The new approach of cloud robotics takes advantage of cloud computing as a vast resource pool for massively parallel computation and sharing of data. Besides, the cloud robotic system removes overheads for maintenance and updates, and reduces dependence on user middleware. This is of particular interest for service robots, because on-board computation entails additional power requirements which may reduce operating duration and constrain robot mobility as well as costs. In order to utilize the cloud technology in service robots, it is crucial to allow different types of robots to share information and to develop new skills on the cloud. / In general, it is cast as a dynamic resource allocation problem. Given a set of resources and a sequence of agents, the goal is to distribute resources to agents in an optimal manner. The resource allocation problem is an NP-hard problem in general. This thesis strives to minimize the resource usage and task completion time by scheduling a number of requests from robots. However, actual realization of fully distributed cloud robotic systems is rarely found in the community. Moreover, unconstrained resources in the cloud are not commonly implemented. Therefore, the optimization of autonomously implemented resource allocation is the primary focus of the thesis. / While a respectable amount of work is done on both resource and task allocation, there is still the need for research towards the integration of problems in a typical cloud robotic system. For the outlined difficulties, this thesis addresses novel research on the following aspects: At first, the underlying architecture of Multi Sensor Data Retrieval (MSDR) is implemented on the twisted-based socket for asynchronous data transmission, which is also investigated as effective decentralized methods for multi-robot coordination, task assignment and service contract establishing. Second, the market-based scheduling mechanism is proposed for the dynamic resource allocation problem in cloud robotics. A set of criteria as empirical Quality of Service (QoS) is optimized, especially Time to Response (ToR) is minimized to fulfill Firm Real-Time (FRT) requirement of robotic tasks. Third, a Link Quality Matrix (LQM) auction-based negotiation strategy is proposed to relieve the competition among multi-robot systems in Mobile Ad-hoc Networks (MANETs). Besides, an incremental auction-based strategy is proposed considering hops, time delay as well as link quality. Both fair allocation when unmanned interference and biased allocation when users have preferences are optimized among multi-robot systems in MANETs. By tackling all these issues, this thesis contributes to general implementation of cloud robotic system into daily. / Future research will focus on task-oriented problems, such as smart home surveillance, guiding and etc., which could be better solved benefiting from cloud robotics. Solutions will proposed in a bidirectional way considering both data uploading and downloading. / 雲機器人是利用雲計算作為龐大的資源池進行大規模并行計算和資源共用。此外，雲機器人系統避免了用於維護和更新各個機器人客戶端的開銷，並降低了機器人客戶端對中間件的依賴。這對於服務機器人尤其有益，因為大量計算所需要的能量會減少機器人運行的持續時間，並且約束機器人移動性能以及增加機器人的成本。爲了更好的利用雲技術提高機器人的服務性能，最重要的是允許不同類型的機器人共用資源，尤其是多傳感器的信息，並在雲上開發新的功能和新的應用。 / 此類問題一般被規劃為動態資源配置問題，即給定一個資源集合和一個多客戶端的序列，最優化地進行資源的分配。資源配置問題是一個非確定性多項式複雜 (NP-hard) 問題 。本文通過優化調度多個資源請求，力求最大限度地減少資源的使用和任務完成的時間。目前很少有真正實現了完全分布式的雲機器人系統。此外，在實際的雲系統中並不存在無限的不受約束的資源。因此，自主地優化雲機器人系統的資源配置是雲機器人系統的關鍵問題，有著重要的現實意義。 / 雖然在資源配置和任務分配領域已經有大量的研究工作，當這兩者在典型的雲機器人系統中結合時，仍然有大量新的問題需要研究。本文著重於以下幾個方面：首先，建立多傳感器資料的檢索架構，即基於twisted的socket 框架建立任務分配和服務合同的構建方法，用於實現多傳感器信息的異步傳輸，同時將其用於研究有效的分布式多機器人協作。其次，提出基於市場范式的調度機制，用於解決雲計算機器人系統的動態資源配置問題；並針對一系列服務品質指標進行優化和驗證，特別是實現回應時間的最小化，以滿足機器人任務即時性的要求。第三，提出基於鏈路信號強度矩陣的協商策略以減輕在移動自組網路中多個機器人的通信競爭；此外，考慮到多跳、時延和鏈路品質等問題，本文提出了增量式的拍賣算法策略；當移動自組網中存在多機器人系統時，所涉算法對無人干擾情況下的公平分配和當有使用者有偏好情況下的偏好分配分別進行了優化。通過解決以上問題，本文的貢獻有助於通用的雲機器人系統融入到人類日常生活和工作中。 / 未來的研究將側重于面向機器人任務分配的問題，例如監控，多機器人嚮導等，及其他受益于雲機器人平臺的各類應用解決方案。 / Wang, Lujia. / Thesis Ph.D. Chinese University of Hong Kong 2014. / Includes bibliographical references (leaves 115-127). / Abstracts also in Chinese. / Title from PDF title page (viewed on 07, October, 2016). / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only.

Philosophiae Doctor - PhD / Recently, massive migration of enterprise applications to the cloud has been recorded in the Information Technology (IT) world. The number of cloud providers offering their services and the number of cloud customers interested in using such services is rapidly increasing. However, one of the challenges of cloud computing is Quality-of-Service management which denotes the level of performance, reliability, and availability offered by cloud service providers. Quality-of-Service is fundamental to cloud service providers who find the right tradeoff between Quality-of-Service levels and operational cost. In order to find out the optimal tradeoff, cloud service providers need to comply with service level agreements contracts which define an agreement between cloud service providers and cloud customers. Service level agreements are expressed in terms of quality of service (QoS) parameters such as availability, scalability performance and the service cost. On the other hand, if the cloud service provider violates the service level agreement contract, the cloud customer can file for damages and claims some penalties that can result in revenue losses, and probably detriment to the provider’s reputation. Thus, the goal of any cloud service provider is to meet the Service level agreements, while reducing the total cost of offering its services.

Australian universities are multi-million dollar operations employing tens of thousands of people. They attract revenue from a variety of government and non-government sources, and yet, as non-profit organisations they are judged by governments, peers and their communities on their performance in teaching and research rather than on a financial bottom line. In order to achieve these results, university managers must make decisions on how to allocate available funding throughout the university. Faced with competing demands on scarce funds, how do university managers make these choices? One option is to use a resource allocation model to 'crunch the numbers'. Resource allocation models can incorporate a number of elements - student and staff numbers, weightings and performance data, for example - to allocate available funds. These allocation models are used in different ways in different universities, but serve the same basic purpose of assisting decision-making on how much to allocate to different sections of the organisation. Such models operate within a process and context that includes the strategic aims of the University, the organisation structure, its committees and culture. This thesis contains case studies of resource allocation models and processes used in three Australian universities. It examines the methods used for resource allocation at the first and second levels within each university; that is, from the Vice-Chancellor to Dean (or equivalent), and from Dean to Head of School (or equivalent). Observations and conclusions are drawn on the models used, the processes surrounding the models, and the continuity between the two layers of allocations. The research finds all the case-study universities operate models at multiple levels in their organisations, and that there is a concerning lack of consistency and flow-through at these different levels. The messages that the university leadership intends to send through the allocations may be lost to managers one-process removed from them. The research also concludes that transparency is the most important element of the resource allocation process. University staff dealing with allocation processes will accept the results, even if they are not ideal, if they can understand how and why decisions were made. As a professional doctorate thesis, the aim is to provide a practical aid to people with responsibility for resource allocation in universities.

Thesis (M.S.) -- Worcester Polytechnic Institute. / Keywords: virtual machine; agility. Includes bibliographical references (p.58-59).

Thesis (Ph.D.)--Hong Kong University of Science and Technology, 2007. / Includes bibliographical references (leaves 114-121). Also available in electronic version.

Future-generation wireless and mobile networks are expected to support a panoply of multimedia services, ranging from voice to video data. There is also a de facto "anytime anywhere" mentality that reliable communications should be ubiquitously guaranteed, irrespective of temporal or geographical constraints. However, the implicit catch is that these specifications should be achieved with only minimal infrastructure expansion or cost increases. In this thesis, various signal processing methodologies conducive to attaining these goals are presented. First, a system model that takes into account the time-varying nature of the mobile environment is developed. To this end, a mathematically tractable basis-expansion model (BEM) of the communication channel, augmented with multiple-state characterization, is proposed. In the context of the developed system model, strategies for enhancing the quality of service (QoS), while maintaining resource efficiency, are then studied. Specifically, dynamic channel tracking, adaptive modulation and coding, interpolation and random sampling, and spatiotemporal processing are examined as enabling solutions. Next, the question of how to appropriately aggregate these disparate methods is recast as a nonlinear constrained optimization problem. This enables the construction of a flexible framework that can accommodate a wide range of applications, to deliver practical network designs. In particular, the developed methods are well-suited for multi-user communication systems, implemented using spread-spectrum and multi-carrier solutions, such as code division multiple access (CDMA) and orthogonal frequency division multiplexing (OFDM). Moreover, privacy and security requirements are increasingly becoming essential aspects of the QoS paradigm in communications. Combined with the advent of novel security technologies, such as biometrics, the conventional communication infrastructure is expected to undergo fundamental modifications to support these new system components and modalities. Therefore, within the same framework for maximizing resource efficiency, several unique signal processing applications in network security using biometrics are also investigated in this thesis. It is shown that a resource allocation approach is equally appropriate, and productive, in delivering efficient and practical key distribution and biometric encryption solutions for secure communications.

In this thesis, we argue for node selection in cooperative decode-and-forward networks. In a single-hop network with multiple relays, we show that selecting a single node to aid in the transmission between a source and a destination outperforms both traditional orthogonal transmissions and distributed space-time codes. In networks where sources transmit information over multiple hops and relays can communicate with each other, we study the relationship between cooperation and channel-adaptive routing. We show that cooperation is only beneficial if designed jointly with a routing scheme. This motivates a search for optimal algorithms in generalized relay networks. In networks without restrictions on the relays in terms of whom they can communicate with, we study the problem of optimal resource allocation in terms of transmission time. The resource allocation selects the relays to participate in the transmission and optimally allocates time resource between the selected relays. To implement this resource allocation algorithm, we propose a recursive solution which reduces the computational complexity of the algorithm. For large networks, the resulting computational complexity of implementing the algorithm is exponential in the size of the network and is likely to preclude its implementation. We thus propose that the resource allocation be implemented sub-optimally through node selection: a subset of the nodes in the network should be selected and used as input to the optimal resource allocation algorithm. We provide guidelines for selecting the nodes and propose four heuristics which offer various complexity-performance trade-offs. Compared to the optimal resource algorithm, all four heuristics significantly decrease the required computation complexity of the optimal algorithm.

We consider a system with a single base station communicating with multiple users over orthogonal channels while being assisted by multiple relays. Several recent works have suggested that, in such a scenario, selection, i.e., a single relay helping the source, is the best relaying option in terms of the resulting complexity and overhead. However, in a multiuser setting, optimal relay assignment is a combinatorial problem. We formulate a related convex optimization problem that provides an extremely tight upper bound on performance and show that selection is, almost always, inherent in the solution. We also provide a heuristic to find a close-to-optimal relay assignment and power allocation across users supported by a single relay. Simulation results using realistic channel models demonstrate the efficacy of the proposed schemes, but also raise the question as to whether the gains from relaying are worth the additional costs.

In this thesis, we explore the use of double auction markets as a general approach to tackle resource allocation problems in large-scale distributed systems, which are traditionally solved using optimization techniques. Prevalently adopted in real-world markets, double auctions have the power of arbitrating mappings between participating players and trading commodities in a decentralized fashion, with every player trying to maximize her own utility selfishly. Through the design of prefetching strategies in peer-assisted video-on-demand systems, we show how the problem of minimizing server bandwidth costs by reallocating media contents can be solved by double auction markets gracefully. However, not every resource allocation problem satisfies requirements of double auctions. We illustrate the limitation of double auctions with an example of virtual machine migration in container-based datacenters, which is then modeled into a Nash bargaining game and solved by a Nash bargaining solution.

In this thesis, we explore the use of double auction markets as a general approach to tackle resource allocation problems in large-scale distributed systems, which are traditionally solved using optimization techniques. Prevalently adopted in real-world markets, double auctions have the power of arbitrating mappings between participating players and trading commodities in a decentralized fashion, with every player trying to maximize her own utility selfishly. Through the design of prefetching strategies in peer-assisted video-on-demand systems, we show how the problem of minimizing server bandwidth costs by reallocating media contents can be solved by double auction markets gracefully. However, not every resource allocation problem satisfies requirements of double auctions. We illustrate the limitation of double auctions with an example of virtual machine migration in container-based datacenters, which is then modeled into a Nash bargaining game and solved by a Nash bargaining solution.