Market research effectiveness : the effects of organisational structure, resource allocation and strategic type

According to the marketing literature and marketing textbooks, market research is required for successful marketing. However organisations differ in the way they both manage and resource their market research. This study hypothesised that market research effectiveness would be a function of internal research buying expertise, resource allocation and strategy. The research was conducted among a sample of 240 Australian marketing managers. Market research effectiveness was measured in terms of a) decision making support, b) contribution to marketing strategy, c) leveraging customer and competitor data, d) its ability to represent the "voice of the customer" and finally, e) bolstering the role of the marketing group and marketing manager within the organisation. The findings showed that having dedicated internal market researchers and allocating internal and external resources to the research function enhanced market research effectiveness. It was also found that organisations with an entrepreneurial strategic orientation were more likely to see value in the market research function, with these organisations being less likely to use market research for internal political purposes.

A decision support tool for resource allocation in batch manufacturing

A decision support tool for production planning is discussed in this paper to perform the job of machine grouping and labour allocation within a machining line. The production plans within the industrial partner have been historically inefficient because the relationship between the cycle times, the machine group size, and the operator's utilisation hasn't been properly understood. Starting with a simulation model, a rule-base has been generated to predict the operator's utilisation for a range of production settings. The resource allocation problem is then solved by breaking the problem into a series of smaller sized tasks. The objective is to minimise the number of operators and the difference between the maximum and minimum cycle times of machines within each group. The results from this decision support tool is presented for the particular case study.

Stress, resource allocation and mortality

We model the optimal allocation of limited resources of an animal during a transient stressful event such as a cold spell or the presence of a predator. The animal allocates resources between the competing demands of combating the stressor and bodily maintenance. Increased allocation to combating the stressor decreases the mortality rate from the stressor, but if too few resources are allocated to maintenance, damage builds up. A second source of mortality is associated with high levels of damage. Thus, the animal faces a trade-off between the immediate risk of mortality from the stressor and the risk of delayed mortality due to the build up of damage. We analyze how the optimal allocation of the animal depends on the mean and predictability of the length of the stressful period, the level of danger of the stressor for a given level of allocation, and the mortality consequences of damage. We also analyze the resultant levels of mortality from the stressor, from damage during the stressful event, and from damage during recovery after the stressful event ceases. Our results highlight circumstances in which most mortality occurs after the removal of the stressor. The results also highlight the importance of the predictability of the duration of the stressor and the potential importance of small detrimental drops in condition. Surprisingly, making the consequences of damage accumulation less dangerous can lead to a reallocation that allows damage to build up by so much that the level of mortality caused by damage build up is increased. Similarly, because of the dependence of allocation on the dangerousness of the stressor, making the stressor more dangerous for a given level of allocation can decrease the proportion of mortality that it causes, while the proportion of mortality caused by damage to condition increases. These results are discussed in relation to biological phenomena.

Novel mechanism for evaluating feedback in the grid environment on resource allocation

The primary concern in proffering an infrastructure for general purpose computational grids formation is security. Grid implementations have been devised to deal with the security concerns. The chief factors that can be problematic in the secured selection of grid resources are the wide range of selection and the high degree of strangeness. Moreover, the lack of a higher degree of confidence relationship is likely to prevent efficient resource allocation and utilization. In this paper, we propose an efficient approach for the secured selection of grid resources, so as to achieve secure execution of the jobs. The presented approach utilizes trust and reputation for securely selecting the grid resources by also evaluation user’s feedback on the basis of the feedback already available about the entities. The proposed approach is scalable for an increased number of resources.

A general cloud firewall framework with dynamic resource allocation

Cloud is becoming a dominant computing platform. However, we see few work on how to protect cloud data centers. As a cloud usually hosts many different type of applications, the traditional packet level firewall mechanism is not suitable for cloud platforms in case of complex attacks. It is necessary to perform anomaly detection at the event level. Moreover, protecting objects are more diverse than the traditional firewall. Motivated by this, we propose a general framework of cloud firewall, which features event level detection chain with dynamic resource allocation. We establish a mathematical model for the proposed framework. Moreover, a linear resource investment function is proposed for economical dynamical resource allocation for cloud firewalls. A few conclusions have been extracted for the reference of cloud service providers and designers.

Resource allocation across the egg laying sequence in the wild zebra finch Taeniopygia guttata

Zebra finches have played a central role in the discovery of a variety of maternal effects over the past decade, with females shown to adjust resource allocation to their eggs in response to variables such as the appearance of their partner, their own condition, and the diet on which they are maintained. In addition to being the focus of some of the most high profile individual studies that have influenced maternal effects research in birds, the multitude of zebra finch studies together provide the most comprehensive set of data to illuminate general patterns and compare different maternally derived variables. Surprisingly, to date, virtually all of this work has focused on captive populations of the zebra finch that have been domesticated for many generations, and which are typically held under relatively constant environmental and dietary conditions. Here we report the first data on resource allocation across the egg laying sequence in a free-living wild population. Reassuringly we find that the patterns that have been found in the majority of studies of domesticated populations with respect to investment across the laying sequence were all present in the wild population. The size and mass of eggs increased through the laying sequence whilst the concentration of carotenoids significantly decreased across the laying sequence. Although there was no significant pattern with respect to testosterone across the laying sequence the first two eggs had a higher level of testosterone than the last few eggs in the clutch, which is also consistent with the findings of earlier studies in captive populations. © 2011 The Authors.

A scalable and automatic mechanism for resource allocation in self-organizing cloud

Taking advantage of the huge potential of consumers’ untapped computing power, self-organizing cloud is a novel computing paradigm where the consumers are able to contribute/sell their computing resources. Meanwhile, host machines held by the consumers are connected by a peer-to-peer (P2P) overlay network on the Internet. In this new architecture, due to large and varying multitudes of resources and prices, it is inefficient and tedious for consumers to select the proper resource manually. Thus, there is a high demand for a scalable and automatic mechanism to accomplish resource allocation. In view of this challenge, this paper proposes two novel economic strategies based on mechanism design. Concretely, we apply the Modified Vickrey Auction (MVA) mechanism to the case where the resource is sufficient; and the Continuous Double Auction (CDA) mechanism is employed when the resource is insufficient. We also prove that aforementioned mechanisms have dominant strategy incentive compatibility. Finally, extensive experiment results are conducted to verify the performance of the proposed strategies in terms of procurement cost and execution efficiency.

Joint resource allocation for max-min throughput in multicell networks

We investigate the resource-allocation problem in multicell networks targeting the max-min throughput of all cells. A joint optimization over power control, channel allocation, and user association is considered, and the problem is then formulated as a nonconvex mixed-integer nonlinear problem (MINLP). To solve this problem, we proposed an alternating-optimization-based algorithm, which applies branch-and-bound and simulated annealing in solving subproblems at each optimization step. We also demonstrate the convergence and efficiency of the proposed algorithms by thorough numerical experiments. The experimental results show that joint optimization over all resources outperforms the restricted optimization over individual resources significantly.

Critically impaired infants and end of life decision making : resource allocation and difficult decisions

Decisions to withdraw or withhold life-sustaining treatment are contentious, and offer difficult moral dilemmas to both medical practitioners and the judiciary. This issue is exacerbated when the patient is unable to exercise autonomy and is entirely dependent on the will of others.This book focuses on the legal and ethical complexities surrounding end of life decisions for critically impaired and extremely premature infants. Neera Bhatia explores decisions to withdraw or withhold life-sustaining treatment from critically impaired infants and addresses the controversial question, which lives are too expensive to treat? Bringing to bear such key issues as clinical guidance, public awareness, and resource allocation, the book provides a rational approach to end of life decision making, where decisions to withdraw or withhold treatment may trump other competing interests.The book will be of great interest and use to scholars and students of bioethics, medical law, and medical practitioners.

Stability analysis and resource allocation for space-based multi-access systems

In space-based networks, the data relay satellites can assist low-earth-orbit satellites in relaying data to other satellites or the ground station and improve the real time system throughput. To take full advantage of transmission resource of the cooperative relays, this paper proposes a multiple access and resource allocation strategy, in which relays can receive and transmit simultaneously according to channel characteristics of space-based systems. Based on the queueing theoretic formulation, the stability of the proposed protocol is analyzed and the maximum stable throughput region is derived, which would provide the appropriate guidance for the design of the system optimal control. Simulation results exhibit multiple factors that affect the stable throughput and verify the theoretical analysis.

Providing fairer resource allocation for multi-tenant cloud-based systems

A fundamental premise in cloud computing is trying to provide a more sophisticated computing resource sharing capability. In order to provide better allocation, the Dominant Resource Fairness (DRF) approach has been developed to address the "fair resource allocation problem" at the application layer for multi-tenant cloud applications. Nevertheless conventional DRF only considers the interplay of CPU and memory, which may result in over allocation of resources to one tenant's application to the detriment of others. In this paper, we propose an improved DRF algorithm with 3-dimensional demand vector to support disk resources as the third dominant shared resource, enhancing fairer resource sharing. Our technique is integrated with LINUX 'group' controls resource utilisation and realises data isolation to avoid undesirable interactions between co-located tasks. Our method ensures all tenants receive system resources fairly, which improves overall utilisation and throughput as well as reducing traffic in an over-crowded system. We evaluate the performance of different types of workload using different algorithms and compare ours to the default algorithm. Results show an increase of 15% resource utilisation and a reduction of 59% completion time on average, indicating that our DRF algorithm provides a better, smoother, fairer high-performance resource allocation scheme for both continuous workloads and batch jobs.

A dynamic pricing reverse auction-based resource allocation mechanism in cloud workflow systems

Market-oriented reverse auction is an efficient and cost-effective method for resource allocation in cloud workflow systems since it can dynamically allocate resources depending on the supply-demand relationship of the cloud market. However, during the auction the price of cloud resource is usually fixed, and the current resource allocation mechanisms cannot adapt to the changeable market properly which results in the low efficiency of resource utilization. To address such a problem, a dynamic pricing reverse auction-based resource allocation mechanism is proposed. During the auction, resource providers can change prices according to the trading situation so that our novel mechanism can increase the chances of making a deal and improve efficiency of resource utilization. In addition, resource providers can improve their competitiveness in the market by lowering prices, and thus users can obtain cheaper resources in shorter time which would decrease monetary cost and completion time for workflow execution. Experiments with different situations and problem sizes are conducted for dynamic pricing-based allocation mechanism (DPAM) on resource utilization and the measurement of Time∗Cost (TC). The results show that our DPAM can outperform its representative in resource utilization, monetary cost, and completion time and also obtain the optimal price reduction rates.