Finally, we present a lower-cost approximate solution algorithm for our routing optimization problem that yields content delivery cost within 2.5% of the optimal solution.     

Analysing interactive devices based on information resource constraints

Analysis of the usability of an interactive system requires both an understanding of how the system is to be used and a means of assessing the system against that understanding. Such analytic assessments are particularly important in safety-critical systems as latent vulnerabilities may exist which have negative consequences only in certain circumstances. Many existing approaches to assessment use tasks or scenarios to provide explicit representation of their understanding of use. These normative user behaviours have the advantage that they clarify assumptions about how the system will be used but have the disadvantage that they may exclude many plausible deviations from these norms. Assessments of how a design fails to support these user behaviours can be a matter of judgement based on individual experience rather than evidence. We present a systematic formal method for analysing interactive systems that is based on constraints rather than prescribed behaviour. These constraints capture precise assumptions about what information resources are used to perform action. These resources may either reside in the system itself or be external to the system. The approach is applied to two different medical device designs, comparing two infusion pumps currently in common use in hospitals. Comparison of the two devices is based on these resource assumptions to assess consistency of interaction within the design of each device.     

How social distance shapes human–robot interaction

This paper investigates how social distance can serve as a lens through which we can understand human–robot relationships and develop guidelines for robot design. In two studies, we examine the effects of distance based on physical proximity (proxemic distance), organizational status (power distance), and task structure (task distance) on people׳s experiences with and perceptions of a humanlike robot. In Study 1, participants (n=32) played a card-matching game with a humanlike robot. We manipulated the power distance (supervisor vs. subordinate) and proxemic distance (close vs. distant) between participants and the robot. Participants who interacted with the supervisor robot reported a more positive user experience when the robot was close than when the robot was distant, while interactions with the subordinate robot resulted in a more positive experience when the robot was distant than when the robot was close. In Study 2, participants (n=32) played the game in two different task distances (cooperation vs. competition) and proxemic distances (close vs. distant). Participants who cooperated with the robot reported a more positive experience when the robot was distant than when it was close. In contrast, competing with the robot resulted in a more positive experience when it was close than when the robot was distant. The findings from the two studies highlight the importance of consistency between the status and proxemic behaviors of the robot and of task interdependency in fostering cooperation between the robot and its users. This work also demonstrates how social distance may guide efforts toward a better understanding of human–robot interaction and the development of effective design guidelines.     

浅谈多媒体教学以信息管理中心为例

高职教育的特点就是以适应社会需要为目标、以培养技术应用能力为主线设计学生的知识、能力、素质结构和培养方案,毕业生应具有基础理论知识适度、技术应用能力强、知识面较宽、素质高等特点.以“应用”为主旨来构建课程和教学内容体系.