3,073 research outputs found
Dynamic Routing Algorithms and Methods for Controlling Traffic Flows of Cloud Applications and Services
Nowadays, we see a steady growth in the use of cloud computing in modern business. This enables to reduce the cost of IT infrastructure owning and operation; however, there are some issues related to the management of data processing centers.One of these issues is the effective use of companies’ computing and network resources. The goal of optimization is to manage the traffic in cloud applications and services within data centers.Taking into account the multitier architecture of modern data centers, we need to pay a special attention to this task. The advantage of modern infrastructure virtualization is the possibility to use software-defined networks and software-defined data storages. However, the existing optimization of algorithmic solutions does not take into account the specific features of the network traffic formation with multiple application types.The task of optimizing traffic distribution for cloud applications and services can be solved by using software-defined infrastructure of virtual data centers.We have developed a simulation model for the traffic in software-defined networks segments of data centers involved in processing user requests to cloud application and services within a network environment.Our model enables to implement the traffic management algorithm of cloud applications and to optimize the access to storage systems through the effective use of data transmission channels. During the experimental studies, we have found that the use of our algorithm enables to decrease the response time of cloud applications and services and, therefore, to increase the productivity of user requests processing and to reduce the number of refusals
Optimization of Airfield Parking and Fuel Asset Dispersal to Maximize Survivability and Mission Capability Level
While the US focus for the majority of the past two decades has been on combatting insurgency and promoting stability in Southwest Asia, strategic focus is beginning to shift toward concerns of conflict with a near-peer state. Such conflict brings with it the risk of ballistic missile attack on air bases. With 26 conflicts worldwide in the past 100 years including attacks on air bases, new doctrine and modeling capacity are needed to enable the Department of Defense to continue use of vulnerable bases during conflict involving ballistic missiles. Several models have been developed to date for Air Force strategic planning use, but these models have limited use on a tactical level or for civil engineer use. This thesis presents the development of a novel model capable of identifying base layout characteristics for aprons and fuel depots to maximize dispersal and minimize impact on sortie generation times during normal operations. This model is implemented using multi-objective genetic algorithms to identify solutions that provide optimal tradeoffs between competing objectives and is assessed using an application example. These capabilities are expected to assist military engineers in the layout of parking plans and fuel depots that ensure maximum resilience while providing minimal impact to the user while enabling continued sortie generation in a contested region
A Framework for Differential Frame-Based Matching Algorithms in Input-Queued Switches
This article is made available under terms and conditions applicable to Open Access Policy Articl
Lazy auctions for multi-robot collision avoidance and motion control under uncertainty
We present an auction-flavored multi-robot planning mechanism where coordination is to be achieved on the occupation of atomic resources modeled as binary inter-robot constraints. Introducing virtual obstacles, we show how this approach can be combined with particlebased obstacle avoidance methods, offering a decentralized, auction-based alternative to previously established centralized approaches for multirobot open-loop control. We illustrate the effectiveness of our new approach by presenting simulations of typical spatially-continuous multirobot path-planning problems and derive bounds on the collision probability in the presence of uncertainty
Enhancing Dynamic Production Scheduling And Resource Allocation Through Adaptive Control Systems With Deep Reinforcement Learning
Traditional production scheduling and resource allocation methods often struggle to adapt to changing conditions in manufacturing environments. To address this challenge, this study leverages an adaptive control system integrated with a Deep Deterministic Policy Gradient (DDPG) alongside a particle swarm optimization algorithm to enable real-time production scheduling and allocation of resources. The system continuously learns from generated production data and adjusts production schedules with resource allocations based on evolving conditions such as demand fluctuations and resource availability. By harnessing the capabilities of Deep Reinforcement learning, the proposed approach of applying the DDPG algorithm to simulate the environment improves production efficiency, minimizes delays, and optimizes resource utilization. Through conducted experiments, the effectiveness of the DDPG-Particle Swarm Optimization technique (DRPO) was demonstrated in enhancing dynamic production scheduling and resource allocation in simulated manufacturing settings. This study presents a significant step towards intelligent, self-improving production control systems that can navigate complex and dynamic manufacturing environments
Multi-Path Alpha-Fair Resource Allocation at Scale in Distributed Software Defined Networks
The performance of computer networks relies on how bandwidth is shared among
different flows. Fair resource allocation is a challenging problem particularly
when the flows evolve over time. To address this issue, bandwidth sharing
techniques that quickly react to the traffic fluctuations are of interest,
especially in large scale settings with hundreds of nodes and thousands of
flows. In this context, we propose a distributed algorithm based on the
Alternating Direction Method of Multipliers (ADMM) that tackles the multi-path
fair resource allocation problem in a distributed SDN control architecture. Our
ADMM-based algorithm continuously generates a sequence of resource allocation
solutions converging to the fair allocation while always remaining feasible, a
property that standard primal-dual decomposition methods often lack. Thanks to
the distribution of all computer intensive operations, we demonstrate that we
can handle large instances at scale
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