Efficient Resource Allocation and Consolidation with Selfish Agents: An Adaptive Auction Approach

Cloud computing is one among the emerging platforms in business, IT enterprise and mobile computing applications. Resources like Software, CPU, Memory and I/O devices etc. are utilized and charged as per the usage, instead of buying it. A Proper and efficient resource allocation in this dynamic cloud environment becomes the challenging task due to drastic increment in cloud usage. Various promising technologies have been developed to improve the efficiency of resource allocation process. But still there is some incompetency in terms of task scheduling and power consumption, when the system gets overloaded. So an energy efficient task scheduling algorithm is required to improve the efficiency of resource allocation process. In this paper an improved task scheduling and an optimal power minimization approach is proposed for efficient dynamic resource allocation process. Using prediction mechanism and dynamic resource table updating algorithm, efficiency of resource allocation in terms of task completion and response time is achieved. This framework brings an efficient result in terms of power reduction since it reduces the power consumption in data centers. The proposed approach gives accurate values for updating resource table. An efficient resource allocation is achieved by an improved task scheduling technique and reduced power consumption approach. The Simulation result gives 8% better results when comparing to other existing methods.

Purpose Using the example of community access programs (CAPs), the purpose of this paper is to describe resource allocation and policy decisions related to providing health services for the uninsured in the USA and the organizational values affecting these decisions. Design/methodology/approach The study used comparative case study methodology at two geographically diverse sites. Researchers collected data from program documents, meeting observations, and interviews with program stakeholders. Findings Five resource allocation or policy decisions relevant to providing healthcare services were described at each site across three categories: designing the health plan, reacting to funding changes, and revising policies. Organizational values of access to care and stewardship most frequently affected resource allocation and policy decisions, while economic and political pressures affect the relative prioritization of values. Research limitations/implications Small sample size, the potential for social desirability or recall bias, and the exclusion of provider, member or community perspectives beyond those represented among participating board members. Practical implications Program directors or researchers can use this study to assess the extent to which resource allocation and policy decisions align with organizational values and mission statements. Social implications The description of how healthcare decisions are actually made can be matched with literature that describes how healthcare resource decisions ought to be made, in order to provide a normative grounding for future decisions. Originality/value This study addresses a gap in literature regarding how CAPs actually make resource allocation decisions that affect access to healthcare services.

Increasing the efficiency of resource allocation is the basis and guarantee for boosting high-quality economic development. Based on the panel data of Chinese industrial enterprises and cities from 2008 to 2013, this paper studies the influence of infrastructure construction demand shocks represented by local government debt expansion on the efficiency of sectoral resource allocation from the perspective of sectoral linkage. According to the empirical findings, local government debt significantly reduces the resource allocation efficiency of manufacturing sector that is highly related to infrastructure construction. This conclusion is still tenable after the robustness test using the simulated local government debt as an instrumental variable. Further mechanism tests show that there are two reasons for the decline of the efficiency of resource allocation in manufacturing sector that is highly related to infrastructure construction. First, more product demands and investments brought by the expansion of local government debt flow to less productive enterprises in the sector. Second, resource misallocation reduces the probability of high-productivity enterprises entering the market and low-productivity enterprises exiting the market, and the effect is more prominent in cities with high dependence on state-owned enterprises and high pressure on officials to be promoted. According to this study, the performance management of local government debt should be further strengthened, and particular attention should be paid to the influence of local government debt on enterprise investment and financing crowding out and resource misallocation.

Social inequalities are worsened by the decision about how resources are to be allocated. Considering that children and adolescents are likely to become future leaders and decision-makers, understanding which principles they follow regarding resource allocation in situations of inequality is crucial. This paper aims at presenting a protocol for systematic review of the relationship between perception of resource inequality and allocation decisions made by children and adolescents. Method: A systematic search will be conducted in the PsycINFO, PsycArticles, Web of Science (Core Collections), Scopus, ERIC and LILACS databases. The paper will also include empirical studies from the authors’ personal files, studies obtained through contact with the main authors of the articles included, and through backward and forward snowballing. The studies’ quality will be assessed using the Quality Assessment with Diverse Studies (QuADS). Finally, a narrative synthesis will be conducted to review and synthesize data. This protocol was outlined following the PRISMA-P guidelines. The protocol is registered at the Open Science Framework (OSF) under the code DOI: https://doi.org/10.17605/OSF.IO/D96MY. The systematic review will follow the Joanna Briggs Institute guidelines, and will be reported according to the PRISMA 2020 reporting guidelines. Discussion: The results of this review will be relevant to policy makers, decision-makers, educators, and researchers.

International evidence on the effects of directed credit programmes on efficiency of resource allocation in developing countries: The case of development bank lendings

Next generation wireless networks are expected to handle an exponential increase in demand for capacity generated by a collection of tenants and/or services with heterogeneous requirements. Multi-tenant network sharing, enabled through virtualization and network slicing, offers the opportunity to reduce operational and deployment costs, and the challenge of managing resource allocations among multiple tenants serving possibly mobile diverse customers. When designing shared radio resource allocation mechanisms, it is as important to provide tenants with customization and isolation guarantees, as it is to achieve high resource utilization and to do so via low complexity and easy to implement algorithms. This thesis is devoted to the design and analysis of resource allocation mechanisms that meet these objectives. We propose a sharing model in which tenants are assigned a share/budget of a pool of network resources. This share is then redistributed in the form of weights amongst users, which in turn drive dynamic resource allocations which are partially able to adapt to the traffic demands on, and requirements of, different slices customer populations. We propose and analyze two approaches for redistributing slices’ share among customers which we classify into their associated (i) cooperative, and (ii) competitive resource allocations. In the cooperative resource allocation setting, a pre-established policy is proposed, in which resources are eventually assigned in proportion to the slice’s share and the relative number of active users in currently has at a resource. This is shown to be socially optimal in a particular setting and simple to implement, with statistical multiplexing gains that increase with the number of tenants and the size of the resource pool. These gains stem from the ability of the scheme to adapt to dynamic loads leading to an up to 50% network capacity savings with respect to static allocations. We further improve these gains by presenting a framework that combines resource allocation and wireless user association which uses limited computational, information, and handoff overheads. However, using our cooperative scheme over a large pool of resources restricts the degree to which a slice can differentiate its customers’ performance at a per resource level. Thus, we study how this trade-off affects the network utility and propose a mechanism to determine an optimal partition the resources into a collection of self-managed pools under cooperative resource allocations. Our competitive resource allocation approach enables tenants to reap the performance benefits of sharing while retaining the ability to customize their own users’ allocations. This setting results in a network slicing game in which each tenant reacts to the user allocations of the others so as to maximize its own customers’ utility. We show that, under appropriate conditions, the game associated with such strategic behavior converges to a Nash equilibrium. At the Nash equilibrium, a tenant always achieves the same, or better, utility than it could achieve under a static partitioning of resources, hence providing the same level of inter-slice protection as static resource partitioning. The network utility of the equilibrium allocations is shown to be, under mild conditions, close to the socially optimal ones. The competitive resource allocation framework is complemented with a study on admission control policies that enable tenants to ensure minimum rate guarantees to their users. Our analysis and extensive simulation results confirm that our framework provides a comprehensive practical solution towards multi-tenant network slicing. We also discuss how our theoretical results fill a gap in the general resource allocation literature for strategic players.

Distributed and efficient resource allocation is critical for fully realizing the benefits of cooperative communications in large scale communication networks. This paper proposes two auction mechanisms, the SNR auction and the power auction, that determine relay selection and relay power allocation in a distributed fashion. A single-relay network is considered first, and the existence and uniqueness of the Nash Equilibrium (i.e., the auction's outcome) are proved. It is shown that the power auction achieves the efficient allocation by maximizing the total rate increase, and the SNR auction is flexible in trading off fairness and efficiency. For both auctions, the distributed best response bid updates globally converge to the unique Nash Equilibrium in a completely asynchronous manner. The analysis is then generalized to networks with multiple relays, and the existence of the Nash Equilibrium is shown under appropriate conditions. Simulation results verify the effectiveness and robustness of the proposed algorithms.

Towards an efficient market mediator for divisible resources

AI-based resource allocation techniques in D2D communication: Open issues and future directions

Digital consumption, driven by the widespread application of information technology, has led to more efficient resource allocation and industrial structure optimization. This promotes green transformation and has a positive impact on carbon productivity. This research examined 275 cities across China, employing a difference-in-differences approach alongside the national information consumption pilot policy to carry out a quasi-natural experiment. The study found that the information consumption pilot policy enhances carbon productivity at the 1% significance level. After controlling for other variables, the regions affected by the information consumption policy saw an increase in carbon productivity that was 0.233 higher than in the regions that were not affected. This growth reflects the positive impact of the information consumption pilot policy or measures on carbon productivity. Meanwhile, the increase in technological resources and the transformation of the industrial framework encouraged by the policy indirectly promote the development of carbon productivity. The information consumption pilot policy promotes technological innovation and increases resource density, leading to more efficient technology application and resource allocation. It also drives industrial structure optimization, particularly accelerating the development of low-carbon industries, thereby effectively enhancing carbon productivity. This study provides theoretical and empirical references for the promotion of carbon productivity through digital consumption.

We address the problem of crowd congestion at venues like theme parks, museums and world expos by providing route guidance to multiple selfish users (with budget constraints) moving through the venue simultaneously. To represent these settings, we introduce the Selfish Orienteering Problem (SeOP) that combines two well studied problems from literature, namely Orienteering Problem (OP) and Selfish Routing (SR). OP is a single agent routing problem where the goal is to minimize latency (or maximize reward) in traversing a subset of nodes while respecting budget constraints. SR is a game between selfish agents looking for minimum latency routes from source to destination along edges of a network available to all agents. Thus, SeOP is a multi-agent planning problem where agents have selfish interests and individual budget constraints. As with Selfish Routing, we employ Nash Equilibrium as the solution concept in solving SeOP. A direct mathematical program formulation to find a Nash equilibrium in SeOP cannot scale because the number of constraints is quadratic in the number of paths, which itself is an exponential quantity. To address scalability issues, we make two key contributions. First, we provide a compact non-pairwise formulation with linear number of constraints in the number of paths to enforce the equilibrium condition. Second, we introduce DIRECT, an incremental and iterative master-slave decomposition approach to compute an approximate equilibrium solution. Similar to existing flow based approaches, DIRECT is scale invariant in the number of agents. We also provide a theoretical discussion of our approximation quality and present extensive empirical results on synthetic and real-world graphs demonstrating the scalability of combining DIRECT with our non-pairwise formulation.

We study distributed load balancing in networks with selfish agents. In the simplest model considered here, there are n identical machines represented by vertices in a network and m > n selfish agents that unilaterally decide to move from one vetex to another if this improves their experienced load. We present several protocols for concurrent migration that satisfy desirable properties such as being based only on local information and computation and the absence of global coordination or cooperation of agents. Our main contribution is to show rapid convergence of the resulting migration process to states that satisfy different stability or balance criteria. In particular, the convergence time to a Nash equilibrium is only logarithmic in m and polynomial in n, where the polynomial depends on the graph structure. Using a slight modification with neutral moves, a perfectly balanced state can be reached after additional time polynomial in n. In addition, we show reduced convergence times to approximate Nash equilibria. Finally, we extend our results to networks of machines with different speeds or to agents that have different weights and show similar results for convergence to approximate and exact Nash equilibria.

Fast multi-resource allocation with patterns in large scale cloud data center

ABSTRACTIn this article, we analyzed the impact mechanism of Internet finance on the efficiency of regional financial resource allocation. A data envelopment analysis–Malmquist model is used to measure the efficiency of financial resource allocation in 30 provinces in China, and the impact of Internet finance on the efficiency of regional financial resource allocation is tested using a spatial Durbin model. The results show clear spatial agglomeration and spillover effects in the efficiency of regional financial resource allocation. In addition, developing Internet finance can promote the efficiency and technological progress of regional financial resource allocation and can enhance efficiency and technological progress in the allocation of financial resources in adjacent regions. However, it can hurt the technical efficiency of regional financial resource allocation.

We consider a multiagent resource allocation problem where individual users intend to route traffic by requesting the help of entities across a network, and a cost is incurred at each network node that depends on the amount of traffic to be routed. We propose to study contract-based network resource allocation. In our model, users and nodes in the network make contracts before nodes route traffic for the users. The problem is an interesting self-interested negotiation problem because it requires the complete assembly of a set of distinct resources, and there are multiple combinations of distinct resources that could satisfy the goal of negotiation. First, we characterize the network allocation problem and show that finding optimal allocations is NP-complete and is inapproximable. We take both Nash equilibrium and pairwise Nash equilibrium as the solution concepts to characterize the equilibrium allocations. We find that, for any resource allocation game, Nash equilibrium and pairwise Nash equilibrium always exist. In addition, socially optimal allocations are always supported by Nash equilibrium and pairwise Nash equilibrium. We introduce best-response dynamics in which each agent takes a myopic best-response strategy and interacts with each other to dynamically form contracts. We analyze the convergence of the dynamics in some special cases. We also experimentally study the convergence rate of the dynamics and how efficient the evolved allocation is as compared with the optimal allocation in a variety of environments.