Fair Caching Networks

2021 ◽  
Vol 48 (3) ◽  
pp. 89-90
Author(s):  
Yuezhou Liu ◽  
Yuanyuan Li ◽  
Qian Ma ◽  
Stratis Ioannidis ◽  
Edmund Yeh
Keyword(s):  
Real World ◽  
Np Hard ◽  
Allocation Strategy ◽  
Gain Rate ◽  
Network Topologies ◽  
Allocation Strategies

We study fair content allocation strategies in caching networks through a utility-driven framework, where each request achieves a utility of its caching gain rate. The resulting problem is NP-hard. Submodularity allows us to devise a deterministic allocation strategy with an optimality guarantee factor arbitrarily close to 1-1/e. When 0 < α ≤ 1, we further propose a randomized strategy that attains an improved optimality guarantee, (1 - 1/e)1-α, in expectation. Through extensive simulations over synthetic and real-world network topologies, we evaluate the performance of our proposed strategies and discuss the effect of fairness.

scholarly journals Offline Algorithms in Low-Frequency Trading

Queue ◽  
2020 ◽  
Vol 18 (6) ◽  
pp. 37-51
Author(s):  
Terence Kelly
Keyword(s):  
Real Time ◽  
Real World ◽  
High Frequency ◽  
Low Frequency ◽  
Combinatorial Auctions ◽  
Hard Problem ◽  
Np Hard ◽  
High Stakes ◽  
Drill Bits ◽  
Np Hard Problem

Expectations run high for software that makes real-world decisions, particularly when money hangs in the balance. This third episode of the Drill Bits column shows how well-designed software can effectively create wealth by optimizing gains from trade in combinatorial auctions. We'll unveil a deep connection between auctions and a classic textbook problem, we'll see that clearing an auction resembles a high-stakes mutant Tetris, we'll learn to stop worrying and love an NP-hard problem that's far from intractable in practice, and we'll contrast the deliberative business of combinatorial auctions with the near-real-time hustle of high-frequency trading. The example software that accompanies this installment of Drill Bits implements two algorithms that clear combinatorial auctions.

Fragment Re-Allocation Strategy Based on Hypergraph for NoSQL Database Systems

2016 ◽  
Vol 8 (3) ◽  
pp. 1-23 ◽  
Author(s):  
Zhikun Chen ◽  
Shuqiang Yang ◽  
Yunfei Shang ◽  
Yong Liu ◽  
Feng Wang ◽  
...  
Keyword(s):  
Database Systems ◽  
Communication Cost ◽  
Allocation Strategy ◽  
Hypergraph Partitioning ◽  
Computing Model ◽  
Operation Pattern ◽  
Nosql Database ◽  
High Scalability ◽  
Weighted Hypergraph ◽  
Allocation Strategies

NoSQL database is famed for the characteristics of high scalability, high availability, and high fault-tolerance. It is used to manage data for a lot of applications. The computing model has been transferred to “computing close to data”. Therefore, the location of fragment directly affects system's performance. Every site's load dynamical changes because of the increasing data and the ever-changing operation pattern. So system has to re-allocate fragment to improve system's performance. The general fragment re-allocation strategies of NoSQL database scatter the related fragments as possible to improve the operations' parallel degree. But those fragments may interact with each other in some application's operations. So the high parallel degree of operation may increase system's communication cost such as data are transferred by network. In this paper, the authors propose a fragment re-allocation strategy based on hypergraph. This strategy uses a weighted hypergraph to represent the fragments' access pattern of operations. A hypergraph partitioning algorithm is used to cluster fragments in the strategy. This strategy can improve system's performance according to reducing the communication cost while guaranteeing the parallel degree of operations. Experimental results confirm that the strategy will effectively contribute in solving fragment re-allocation problem in specific application environment of NoSQL database system, and it can improve system's performance.

Deriving network topologies from real world constraints

2010 ◽  
Author(s):  
Mahmood A. Hameed ◽  
Abdul Jabbar ◽  
Egemen K. Cetinkaya ◽  
James P.G. Sterbenz
Keyword(s):  
Real World ◽  
Network Topologies

scholarly journals Evaluation of the United States COVID-19 Vaccine Allocation Strategy

2021 ◽  
Author(s):  
Md Rafiul Islam ◽  
Tamer Oraby ◽  
Audrey McCombs ◽  
Mohammad Mihrab Chowdhury ◽  
Mohammad Al-Mamun ◽  
...  
Keyword(s):  
United States ◽  
Disease Model ◽  
Age Groups ◽  
Optimal Strategies ◽  
The United States ◽  
Living Condition ◽  
Years Of Life Lost ◽  
Allocation Strategy ◽  
Optimal Allocations ◽  
Allocation Strategies

Background: Anticipating an initial shortage of vaccines for COVID-19, the Centers for Disease Control (CDC) in the United States developed priority vaccine allocations for specific demographic groups in the population. This study evaluates the performance of the CDC vaccine allocation strategy with respect to multiple potentially competing vaccination goals (minimizing mortality, cases, infections, and years of life lost (YLL)), under the same framework as the CDC allocation: four priority vaccination groups and population demographics stratified by age, comorbidities, occupation and living condition (congested or non-congested). Methods: We developed a compartmental disease model that incorporates key elements of the current pandemic including age-varying susceptibility to infection, age-varying clinical fraction, an active case-count dependent social distancing level, and time-varying infectivity (accounting for the emergence of more infec- tious virus strains). Under this model, the CDC allocation strategy is compared to all other possibly optimal allocations that stagger vaccine roll-out in up to four phases (17.5 million strategies). Results: The CDC allocation strategy performed well in all vaccination goals but never optimally. Under the developed model, the CDC allocation deviated from the optimal allocations by small amounts, with 0.19% more deaths, 4.0% more cases, 4.07% more infections, and 0.97% higher YLL, than the respective optimal strategies. The CDC decision to not prioritize the vaccination of individuals under the age of 16 was optimal, as was the prioritization of health-care workers and other essential workers over non-essential workers. Finally, a higher prioritization of individuals with comorbidities in all age groups improved outcomes compared to the CDC allocation. Interpretation: The developed approach can be used to inform the design of future vaccine allocation strategies in the United States, or adapted for use by other countries seeking to optimize the effectiveness of their vaccine allocation strategies. Funding: The authors received no funding for this work.

Submesh Allocation in 3D Mesh Multicomputers Using Free Lists

2015 ◽  
pp. 171-187
Author(s):  
Saad Bani-Mohammad ◽  
Ismail Ababneh ◽  
Motasem Al Smadi
Keyword(s):  
Job Scheduling ◽  
Substantial Effect ◽  
Allocation Strategy ◽  
3D Mesh ◽  
Extensive Simulation ◽  
System Utilization ◽  
Service Demand ◽  
Extensive Evaluation ◽  
Simulation Results ◽  
Allocation Strategies

This chapter presents an extensive evaluation of a new contiguous allocation strategy proposed for 3D mesh multicomputers. The strategy maintains a list of maximal free sub-meshes and gives priority to allocating corner and boundary free sub-meshes. This strategy, which we refer to as Turning Corner-Boundary Free List (TCBFL) strategy, is compared, using extensive simulation experiments, to several existing allocation strategies for 3D meshes. In addition to allocation strategies, two job scheduling schemes, First-Come-First-Served (FCFS) and Shortest-Service-Demand (SSD) are considered in comparing the performance of the allocation strategies. The simulation results show that TCBFL produces average turnaround times and mean system utilization values that are superior to those of the existing allocation strategies. The results also reveal that SSD scheduling is much better than FCFS scheduling. Thus, the scheduling and allocation strategies both have substantial effect on the performance of contiguous allocation strategies in 3D mesh-connected multicomputers.

scholarly journals MISSING DATA AS PART OF THE SOCIAL BEHAVIOR IN REAL-WORLD FINANCIAL COMPLEX SYSTEMS

2018 ◽  
Vol 21 (01) ◽  
pp. 1850002 ◽  
Author(s):  
GUY KELMAN ◽  
ERAN MANES ◽  
MARCO LAMIERI ◽  
DAVID S. BRÉE
Keyword(s):  
Complex Systems ◽  
Real World ◽  
Missing At Random ◽  
Strategic Information ◽  
The Social ◽  
Network Topologies ◽  
Not Missing At Random ◽  
Information Withholding ◽  
Data Collection Procedure

Many real-world networks are known to exhibit facts that counter our knowledge prescribed by the theories on network creation and communication patterns. A common prerequisite in network analysis is that information on nodes and links will be complete because network topologies are extremely sensitive to missing information of this kind. Therefore, many real-world networks that fail to meet this criterion under random sampling may be discarded.In this paper, we offer a framework for interpreting the missing observations in network data under the hypothesis that these observations are not missing at random. We demonstrate the methodology with a case study of a financial trade network, where the awareness of agents to the data collection procedure by a self-interested observer may result in strategic revealing or withholding of information. The non-random missingness has been overlooked despite the possibility of this being an important feature of the processes by which the network is generated. The analysis demonstrates that strategic information withholding may be a valid general phenomenon in complex systems. The evidence is sufficient to support the existence of an influential observer and to offer a compelling dynamic mechanism for the creation of the network.

A New Compacting Non-Contiguous Processor Allocation Algorithm for 2D Mesh Multicomputers

2015 ◽  
Vol 8 (4) ◽  
pp. 57-75 ◽  
Author(s):  
Saad Bani-Mohammad ◽  
Ismail M. Ababneh ◽  
Mohammad Yassen
Keyword(s):  
Performance Improvement ◽  
Communication Overhead ◽  
Allocation Strategy ◽  
Processor Allocation ◽  
Improve Performance ◽  
Simulation Experiments ◽  
Extensive Simulation ◽  
System Utilization ◽  
Selection Of ◽  
Allocation Strategies

In non-contiguous allocation, a job request can be split into smaller parts that are allocated possibly non-adjacent free sub-meshes rather than always waiting until a single sub-mesh of the requested size and shape is available. Lifting the contiguity condition is expected to reduce processor fragmentation and increase system utilization. However, the distances traversed by messages can be long, and as a result the communication overhead, especially contention, is likely to increase. The extra communication overhead depends on how the allocation request is partitioned and assigned to free sub-meshes. In this paper, a new non-contiguous processor allocation strategy, referred to as Compacting Non-Contiguous Processor Allocation Strategy (CNCPA), is suggested for the 2D mesh multicomputers. In the proposed strategy, a job is compacted into free locations. The selection of the free locations has for goal leaving large free sub-meshes in the system. To evaluate the performance improvement achieved by the proposed strategy and compare it against well-known existing non-contiguous allocation strategies, the authors conducted extensive simulation experiments. The results show that the proposed strategy can improve performance in terms of job turnaround times and system utilization.

scholarly journals Evaluation of the United States COVID-19 vaccine allocation strategy

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0259700
Author(s):  
Md Rafiul Islam ◽  
Tamer Oraby ◽  
Audrey McCombs ◽  
Mohammad Mihrab Chowdhury ◽  
Mohammad Al-Mamun ◽  
...  
Keyword(s):  
United States ◽  
Disease Model ◽  
Age Groups ◽  
Optimal Strategies ◽  
The United States ◽  
Living Condition ◽  
Years Of Life Lost ◽  
Allocation Strategy ◽  
Optimal Allocations ◽  
Allocation Strategies

Background Anticipating an initial shortage of vaccines for COVID-19, the Centers for Disease Control (CDC) in the United States developed priority vaccine allocations for specific demographic groups in the population. This study evaluates the performance of the CDC vaccine allocation strategy with respect to multiple potentially competing vaccination goals (minimizing mortality, cases, infections, and years of life lost (YLL)), under the same framework as the CDC allocation: four priority vaccination groups and population demographics stratified by age, comorbidities, occupation and living condition (congested or non-congested). Methods and findings We developed a compartmental disease model that incorporates key elements of the current pandemic including age-varying susceptibility to infection, age-varying clinical fraction, an active case-count dependent social distancing level, and time-varying infectivity (accounting for the emergence of more infectious virus strains). The CDC allocation strategy is compared to all other possibly optimal allocations that stagger vaccine roll-out in up to four phases (17.5 million strategies). The CDC allocation strategy performed well in all vaccination goals but never optimally. Under the developed model, the CDC allocation deviated from the optimal allocations by small amounts, with 0.19% more deaths, 4.0% more cases, 4.07% more infections, and 0.97% higher YLL, than the respective optimal strategies. The CDC decision to not prioritize the vaccination of individuals under the age of 16 was optimal, as was the prioritization of health-care workers and other essential workers over non-essential workers. Finally, a higher prioritization of individuals with comorbidities in all age groups improved outcomes compared to the CDC allocation. Conclusion The developed approach can be used to inform the design of future vaccine allocation strategies in the United States, or adapted for use by other countries seeking to optimize the effectiveness of their vaccine allocation strategies.

scholarly journals Competition alters predicted forest carbon cycle responses to nitrogen availability and elevated CO<sub>2</sub>: simulations using an explicitly competitive, game-theoretic vegetation demographic model

2019 ◽  
Author(s):  
Ensheng Weng ◽  
Ray Dybzinski ◽  
Caroline E. Farrior ◽  
Stephen W. Pacala
Keyword(s):  
Carbon Cycle ◽  
Elevated Co2 ◽  
Fine Roots ◽  
Nitrogen Availability ◽  
Optimal Allocation ◽  
Plant Biomass ◽  
Allocation Strategy ◽  
Terrestrial Carbon Cycle ◽  
Competition For Light ◽  
Allocation Strategies

Abstract. Competition is a major driver of carbon allocation to different plant tissues (e.g. wood, leaves, fine roots), and allocation, in turn, shapes vegetation structure. To improve their modeling of the terrestrial carbon cycle, many Earth system models now incorporate vegetation demographic models (VDMs) that explicitly simulate the processes of individual-based competition for light and soil resources. Here, in order to understand how these competition processes affect predictions of the terrestrial carbon cycle, we simulate forest responses to elevated CO2 along a nitrogen availability gradient using a VDM that allows us to compare fixed allocation strategies versus competitively-optimal allocation strategies. Our results show that competitive- and fixed-allocation strategies predict opposite fractional allocation to fine roots and wood, though they predict similar changes in total NPP along the nitrogen gradient. The competitively-optimal allocation strategy predicts decreasing fine root and increasing wood allocation with increasing nitrogen, whereas the fixed allocation strategy predicts the opposite. Although simulated plant biomass at equilibrium increases with nitrogen due to increases in photosynthesis for both allocation strategies, the increase in biomass with nitrogen is much steeper for competitively-optimal allocation due to its increased allocation to wood. The qualitatively opposite fractional allocation to fine roots and wood of the two strategies also impacts the effects of elevated [CO2] on plant biomass. Whereas the fixed allocation strategy predicts an increase in plant biomass under elevated [CO2] that is approximately independent of nitrogen availability, competition’s effect on wood allocation amplifies plant biomass under elevated [CO2] with increasing nitrogen availability. Our results indicate that the VDMs that explicitly include the effects of competition for light and soil resources on plant strategies may generate significantly different ecosystem-level predictions than those that use fixed allocation strategies.

scholarly journals Matchings with Group Fairness Constraints: Online and Offline Algorithms

2021 ◽  
Author(s):  
Govind S. Sankar ◽  
Anand Louis ◽  
Meghana Nasre ◽  
Prajakta Nimbhorkar
Keyword(s):  
School Choice ◽  
Approximation Algorithms ◽  
Real World ◽  
Upper Bound ◽  
Upper Bounds ◽  
Np Hard ◽  
Resource Allocations ◽  
Fairness Constraints ◽  
Ad Auctions ◽  
Real World Problems

We consider the problem of assigning items to platforms in the presence of group fairness constraints. In the input, each item belongs to certain categories, called classes in this paper. Each platform specifies the group fairness constraints through an upper bound on the number of items it can serve from each class. Additionally, each platform also has an upper bound on the total number of items it can serve. The goal is to assign items to platforms so as to maximize the number of items assigned while satisfying the upper bounds of each class. This problem models several important real-world problems like ad-auctions, scheduling, resource allocations, school choice etc. We show that if the classes are arbitrary, then the problem is NP-hard and has a strong inapproximability. We consider the problem in both online and offline settings under natural restrictions on the classes. Under these restrictions, the problem continues to remain NP-hard but admits approximation algorithms with small approximation factors. We also implement some of the algorithms. Our experiments show that the algorithms work well in practice both in terms of efficiency and the number of items that get assigned to some platform.

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