Resource Allocation with Noise Constraints

Moldable scientific workflows represent a special class of scientific workflows where the tasks are written as distributed programs being able to exploit various amounts of computer resources. However, current cluster job schedulers require the user to specify the amount of resources per task manually. This often leads to suboptimal execution time and related cost of the whole workflow execution since many users have only limited experience and knowledge of the parallel efficiency and scaling. This paper proposes several mechanisms to automatically optimize the execution parameters of moldable workflows using genetic algorithms. The paper introduces a local optimization of workflow tasks, a global optimization of the workflow on systems with on-demand resource allocation, and a global optimization for systems with static resource allocation. Several objectives including the workflow makespan, computational cost and the percentage of idling nodes are investigated together with a trade-off parameter putting stress on one objective or another. The paper also discusses the structure and quality of several evolved workflow schedules and the possible reduction in makespan or cost. Finally, the computational requirements of evolutionary process together with the recommended genetic algorithm settings are investigated. The most complex workflows may be evolved in less than two minutes using the global optimization while in only 14 s using the local optimization.

Software reliability growth model with debugging efforts

In this paper, we propose cumulative distribution function (CDF)-based multiuser scheduling (CMS) for downlink non-orthogonal multiple access (NOMA) to accommodate a massive number of IoT devices over limited radio resources and statistically analyze the ergodic capacity of devices. With the proposed CMS algorithm, multiple devices having relatively better channel gain to exploit multiuser diversity are selected while meeting QoS requirements by controlling the amount of resources. We first provide the user selection probability in terms of the weight of each device, which determines the amount of time resources. Based on some selected results, it is shown that equal weights ensure fair resource allocation. Further, for fair resource allocation scenario, the distribution of signal-to-interference-plus-noise ratio (SINR) when a specific device is selected is derived. With this derived statistical result of SINR, we analyze the ergodic capacity of the specific device. Some selected results show that the proposed CMS algorithm allocates the time resource more fairly than proportional fair scheduling. Further, the average throughput and amount of resources can be controlled by adjusting the weights of each device, so different QoS requirements can be guaranteed.

Achieving high performance in many distributed systems requires finding a good assignment of threads to servers as well as effectively allocating each server's resources to its assigned threads. The assignment and allocation components of this problem have both been studied extensively, but separately in the literature. In this paper, we introduce the assign and allocate (AA) problem, which seeks to simultaneously find an assignment and allocations that maximize the total utility of the threads. Assigning and allocating the threads together can result in substantially better overall utility than performing the steps separately, as is traditionally done. We model each thread by a concave utility function giving its throughput as a function of its assigned resources. We first show that the AA problem is NP-hard, even when there are only two servers. We then present a 2(√2-1) > 0.828 factor approximation algorithm, which runs in O(mn2 + n (log mC)2) time for n threads and m servers with C amount of resources each. We also present a faster algorithm with the same approximation ratio and O(n(log mC)2) running time. We conducted experiments to test the performance of our algorithm on threads with different types of utility functions, and found that it achieves over 99% of the optimal utility on average. We also compared our algorithm against several other assignment and allocation algorithms, and found that it achieves up to 5.7 times better total utility.

Achieving high performance in many multi-server systems (e.g., web hosting center, cloud) requires finding a good assignment of worker threads to servers and also effectively allocating each server’s resources to its assigned threads. The assignment and allocation components of this problem have been studied extensively but largely separately in the literature. In this paper, we introduce the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">assign and allocate (AA)</i> problem, which seeks to simultaneously find an assignment and allocation that maximizes the total utility of the threads. Assigning and allocating the threads together can result in substantially better overall utility than performing the steps separately, as is traditionally done. We model each thread by a utility function giving its performance as a function of its assigned resources. We first prove that the AA problem is NP-hard. We then present a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2 (\sqrt {2}-1) &gt; 0.828$ </tex-math></inline-formula> factor approximation algorithm for concave utility functions, which runs in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O(mn^{2} + n (\log mC)^{2})$ </tex-math></inline-formula> time for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> threads and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula> servers with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$C$ </tex-math></inline-formula> amount of resources each. We also give a faster algorithm with the same approximation ratio and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O(n (\log mC)^{2})$ </tex-math></inline-formula> time complexity. We then extend the problem to two more general settings. First, we consider threads with nonconcave utility functions, and give a 1/2 factor approximation algorithm. Next, we give an algorithm for threads using multiple types of resources, and show the algorithm achieves good empirical performance. We conduct extensive experiments to test the performance of our algorithms on threads with both synthetic and realistic utility functions, and find that they achieve over 92% of the optimal utility on average. We also compare our algorithms with a number of practical heuristics, and find that our algorithms achieve up to 9 times higher total utility.

The strategic allocation of resources to maximize fitness is the organizing principle shaping the evolution of life histories. The physiology and behaviour of animals is a manifestation of trade-offs in resource allocation among traits and activities that benefit current or future reproduction. In theory, traits subject to such trade-offs are expected to show negative correlations, but numerous field studies have documented apparent contradictions to theoretical expectations: in some populations, parental effort (allocation to current reproduction) and reproductive performance (i.e., a fitness component) are not negatively correlated to self-maintenance (allocation to future reproduction). One explanation for such findings is that inter-individual variation in resource acquisition can obscure trade-offs by altering the overall quantity of resources individuals are able to invest. Acquisition may influence both the quantity of resources allocated in trade-offs as well as reproductive success. Asymmetries in acquisition may be the product of differences in intrinsic quality (individual phenotypes) and/or environmental factors (territory quality). In a population of breeding mountain bluebirds (Sialia currucoides), I sought to determine how resource acquisition varied, and to identify how it may affect life-history trade-offs and reproductive performance. In my research, I used several lines of inquiry to characterize how resource acquisition varies and affects trade-offs in the study population. First, I showed that offspring quality, a contributor to fitness, is influenced by the types of prey nestling bluebirds are fed by their parents, and that the resources parents provide to their broods vary seasonally and as their nestlings age. Next, I used a short-term manipulation of brood age to find that parents have limited flexibility in their capacity to acquire resources while provisioning broods, which may be due to intrinsic or extrinsic constraints. I then identified relationships among the landscape characteristics of breeding territories (extrinsic factors, potentially influencing acquisition) and the reproductive performance of bluebirds, over an 11-year period. These characteristics, elevation and distance to forest edges, are subsequently linked to differences in microclimate, microhabitat, and parental prey use, providing a partial explanation for why occupancy and reproductive success are spatially clustered and consistent over time. Finally, I used a brood size manipulation to induce changes in parental effort, revealing that acquisition (quantified as both individual and territory quality) influences how mountain bluebirds trade-off one proxy for self-maintenance (energy metabolites) with providing food to their offspring. Interestingly, these effects differed between the sexes. Females generally biased allocation towards current reproduction, increasing provisioning to larger broods, regardless of territory quality; females on low-quality territories subsidized increased provisioning activity by catabolizing stored resources. In contrast, only males on high-quality territories increased parental effort in response to larger broods, and among these individuals, only high-quality males incurred an energy deficit to subsidize this activity. My findings showed that resource acquisition may be critical to understanding why trade-offs differ among individuals, and that both intrinsic and extrinsic limits on acquisition may influence the patterns of allocation we observe in wild populations.

While Peer-to-Peer streaming has become increasingly popular over the Internet during recent years, the proper allocation of available resources among peers in a resource constraint environment, remains a challenging problem. In a resource constraint environment, the allocated resources and thus delivered quality to individual peers should be proportional to their contribution to the system, i.e., resource allocation should be contribution aware. This in turn results in fairness among peers and encourages active contribution from participating peers which is essential for scalability of P2P systems. However, contribution-aware resource allocation is challenging due to the distributed and dynamic nature of resources in P2P systems. In this paper, we present a tax-based contribution-aware scheme for live mesh-based P2P streaming approaches. In our proposed scheme, individual peers use a tax function to determine their number of parent peers (i.e., their share of resources) based on the number of their child peers (i.e., peers’ contributed resources) and the aggregate available resources in the system. We examine the behavior of a commonly used tax function, and describe how the contribution aware scheme can leverage the tax function. Through extensive simulations we demonstrate the ability of our proposed scheme to properly allocate available resources among participating peers over a wide range of scenarios. We show that the amount of resources (i.e., bandwidth) is divided across peers proportional to their contribution and in our default simulation setting the median delivered quality to high bandwidth peers with high contribution is improved by 100%. We believe that our results shed an insightful light on the dynamics of resource utilization and allocation in the context of live mesh-based P2P streaming.

In this paper, we study a novel Stochastic Network Utility Maximization (NUM) problem where the utilities of agents are unknown. The utility of each agent depends on the amount of resource it receives from a network operator/controller. The operator desires to do a resource allocation that maximizes the expected total utility of the network. We consider threshold type utility functions where each agent gets non-zero utility if the amount of resource it receives is higher than a certain threshold. Otherwise, its utility is zero (hard real-time). We pose this NUM setup with unknown utilities as a regret minimization problem. Our goal is to identify a policy that performs as ‘good’ as an oracle policy that knows the utilities of agents. We model this problem setting as a bandit setting where feedback obtained in each round depends on the resource allocated to the agents. We propose algorithms for this novel setting using ideas from Multiple-Play Multi-Armed Bandits and Combinatorial Semi-Bandits. We show that the proposed algorithm is optimal when all agents have the same utility. We validate the performance guarantees of our proposed algorithms through numerical experiments.

Individuals scoring higher in tests of general cognitive abilities tend to perform better on novel and familiar mathematical tasks. It has been scarcely investigated how this superior mathematical performance relates to the amount of cognitive resources that is invested to solve a given task. In this study we propose that, on novel tasks, individuals with high cognitive abilities outperform less able individuals, because they allocate a higher amount of resources. On familiar tasks, however, individuals with higher abilities profit from more efficient processes compared to individuals of lower cognitive abilities. We tested this hypothesis by administering to 11th graders a geometric analogy task not practiced at school and an algebraic transformation task comprising operations that are routinely required during mathematical courses. General cognitive abilities were measured with Ravens Advanced Progressive matrices (fluid intelligence), the d2 (focused attention) and KAI-N (working memory capacity). Resource allocation was measured by assessing pupil diameter during the problem-solving process. Performance on both the analogy and the algebra task was correlated with general cognitive abilities, especially fluid intelligence. In line with our assumptions, a positive correlation between fluid intelligence and resource allocation was observed in the novel geometric analogy task, whereas the correlation was not significant in the more familiar algebra task.

BackgroundWith the increasing global aging population, how to allocate older people care resources reasonably has become an increasingly urgent international issue. China, as the largest developing country, has made many efforts to actively respond to the challenges of an aging population. However, there are still problems with uneven allocation of older people care resources and low efficiency of allocation. Therefore, this study evaluates the regional differences and dynamic evolution of the equity and efficiency of older people care resource allocation in China from 2009 to 2020, and explores ways to change the current situation.MethodsThe data used in this study were derived from the “China Statistical Yearbook” and the “China Civil Affairs Statistical Yearbook” for the period of 2010–2021. Firstly, the equity of older people care resource allocation was measured using the Gini coefficient, the Theil index, the Older People Care Resource Density Index, and the Older People Care Resource Agglomeration Degree. Secondly, the dynamic Slack-Based Measure data envelopment analysis method was adopted to evaluate efficiency. Lastly, the Z-score is used to normalize the equity index and perform classification matching with the efficiency value. Spatial autocorrelation analysis and hotspot analysis were conducted using GIS technology to examine the dynamic evolution process of older people care resource allocation equity and efficiency, as well as their spatial distribution patterns and coordination across provinces from 2009 to 2020.ResultsThe equity analysis showed that the spatial distribution of various types of older people care resources was uneven, and the differences were mainly due to internal differences within each region, with the largest equity differences observed in western provinces. Currently, older people care resources are mainly concentrated in eastern regions, while the total amount of older people care resources in western regions and some central regions is relatively small, which cannot meet the older people care needs of residents. The efficiency analysis results showed that the efficiency of older people care resource allocation has been improving over the past 12 years, and in 2020, 77.42% of provinces were located on the efficiency frontier with an average efficiency value of 0.9396. Finally, the coordination analysis results showed that there were significant spatiotemporal differences in the equity and efficiency of older people care resources allocation.ConclusionWith the development of society and economy, the total amount and service capacity of older people care resources in China have greatly improved. However, there are still significant spatiotemporal differences in the equity and efficiency of older people care resource allocation. The development of older people care services in central and eastern provinces is unbalanced, and there is a polarization trend in terms of equity and efficiency of older people care resource allocation. Most provinces in western regions face the dual dilemma of inadequate older people care resources and low utilization efficiency. It is recommended that policymakers comprehensively consider population and geographic factors in different provinces, establish relevant allocation standards according to local conditions, improve the redistribution system, and focus on increasing the total amount of older people care resources in underdeveloped provinces while promoting resource flow.

&lt;p&gt;Nonprofit partnership with government and with business is well documented in the literature. However partnership between nonprofit organisations has largely been ignored. This research focuses on such New Zealand partnerships. It investigates three aspects of partnership: the reasons why organisations partner, the resources they allocate to those partnerships, and the competencies used by the people managing the partnerships. How these three partnership aspects influence each other and the effect of that on the partnerships is also explored. The study focuses on the ‘partnership’ rather than the ‘co-existence’ end of Craig and Courtney’s (2004) continuum where elements such as working from agreed values, sharing resources and decision making, and developing systems to support the partnership will be evident. Two partnerships were selected as case studies. Data came from semi-structured interviews with seven participants from five partner organisations. The literature provided a framework for analysing the interview data in relation to the three aspects of partnership. This framework led to the identification of four new motivational factors, two new resource allocations and two new competencies. The literature also provided six theories that help explain partnership motivations. In addition to the three aspects (motivation, resources, and competencies) of partnership, it became clear that these things made a difference: · the term used to describe what they were doing – collaboration or partnership, · the context, · the presence of trust and goodwill, · voluntary participation in partnership, and · the level of formality. The findings demonstrated that the motivation to partner at all was the most important of the three aspects of partnership. It influenced resource allocations and the competencies used by partnership managers. Resource allocations did not influence the motivation to partner but influenced the competencies used by the partnership managers. And the competencies used by the partnership managers influenced the resources allocated by organisations but generally did not influence the motivation to partner. The findings add to our knowledge about nonprofit-nonprofit partnership and will help people to plan partnerships. I also propose five areas for further research: · factors that influence formality, · factors that prevent nonprofit partnership, · role of competencies in motivating partnership, · quantity of resources and the impact on outcomes, and · ownership of intellectual property.&lt;/p&gt;

Media streaming applications have recently attracted large number of users in the Internet. With the advent of these bandwidth-intensive applications, it is difficult to provide streaming distribution with guaranteed QoS relying only on central resources at the content provider. Cloud computing offers an elastic infrastructure that media content providers (e.g., VoD provider) can use to obtain resources on-demand. Since a media content provider is charged for amount of resources (bandwidth) rented from the cloud, an open problem is to decide on the right amount of resources allocated in the cloud and their reservation time such that the financial cost on the content provider is minimized. We consider a practical pricing model that is based on a non-linear tariff (i.e., a pricing scheme that depends non-linearly on the resources purchased or time reserved). We formulate the optimization problem based on prediction of future streaming demand. We then propose a simple (easy to implement) algorithm for resource allocation that exploits the non-linearity in the price contract, while ensuring that sufficient resources is reserved in the cloud without incurring wastage. The results of our numerical evaluation and simulations show that the proposed algorithm mimics the optimum solution very well.

The conventional resource allocation method for civil aviation security post competency course reform is based on post elements, and the allocated resources are more considering the post level, which can not meet the adaptability demand of resource allocation. Therefore, a resource allocation method based on big data multimedia technology is designed for the reform of civil aviation security post competency course. We determine the set of big data cluster elements for resource allocation of security inspection job competency course, and deeply understand the connotation of civil aviation security inspection job competency, so as to adjust the resource allocation strategy at any time. Based on the big data multimedia technology, we construct a resource allocation model for the reform of security inspection job competency course, collect data related to security inspection job competency, and comprehensively assess the job competency. The optimal parameters of resource allocation for the reform of civil aviation security post competency course are adjusted to adapt to multiple course reform scenarios by adjusting the type, quantity and quality of course resources, the skill level, learning speed and willingness to learn of the security personnel, as well as the duration, frequency and mode of the training courses. Comparative experiments are used to verify that the method is more effective in resource allocation and can be applied in real life.

We study reconfigurations of wavelength-routed Wavelength Division Multiplexing (WDM) networks in response to lightpath demand changes, with the objective of servicing more lightpath demands without additional network resources from a long-term network operation point of view. For the reconfiguration problem under study, we assume WDM network operators are provided with lightpath demands in batches. With limited network resources, our problem has two unique challenges: balancing network resource allocations between current and future lightpath demands, and modeling future lightpath demands. The first challenge implies making tradeoffs between accepting as many current immediate lightpath demands as possible and reserving a certain amount of network resources for near future predicted lightpath demands. The second challenge implies modeling future predicted lightpath demands, which are not exactly known or certain as the current lightpath demands. Our proposed model allows a natural separation between the operation of the optical layer and the user traffic layer (predominantly the IP-layer), while supporting their interactions, for which we propose a new formulation for per-link congestion control, associated with a mathematical solution procedure. Our simulation results reveal that by properly controlling resource allocations in the current session using our proposed mechanism, rejections in future sessions are greatly reduced.

Tabu search for discrete–continuous scheduling problems with heuristic continuous resource allocation