Fluid Models of Parallel Service Systems Under FCFS

In parallel service systems, customers of various types are served simultaneously by several servers that have some dierent and some overlapping skills or capacities. Examples include customer routing in call centers, scheduling of hospital operating rooms, and driver assignment in ride-hailing services. First come first served (FCFS) is a natural and widespread resource allocation policy, yet its performance both during the transient stage and in the long run is often difficult to analyze. In particular, matching rates, which indicate what fraction from all services are of a specific server customer type, are difficult to evaluate under FCFS. In Fluid Models of Parallel Service Systems under FCFS, Yuval Nov, Gideon Weiss, and Hanqin Zhang formulate a stochastic queueing model for such systems and study its fluid approximation. The paper demonstrates the usefulness and the limitations of exploring fluid performance in evaluating system behavior in terms of stability, responsiveness, and utilization.

Fault Tolerant - Optimal Resource Allocator for Cloud

Cloud Computing is mainly attracted by people for its unlimited storage space and worldwide accessibility from anywhere and anytime. The data that is stored in the cloud has to be retrieved in a faster manner as well as without any faults. Content Delivery Networks dominates Cyberspace traffic heavily due to its increasing demand. Resource allocation plays a vital role in determining the performance of the cloud. Over allocation leads to wastage which can be used for instances that are running short of resources. This work proposes an optimal resource allocation through Genetic algorithm. They help in increasing the download speed which in turn would be helpful in controlling traffic to greater extent. Recently cloud downloading Services have been emerged, in which cloud storage hoards the user interested content and updates the cloud cache. This process takes place in two modes, server mode and the helper mode. The proposed Resource Allocation Policy can well support the server mode of processing by the way it can increment the download speed with dynamic load balancing and fault tolerance.

Evaluating effects of recent changes in NHS resource allocation policy on inequalities in amenable mortality in England, 2007–2014: time-series analysis

BackgroundHealth investment in England post-2010 has increased at lower rates than previously, with proportionally less being allocated to deprived areas. This study seeks to explore the impact of this on inequalities in amenable mortality between local areas.MethodsWe undertook a time-series analysis across 324 lower-tier local authorities in England, evaluating the impact of changes in funding allocations to health commissioners from 2007 to 2014 on spatial inequalities in age-standardised under-75 mortality rates for conditions amenable to healthcare for men and women, adjusting for trends in household income, unemployment and time-trends.ResultsMore deprived areas received proportionally more funding between 2007 and 2014, though the reorganisation of commissioning in 2012 stalled this. Funding increases to more deprived local areas accounted for a statistically significant reduction in inequalities in male amenable mortality between local areas of 13 deaths per 100 000 (95% CI 2.5 to 25.9). Funding changes were associated with a reduction in inequalities in female amenable mortality of 7.0 per 100,000, though this finding did not reach significance (p=0.09).ConclusionCurrent National Health Service (NHS) resource allocation policy in England appears to be contributing to a convergence in health outcomes between affluent and deprived areas. However, careful surveillance is needed to evaluate whether diminished allocations to more deprived areas in recent years and reduced NHS investment as a whole is impacting adversely on inequalities between groups.

Fair Resource Allocation with QoS Guarantee in Secure Multiuser TDMA Networks

We investigate a secure multiuser time division multiple access (TDMA) system with statistical delay quality of service (QoS) guarantee in terms of secure effective capacity. An optimal resource allocation policy is proposed to minimize the β-fair cost function of the average user power under the individual QoS constraint, which also balances the energy efficiency and fairness among the users. First, convex optimization problems associated with the resource allocation policy are formulated. Then, a subgradient iteration algorithm based on the Lagrangian duality theory and the dual decomposition theory is employed to approach the global optimal solutions. Furthermore, considering the practical channel conditions, we develop a stochastic subgradient iteration algorithm which is capable of dynamically learning the intended wireless channels and acquiring the global optimal solution. It is shown that the proposed optimal resource allocation policy depends on the delay QoS requirement and the channel conditions. The optimal policy can save more power and achieve the balance of the energy efficiency and the fairness compared with the other resource allocation policies.