Multi-resource Environment Research Articles

Macroeconomic dynamics in a finite world based on thermodynamic potential

This paper presents a conceptual model describing the medium and long term co-evolution of natural and socio-economic subsystems of Earth. An economy is viewed as an out-of-equilibrium dissipative structure that can only be maintained with a flow of energy and matter. The distinctive approach emphasized here consists in capturing the economic impact of natural ecosystems’ depletion by human activities via a pinch of thermodynamic potentials. This viewpoint allows: (i) the full-blown integration of a limited quantity of primary resources into a non-linear macrodynamics that is stock-flow consistent both in terms of matter-energy and economic transactions; (ii) the inclusion of natural and forced recycling; (iii) the inclusion of a friction term which reflects the impossibility to produce (and recycle)goods and services without exuding energy and matter wastes, and (iv) the computation of the anthropically produced entropy as a function of metabolizing intensity and frictions. Analysis and numerical computations confirm the role played by intensity and frictions as key factors for sustainability by contrast with real gdp growth—as well as the interplay between resource scarcity, income inequality, and inflation. A more egalitarian society with moderate inflation turns out to be more sustainable than an unequal society with low inflation. Our approach is flexible enough to allow for various economic models to be embedded into our thermodynamic framework. Finally, we propose the open source EcoDyco software as a first complete realization implementing economic dynamics in a multi-resource environment.

A two-step stochastic approach for operating rooms scheduling in multi-resource environment

Planning and scheduling of operating rooms (ORs) is important for hospitals to improve efficiency and achieve high quality of service. Due to significant uncertainty in surgery durations, scheduling of ORs can be very challenging. In this paper, surgical case scheduling problem with uncertain duration of surgeries in multi resource environment is investigated. We present a two-stage stochastic mixed-integer programming model, named SOS, with the objective of total ORs idle time and overtime. Also, in this paper a two-step approach is proposed for solving the model based on the L-shaped algorithm. Proposing the model in a multi resources environment with considering real-life limitations in academic hospitals and developing an approach for solving this stochastic model efficiently form the main contributions of this paper. The model is evaluated through several numerical experiments based on real data from Hasheminejad Kidney Center (HKC) in Iran. The solutions of SOS are compared with the deterministic solutions in several real instances. Numerical results show that SOS enjoys a better performance in 97% of the cases. Furthermore, the results of comparing with actual schedules applied in HKC reveal a notable reduction of OR idle time and over time which illustrate the efficiency of the proposed model in practice.

Coflow Scheduling in the Multi-Resource Environment

In data centers, a lot of cluster computing applications follow the coflow working pattern. That is, a collection of flows between two groups of machines is semantically related. On the other hand, network function virtualization sufficiently improves the performance of data center networks. It however complicates the network environment by introducing many multi-function middleboxes each with multiple resources. Coflows encounter extremely different processing delays under diverse network functions. Prior coflow scheduling schemes are insufficient to guarantee the coflow completion time in the multi-resource environment. In this paper, we propose, model, and analyze the coflow scheduling problem in the multi-resource environment. We present a dedicated method, data rate guarantee for coflow (DRGC), to guarantee the data rate requirements of coflows in this situation. DRGC prioritizes the coflow scheduling sequence, assigns precise data rates for coflows, and deploys a packet scheduling algorithm at middleboxes to guarantee their transmissions. In our experiments, DRGC efficiently guarantees the completion times of coflows and supports 15% more workload, compared with other scheduling schemes.

Sustainable green data center: Guaranteeing flow deadlines in chains of virtual network functions with MRouting

Power consumption has always been a challenging problem in data centers. Green data center has been proposed to alleviate the growing tendency of power consumption from the aspects of architecture and resource management. For improving the network performance and guaranteeing the quality of service (QoS) requirements of data flows, various kinds of network function virtualization (NFV) devices, which are equipped with multiple hardware resources, are ubiquitously deployed in data centers. Meanwhile, a chain of virtual network functions (VNFs) is usually needed to be sequentially executed on flows. Existing queueing schemes used in multi-resource environments are insufficient to guarantee the QoS requirements of flows in this situation. Based on this observation, we modeled and analyzed the flow scheduling problem in chains of VNFs. Further, we proposed a scheduling framework, named MRouting, to guarantee the deadline requirement of flows. MRouting determines the scheduling sequence of flows according to their urgency degree. It also implements appropriate data rate control and routing selection for flows, so that they can finish their transmissions before their deadlines. We have conducted extensive experiments to evaluate the performance of MRouting. The results verified that MRouting efficiently guaranteed the deadlines of flows, and also achieved obvious improvements on flow completion time and resource utilization.

ATFQ: A Fair and Efficient Packet Scheduling Method in Multi-Resource Environments

Large-scale data centers are the key infrastructures for hosting and running a variety of applications. Besides traditional L2/L3 devices, middleboxes are widely deployed in data centers and perform many important functions, e.g., the intrusion detection and firewall. Middleboxes are equipped with multiple kinds of resources, such as CPU and memory. Data flows undergoing different functions have heterogeneous processing time requirements on diverse resources. Researchers are in a dilemma as to how to provide fair service for flows and efficiently utilize those scarce resources. To address this problem, we propose a novel packet scheduling method, active time fairness queuing (ATFQ), for multi-resource environments. Prior packet scheduling methods usually focus on pursuing the fairness among flows, resulting in enormous waste of those scarce resources. ATFQ overcomes this essentially by redefining the fairness and can maximize the resource utilization with the guarantee of fairness. We conduct extensive simulations to evaluate the performance of ATFQ. The evaluation results demonstrate that flows get better service in many aspects under ATFQ. Meanwhile, the resource utilization rises up by about 10% than the traditional DRFQ, which is one of the mainstream involved methods.

MDFS: Deadline-Driven Flow Scheduling Scheme in Multi-Resource Environments

Data centers have emerged as infrastructures for deploying various applications and services. To improve the security and performance, middleboxes are vastly deployed inside data centers to perform a large range of functionalities. Each of such middleboxes is equipped with diverse resources. Compared with traditional switches, middleboxes analyze the content of packets. This leads to the long processing time for flows passing through a middlebox. Additionally, executing different functionalities incurs diverse consumption on resources. Consequently, data flows undergoing different function components need different processing time on diverse resources. How to complete the transmission of such flows before their deadlines when passing through a middlebox comes out to be an essential issue, which lacks effective solutions. In this paper, we propose multi-resource & deadline-driven flow scheduling (MDFS) to satisfy the deadline requirements of flows in multi-resource environments. Besides guaranteeing the deadline, MDFS treats flows fairly and provides reliable service for them. To the best of our knowledge, this is the first paper trying to solve the deadline-driven flow scheduling problem in a multi-resource environment. With respect to the performance evaluation, up to $90$ percent flows meet their deadlines in normal conditions by using MDFS, which greatly outperforms the performance of other scheduling schemes.

The effect of selection environment on the probability of parallel evolution.

Across the great diversity of life, there are many compelling examples of parallel and convergent evolution-similar evolutionary changes arising in independently evolving populations. Parallel evolution is often taken to be strong evidence of adaptation occurring in populations that are highly constrained in their genetic variation. Theoretical models suggest a few potential factors driving the probability of parallel evolution, but experimental tests are needed. In this study, we quantify the degree of parallel evolution in 15 replicate populations of Pseudomonas fluorescens evolved in five different environments that varied in resource type and arrangement. We identified repeat changes across multiple levels of biological organization from phenotype, to gene, to nucleotide, and tested the impact of 1) selection environment, 2) the degree of adaptation, and 3) the degree of heterogeneity in the environment on the degree of parallel evolution at the gene-level. We saw, as expected, that parallel evolution occurred more often between populations evolved in the same environment; however, the extent of parallel evolution varied widely. The degree of adaptation did not significantly explain variation in the extent of parallelism in our system but number of available beneficial mutations correlated negatively with parallel evolution. In addition, degree of parallel evolution was significantly higher in populations evolved in a spatially structured, multiresource environment, suggesting that environmental heterogeneity may be an important factor constraining adaptation. Overall, our results stress the importance of environment in driving parallel evolutionary changes and point to a number of avenues for future work for understanding when evolution is predictable.

Search and track coordination in multi-ship multi-radar systems using schedulability envelope

This paper addresses the search and track coordination problems of multiple shipboard radars. The proposed approach first exploits the physical characteristics of a single phased array radar to improve its effective capacity. Its effective capacity is abstracted by a closed-form equation called a schedulability envelope. Using the schedulability envelope for each radar, we deal with search and track coordination as a relative-load-balancing problem in a multi-resource environment. The simulation results show that the proposed approach significantly improves the overall capacity of a multi-ship multi-radar system.

In a multi-resource environment, how much is enough?

One of the important elements of strategic management is to establish resource levels over the long run—capacity planning. More specifically, managers are faced with the need to determine resource levels that are sufficient to meet corporate objectives and remain competitive; and, yet, not excessive. This research has explored the relationship between ten different availability levels of resources in a constrained, multiple resource environment and their impact on project completion times and promise date performance measures. It also evaluated four finite scheduling heuristics which use a finite scheduling system and are used to estimate and, then, meet a project's completion date in the dynamic, multi-project environment. As a part of the discussion we examine the tradeoff between project completion times and resource availability levels. This research has found that (1) three of the four tested finite scheduling heuristics can produce/meet very good promise dates; (2)-resources do not need to be provided in quantities that exceed 160% of system requirements; (3) when resources are provided at the 130% or greater level, all four heuristics perform equally well with regard to completion times, and complete projects at less than one and a half times a project's critical path time; and (4) when resources are greatly constrained (110% to 130%) the effects are significant and the choice of heuristic is very important.