A window-assisted video partitioning strategy for partitioning and caching video streams in distributed multimedia systems

Design and performance analysis of multimedia document retrieval strategies for networked Video-on-Reservation systems

Efficient instance reuse approach for service function chain placement in mobile edge computing

Ubiquitous in-network caching is one of key features of Information Centric Network, together with receiver-drive content retrieval paradigm, Information Centric Network is better support for content distribution, multicast, mobility, etc. Cache placement strategy is crucial to improving utilization of cache space and reducing the occupation of link bandwidth. Most of the literature about caching policies considers the overall cost and bandwidth, but ignores the limits of node cache capacity. This paper proposes a G-FMPH algorithm which takes into account both constrains on the link bandwidth and the cache capacity of nodes. Our algorithm aims at minimizing the overall cost of contents caching afterwards. The simulation results have proved that our proposed algorithm has a better performance.

Cache compression improves the performance of a multi-core system by being able to store more cache blocks in a compressed format. Compression is achieved by exploiting data patterns present within a block. For a given cache space, compression increases the effective cache capacity. However, this increase is limited by the number of tags that can be accommodated at the cache. Prefetching is another technique that improves system performance by fetching the cache blocks ahead of time into the cache and hiding the off-chip latency. Commonly used hardware prefetchers, such as stream and stride, fetch multiple contiguous blocks into the cache. In this paper we propose prefetched blocks compaction (PBC) wherein we exploit the data patterns present across these prefetched blocks. PBC compacts the prefetched blocks into a single block with a single tag, effectively increasing the cache capacity. We also modify the cache organization to access these multiple cache blocks residing in a single block without any need for extra tag look-ups. PBC improves the system performance by 11.1 percent with a maximum of 43.4 percent on a four-core system.

Code generation methods for DSP applications are hampered by the combination of tight timing constraints imposed by the performance requirements of DSP algorithms, and resource constraints imposed by a hardware architecture. In this paper, we present a method for register binding and instruction scheduling based on the exploitation and analysis of resource- and timing constraints. The analysis identifies sequencing constraints between operations additional to the precedence constraints. Without the explicit modeling of these sequencing constraints, a scheduler is often not capable of finding a solution that satisfies the timing, resource and register constraints. The presented approach results in an efficient method of obtaining high quality instruction schedules with low register requirements.

Lean strategies have been developed to eliminate or reduce waste and thus improve operational efficiency in a manufacturing environment. However, in practice, manufacturers encounter difficulties to select appropriate lean strategies within their resource constraints and to quantitatively evaluate the perceived value of manufacturing waste reduction. This paper presents a methodology developed to quantitatively evaluate the contribution of lean strategies selected to reduce manufacturing wastes within the manufacturers’ resource (time) constraints. A mathematical model has been developed for evaluating the perceived value of lean strategies to manufacturing waste reduction and a step-by-step methodology is provided for selecting appropriate lean strategies to improve the manufacturing performance within their resource constraints. A computer program is developed in MATLAB for finding the optimum solution. With the help of a case study, the proposed methodology and developed model has been validated. A ‘lean strategy-wastes’ correlation matrix has been proposed to establish the relationship between the manufacturing wastes and lean strategies. Using the correlation matrix and applying the proposed methodology and developed mathematical model, authors came out with optimised perceived value of reduction of a manufacturer's wastes by implementing appropriate lean strategies within a manufacturer's resources constraints. Results also demonstrate that the perceived value of reduction of manufacturing wastes can significantly be changed based on policies and product strategy taken by a manufacturer. The proposed methodology can also be used in dynamic situations by changing the input in the programme developed in MATLAB. By identifying appropriate lean strategies for specific manufacturing wastes, a manufacturer can better prioritise implementation efforts and resources to maximise the success of implementing lean strategies in their organisation.

In this work, we address the multiple voltage scheduling problem to minimize power consumption under both timing and resource constraints. We develop a tabu search-based algorithm with a general vector representation of solution and an effective performance estimation. Moreover, our approach tends to solve a series of scheduling problems with respect to multiple voltage designs. Specifically, our method represents each solution by a vector of operation types and solves the scheduling subproblem corresponding to a certain solution vector. To get feasible solutions of the subproblem, a two-stage method is presented by first adopting the PLNSM algorithm [3] to generate delay assignment satisfying the timing constraint and then performing delay adjustment iteratively until the resource constraints are met. A heuristic estimation is introduced to predict the total power and resource usage of the neighborhood. Our proposed method achieves near-optimal solutions with only an average power increase of 0.84% compared with ILP for small-size designs, and a 24.1% reduction over a previous tabu search-based algorithm [7] for a set of benchmarks.

We propose a combinatorial auction for the allocation of home visits to independent health care practitioners in the setting of city home health care. Given the practitioners' different skill sets, time constraints and costs incurred in order to provide the services, the auction allocate home visits to practitioners such that all planned visits are covered with qualified practitioners and, at the same time, the overall service costs are minimized. We assume a game theoretic setting where practitioners are modeled as self-interested agents. We present a combinatorial auction as the allocation mechanism. We show that the auction is incentive compatible and the individual rational.

In this paper, we propose a novel wireless caching scheme to enhance the physical layer security of video streaming in cellular networks with limited backhaul capacity. By proactively sharing video data across a subset of base stations (BSs) through both caching and backhaul loading, secure cooperative joint transmission of several BSs can be dynamically enabled in accordance with the cache status, the channel conditions, and the backhaul capacity. Assuming imperfect channel state information (CSI) at the transmitters, we formulate a two-stage non-convex mixed-integer robust optimization problem for minimizing the total transmit power while providing the quality of service and guaranteeing communication secrecy during video delivery, where the caching and the cooperative transmission policy are optimized in an offline video caching stage and an online video delivery stage, respectively. Although the formulated optimization problem turns out to be NP-hard, low-complexity polynomial-time algorithms, whose solutions are globally optimal under certain conditions, are proposed for cache training and video delivery control. Caching is shown to be beneficial as it reduces the data sharing overhead imposed on the capacity-constrained backhaul links, introduces additional secure degrees of freedom, and enables a power-efficient communication system design. Simulation results confirm that the proposed caching scheme achieves simultaneously a low secrecy outage probability and a high power efficiency. Furthermore, due to the proposed robust optimization, the performance loss caused by imperfect CSI knowledge can be significantly reduced when the cache capacity becomes large.

Nowadays, there appeared a tendency of expanding scope and scale of activities in the Arctic. This is accompanied by the increasing number of security threats and potential emergencies factors in the region. Arctic region development involves implementing the goals of reducing risks to global and regional security. An important role in implementing the strategy of creating a comprehensive security system in the Arctic belongs to efficient protection of population and territories, critically important and potentially dangerous facilities from natural and man-made emergencies. All dangerous factors characterizing emergencies require generation of a complex approach to ensure security in the Arctic territories, increasing the rescue services resource potential and providing monitoring the environment components state and pollution. This article identifies ways to solve the problem of strategic planning and deployment of forces and facilities when creating an integrated security system under resource constraints and dynamically changing situation in the Arctic. An organization chart of strategic resource planning for emergencies management in the Arctic was developed. A decision-making algorithm is proposed to elaborate forces and facilitate composition for the Arctic integrated search-and-rescue centers based on the developed chart. Achieving results in strategic planning involves elaboration of a program for developing forces and facilities of the integrated security system in the Arctic region on the basis of results obtained within the simulation process. The program should reflect step-by-step solution of strategic planning tasks in accordance with goals, resource and time constraints.

Physicians have dual responsibilities to make medical decisions that serve their patients' best interests but also utilize health care resources wisely. Their ability to practice cost-consciously is particularly challenged when faced with patient expectations or requests for medical services that may be unnecessary. To understand how physicians consider health care resources and the strategies they use to exercise cost-consciousness in response to patient expectations and requests for medical care. Exploratory, qualitative focus groups of practicing physicians were conducted. Participants were encouraged to discuss their perceptions of resource constraints, and experiences with redundant, unnecessary and marginally beneficial services, and were asked about patient requests or expectations for particular services. Sixty-two physicians representing a variety of specialties and practice types participated in nine focus groups in Michigan, Ohio, and Minnesota in 2012 MEASUREMENTS: Iterative thematic content analysis of focus group transcripts Physicians reported making trade-offs between a variety of financial and nonfinancial resources, considering not only the relative cost of medical decisions and alternative services, but the time and convenience of patients, their own time constraints, as well as the logistics of maintaining a successful practice. They described strategies and techniques to educate patients, build trust, or substitute less costly alternatives when appropriate, often adapting their management to the individual patient and clinical environment. Physicians often make nuanced trade-offs in clinical practice aimed at efficient resource use within a complex flow of clinical work and patient expectations. Understanding the challenges faced by physicians and the strategies they use to exercise cost-consciousness provides insight into policy measures that will address physician's roles in health care resource use.

The amount of streaming multimedia data delivered to mobile devices is growing at a high rate. Research shows that a large number of daily commuters stream audio and video to their mobile devices during their travels. This makes urban rail environments a suitable platform for delivering entertainment, information and advertisement multimedia using novel delivery techniques. In order to do so, the system presented in this paper utilizes the unused bandwidth of a Communications-Based Train Control link to transmit multimedia to urban trains. Once on the train, multimedia is distributed to passenger devices using Wi-Fi Peer-to-Peer (P2P) technology. Such a multimedia distribution system can be deployed incrementally, as it can function concurrently with Wi-Fi connections already available in a number of trains. This paper presents the results obtained by emulating multimedia streaming in an urban rail use-case. Namely, it evaluates the received streaming multimedia quality parameters when new users arrive or existing users are replaced during the train stops.

Edge computing has been introduced as a promising technology for real-time video streaming systems. However, due to the lack of automatic incentives and the limitation of resources, traditional edge computing performs poorly in nowadays scenarios. To handle these two challenges, we propose a framework of Joint Incentive design and Resource Allocation (JIRA) for edge-based real-time video streaming systems. Technically, to ensure the trust and automatic distribution of incentives, we develop a novel smart contract based incentive mechanism and implement a prototype. Meanwhile, we propose an efficient online algorithm, i.e., JIRA, which dynamically adjusts compression ratio, offloading decision, and resource allocation to achieve performance optimization for video streaming under long-term latency and resource constraints. Specifically, JIRA is based on Lyapunov optimization, which decomposes the challenging long-term decision problem into a series of real-time optimization problems. Then we propose a multi-cut <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Generalized Benders Decomposition</i> based algorithm (MGA) to tackle the non-convexity of the decomposed problem. Through rigorous theoretical analysis, we prove the performance bound of JIRA. Extensive simulations demonstrate that the proposed schemes can achieve an efficient trade-off between accuracy performance and energy consumption.

In this paper, a scheduling algorithm for multi-processor real-time systems with relative timing constraints and resource constraints is proposed. The proposed algorithm has the capability to satisfy any timing constraints expressed as inequalities in two variables and a constant. This implies that scheduling problems imposed on various timing constraints such as end-to-end timing constraints and/or relative timing constraints, which are known to be difficult to handle, can be solved. Since the problem addressed in this paper is a generalization of some NP-complete problems, it is difficult to solve efficiently. Thus, the goal of the authors is to derive a heuristic algorithm which performs well on many instances of the problem, although in some cases it will take enormous space and time. Experimental results show that the proposed algorithm achieves a success rate of 80% in 3 minutes of computation time.

The emerging paradigm of network function virtualization (NFV) technology promises an efficient solution for optimized service deployment in the cloud computing environment thanks to its ability to dynamically add or remove virtual resources when there is a change in workload. Nevertheless, telecom providers are still facing a challenging issue in efficiently adopting NFV to deploy Web real-time communication (WebRTC) service on top of IP multimedia subsystem (IMS). Providing WebRTC service increases the inherent complexity of the IMS system in terms of the number of service nodes as virtual network functions (VNFs) and the way they interact, both of which play significant roles in the problem of optimally allocating resources. This paper proposes a virtualized interworking system between IMS and WebRTC called NFV-based interworking architecture, and describes the mechanism for VNFs to exchange messages with each other. We present an analytic system model considering the constraints of resources, quality of service (QoS), and service costs. A real-time Markov approximation-based resource allocation algorithm (RIDRA) is then designed allowing a provisioned resource at service nodes to be reconfigured in time to meet performance requirements. The proposed solution is evaluated on the large scale by simulation and on the small scale by our developed testbed. Experimental results reveal that our algorithm effectively responds to fluctuating service demands with a service cost reduced by 19% via efficiently allocating virtual resources while maintaining QoS requirement.