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About 2,880 papers for “static resource allocation”

Online Resource Allocation via Static Bundle Pricing

Fotakis, Dimitris, Platanos, Charalampos, Tolias, Thanos · arXiv (Cornell University) · 2025

Online Resource Allocation addresses the problem of efficiently allocating limited resources to buyers with incomplete knowledge of future requests. In our setting, buyers arrive sequentially requesting a set of items, each with a value drawn from a known distribution. We study the efficiency of static and anonymous bundle pricing in environments where the buyers' valuations exhibit strong complementarities. In such settings, standard item pricing fails to leverage item multiplicities, while static bundle pricing mechanisms are only known for very restricted domains and their analysis relies on domain-specific arguments. We develop a unified bundle pricing framework for online resource allocation in three well-studied domains with complementarities: (i) single-minded combinatorial auctions with maximum bundle size $d$; (ii) general single-minded combinatorial auctions; and (iii) network routing, where each buyer aims to route a unit of flow from a source node $s$ to a target node $t$ in a capacitated network. Our approach yields static and anonymous bundle pricing mechanisms whose performance improves exponentially with item multiplicity. For the $d$-single-minded setting with minimum item multiplicity $B$, we obtain an $O(d^{1/B})$-competitive mechanism. For general single-minded combinatorial auctions and online network routing, we obtain $O(m^{1/(B+1)})$-competitive mechanisms, where $m$ is the number of items. We complement these results with information-theoretic lower bounds. We show that no online algorithm can achieve a competitive ratio better than $ \widetildeΩ(m^{1/(B+2)})$ for single-minded combinatorial auctions and $ \widetildeΩ(d^{1/(B+1)})$ for the $d$-single-minded setting. Our constructions exploit a deep connection to the extremal combinatorics problem of determining the maximum number of qualitatively independent partitions of a ground set.

Cited by 0Open access

Differentiated Static Resource Allocation in WDM Networks

J. Y. Zhang, Jing Wu, Gregor von Bochmann et al. · 2009

We present a study on the static resource allocation in lightpath routed WDM networks, where each request is associated with a service grade. The goal is to maintain certain acceptance ratios for the requests of all grades, as well as to minimize the resource consumption. We propose a model of static grade-of-service (GoS) differentiation as minimizing the total rejection and cost penalty. Then, we use the Lagrangian relaxation and subgradient methods to solve the problem. The results of using static GoS differentiation are presented.

Cited by 2

Multiclass P2P Networks: Static Resource Allocation for Service Differentiation

Florence Clévenot-perronnin, Keith W. Ross · 2005

We propose a multiclass fluid model for BitTorrent-like content distribution systems. The new model can model heterogeneous peers, in which peers have different access bandwidths. The model can also model BitTorrent-like systems which provide differential service (for example, first class and second class service) to the participating peers. The fluid model leads to a non-linear system of differential equations with special structure. For the service differentiation problem, we prove that the system of differential equations admits a unique stable equilibrium, that we compute in closed-form. We also give the average download times for both classes. For the bandwidth diversity problem, we show that the system of differential equations has a stable state that may depend on the initial conditions. We give the average download time of both classes for each reachable steady-state.

Cited by 0

Using Static Flow Patterns in Time-Staged Resource Allocation Problems

Arun Marar, Warren B. Powell · 2006

We address the problem of combining a cost-based simulation model, which makes decisions over time by minimizing a cost model, and rule-based policies, where a knowledgeable user would like certain types of decisions to happen with a specified frequency when averaged over the entire simulation. These rules are designed to capture issues that are difficult to quantify as costs, but which produce more realistic behaviors in the judgment of a knowledgeable user. We consider patterns that are specified as averages over time, which have to be enforced in a model that makes decisions while stepping through time (for example, while optimizing the assignment of resources to tasks). We show how an existing simulation, as long as it uses a cost-based optimization model while stepping through time, can be modified to more closely match exogenously specified patterns.

Cited by 7

Comparison of Static and Dynamic Resource Allocation Strategies for Matrix Multiplication

Olivier Beaumont, Lionel Eyraud‐Dubois, Abdou Guermouche et al. · HAL (Le Centre pour la Communication Scientifique Directe) · 2015

The tremendous increase in the size and heterogeneity of supercomputers makes it very difficult to predict the performance of a scheduling algorithm. In this context, relying on purely static scheduling and resource allocation strategies, that make scheduling and allocation decisions based on the dependency graph and the platform description, is expected to lead to large and unpredictable makespans whenever the behavior of the platform does not match the predictions. For this reason, the common practice in most runtime libraries is to rely on purely dynamic scheduling strategies, that make short-sighted scheduling decisions at runtime based on the estimations of the duration of the different tasks on the different available resources and on the state of the machine. In this paper, we consider the special case of Matrix Multiplication, for which a number of static allocation algorithms to minimize the amount of communications have been proposed. Through a set of extensive simulations, we analyze the behavior of static, dynamic, and hybrid strategies, and we assess the possible benefits of introducing more static knowledge and allocation decisions in runtime libraries.

Cited by 2Open access

2D Static Resource Allocation for Compressed Linear Algebra and Communication Constraints

Olivier Beaumont, Lionel Eyraud‐Dubois, Mathieu Vérité · 2020

This paper adresses static resource allocation problems for irregular distributed parallel applications. More precisely, we focus on two classical tiled linear algebra kernels: the Matrix Multiplication and the LU decomposition algorithms on large dense linear systems. In the context of parallel distributed platforms, data exchanges can dramatically degrade the performance of linear algebra kernels and in this context, compression techniques such as Block Low Rank (BLR) are good candidates both for limiting data storage on each node and data exchanges between nodes. On the other hand, the use of BLR representation makes the static allocation problem of tiles to nodes more complex. Indeed, the load associated to each tile depends on its compression factor, which induces an heterogeneous load balancing problem. In turn, solving this load balancing problem optimally might lead to complex allocation schemes, where the tiles allocated to a given node are scattered on all the matrix. This causes communication complexity problems, since matrix multiplication and LU decompositions rely heavily on broadcasting operations along rows and columns of processors, so that the communication volume is minimized when the number of different nodes on each row and column is minimized. In the fully homogeneous case, 2D block cyclic allocation solves both load balancing and communication minimization issues simultaneously, but it might lead to bad load balancing in the heterogeneous case. Our goal in this paper is to propose data allocation schemes dedicated to BLR format and to prove that it is possible to obtain good overall performance when simultaneously balancing the load and minimizing the maximal number of different resources in any row or column.

Cited by 0

Quasi-static resource allocation with interference avoidance for fixed wireless systems

K. Chawla, Xiaoxin Qiu · IEEE Journal on Selected Areas in Communications · 1999

We propose a novel intercell interference management technique, called quasi-static resource allocation with interference avoidance (QRA-IA), for fixed broadband wireless systems with narrow sectors or equipped with fixed or adaptive array antennas. The basic idea of QRA-IA is for every base to periodically turn off each of its beams (sectors) for a certain amount of time. This periodic turn off introduces a predictable nonuniformity in a terminal's performance and therefore permits each terminal to identify a preferred time period for transmission. QRA-IA requires that each terminal sense the interference over time, select the preferred transmission periods, and report these to the base. The resource allocation algorithm at the base can then use this information for scheduling transmissions to the terminals. The base can also use this information for obtaining an appropriate beam-off sequence. A graph theory model is used to show that an acceptable beam-off sequence exists for each base. A simple, distributed, and measurement-based beam-switching algorithm is designed for the system to find these sequences autonomously. We demonstrate via simulations that a system with QRA-IA provides a marked improvement in the packet error rate performance of terminals over the same system without QRA-IA. This improvement translates directly to improved coverage and throughput and also reduces the burden on higher layer protocols to ensure fairness and quality of service.

Cited by 32

STATIC AND DYNAMIC RESOURCE ALLOCATION STRATEGIES IN HIGH PERFORMANCE HETEROGENEOUS COMPUTING APPLICATION

Ms. Ragini Karwayun · International Journal of Advanced Research in Computer Science · 2018

More and more computing services are running in clouds as the number and scope of internet services are increasing exponentially. Major objective of cloud computing is to give users virtually unlimited pay per use computing resources without any concern for managing the underlying infrastructure. But this results in huge increase in size of computing environment which makes it very difficult to measure the performance of allocation strategies that use the description of underlying infrastructure and resource dependency graphs for making decisions. Both dynamic and static allocation strategies have their share of advantages and drawbacks. In this paper we will try to define a hybrid scheme for resource allocation that will use the positive features of both schemes to give better performance.

Cited by 1Open access

Dynamic Edge/Cloud Resource Allocation for Distributed Computation Under Semi-Static Demands

Ippokratis Sartzetakis, Panagiotis Pantazopoulos, Konstantinos V. Katsaros et al. · Zenodo (CERN European Organization for Nuclear Research) · 2024

Edge computing is a recent paradigm where the processing takes place close to the data sources. It therefore reduces latency and saves bandwidth compared to traditional cloud computing. The latter can continue to play a supportive role. Edge-cloud computing provides benefits in many use cases including distributed computation algorithms, where the processing is divided into a number of tasks that are executed in parallel on different equipment. An important relevant challenge is to allocate the appropriate resources to process the data that are continuously generated from user devices. The issue becomes more complicated when we take into account the variations in the volume of the generated data as a function of time. In this paper we present a resource allocation algorithm for distributed computation with emphasis on machine learning algorithms. We consider that the resource requirements vary with time in a semi-static way that exhibits some daily pattern. We distinguish between periodic (expected) variations that occur during the day, and sporadic variations due to unexpected events. We propose an Integer Linear Programming algorithm to allocate the periodic resource requirements. To handle the non-periodic requirements, we consider a suitable prediction algorithm coupled with a reconfiguration algorithm that allocates the predicted required resources. Our results indicate that our proposal outperforms traditional allocation algorithms in terms of resource utilization, monetary cost and achieved accuracy.

Cited by 1Open access

Transformer-pointer DRL model for static resource allocation problems in SDM-EONs

Sibo Chen, Jiading Wang, Maiko Shigeno · Journal of Optical Communications and Networking · 2026

The static resource allocation problem in space-division multiplexing elastic optical networks (SDM-EONs) requires joint optimization of routing, modulation, space, and spectrum assignment (RMSSA) for efficient resource use. Because integer-programming models and sophisticated heuristics are computationally expensive, the First-Fit algorithm is often used for fast feasible solutions, yet its quality is usually poor and highly order-dependent. To address this, we propose a deep reinforcement learning method for static resource allocation. We reformulate the combinatorial problem as sequence optimization by pairing with a fixed First-Fit allocator and prove that, for the space-spectrum assignment (SSA) subproblem, First-Fit is order-expressive and can achieve an optimal solution under a suitable ordering. A Transformer encoder extracts features of the request set, and a pointer-network decoder optimizes the output order. The trained network can be used as a black-box heuristic. Compared with hand-crafted orderings, it delivers higher solution quality with rapid inference speed, making it highly suitable for time-sensitive reconfiguration and large-scale planning tasks, while maintaining a runtime comparable to First-Fit. Finally, ablation studies verify the contributions of the encoder and decoder, and we attempt to interpret the trained network.

Cited by 1

Static and dynamic resource allocation in a multiservice satellite network with fading

CELANDRONI N., FERRO E., DAVOLI, FRANCO · ISTI Open Portal · 2003

A control architecture for resource allocation in satellite networks is proposed, along with
\nthe specification of performance indexes and control strategies. The latter, besides being based on
\ninformation on traffic statistics and network status, rely upon some knowledge of the fading conditions
\nover the satellite network channels. The resource allocation problem consists of the assignment, by a
\nmaster station, of a total available bandwidth among traffic earth stations in the presence of different
\ntraffic types. Traffic stations are assumed to measure continuously their signal fade level, but this
\ninformation may either be used only locally or also communicated to the master station. According to
\nthe information made available on-line to the master station on the level of the fading attenuation of
\nthe traffic stations, the assignment can be made static, based on the a priori knowledge of long-term
\nfading statistics, or dynamic, based on the updated measurements. In any case, the decisions can be
\nadapted to slowly time-varying traffic characteristics. At each earth station, two basic traffic types are
\nassumed to be present, namely guaranteed bandwidth, real-time, synchronous data (stream traffic), and
\nbest effort traffic (datagram traffic). Numerical results are provided for a specific architecture in the
\ndynamic case, in a real environment, based on the Italsat satellite national coverage payload
\ncharacteristics.

Cited by 0