Fuzzy Continuous Resource Allocation Mechanisms in Workflow Management Systems

In a shared virtual computing environment, dynamic load changes as well as different quality requirements of applications in their lifetime give rise to dynamic and various capacity demands, which results in lower resource utilization and application quality using the existing static resource allocation. Furthermore, the total required capacities of all the hosted applications in current enterprise data centers, for example, Google, may surpass the capacities of the platform. In this paper, we argue that the existing techniques by turning on or off servers with the help of virtual machine (VM) migration is not enough. Instead, finding an optimized dynamic resource allocation method to solve the problem of on-demand resource provision for VMs is the key to improve the efficiency of data centers. However, the existing dynamic resource allocation methods only focus on either the local optimization within a server or central global optimization, limiting the efficiency of data centers. We propose a two-tiered on-demand resource allocation mechanism consisting of the local and global resource allocation with feedback to provide on-demand capacities to the concurrent applications. We model the on-demand resource allocation using optimization theory. Based on the proposed dynamic resource allocation mechanism and model, we propose a set of on-demand resource allocation algorithms. Our algorithms preferentially ensure performance of critical applications named by the data center manager when resource competition arises according to the time-varying capacity demands and the quality of applications. Using Rainbow, a Xen-based prototype we implemented, we evaluate the VM-based shared platform as well as the two-tiered on-demand resource allocation mechanism and algorithms. The experimental results show that Rainbow without dynamic resource allocation (Rainbow-NDA) provides 26 to 324 percent improvements in the application performance, as well as 26 percent higher average CPU utilization than traditional service computing framework, in which applications use exclusive servers. The two-tiered on-demand resource allocation further improves performance by 9 to 16 percent for those critical applications, 75 percent of the maximum performance improvement, introducing up to 5 percent performance degradations to others, with 1 to 5 percent improvements in the resource utilization in comparison with Rainbow-NDA.

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.

Resource allocation is playing a vital role in grid environment because of the dynamic and heterogeneous nature of grid resources. Literature offers numerous studies and techniques to solve the grid resource allocation problem. Some of the drawbacks occur during grid resource allocation are low utilization, less economic reliability and increased waiting time of the jobs. These problems were occurred because of the inconsiderable level in the code of allocating right resources to right jobs, poor economic model and lack of provision to minimize the waiting time of jobs to get their resources. So, all these drawbacks need to be solved in any upcoming resource allocation technique. Hence in this paper, the efficiency of the resource allocation mechanism is improved by proposing two allocation models. Both the allocation models have used the Genetic Algorithm to overcome all the aforesaid drawbacks. However, one of the allocation models includes penalty function and the other does not consider the economic reliability. Both the models are implemented and experimented with different number of jobs and resources. The proposed models are compared with the conventional resource allocation models in terms of utilization, cost factor, failure rate and make span.

Optical–wireless convergence is becoming popular as one of the most efficient access network designs that provides quality of service (QoS) guaranteed, uninterrupted, and ubiquitous access to end users. The integration of passive optical networks (PONs) with next-generation wireless access networks is not only a promising integration option but also a cost-effective way of backhauling the next generation wireless access networks. The QoS performance of the PON–wireless converged network can be improved by taking the advantages of the features in both network segments for bandwidth resources management. In this paper, we propose a novel resource allocation mechanism for long term evolution–Gigabit Ethernet PON (LTE–GEPON) converged networks that improves the QoS performance of the converged network. The proposed resource allocation mechanism takes the advantage of the ability to forecast near future packet arrivals in the converged networks. Moreover, it also strategically leverages the inherited features and the frame structures of both the LTE network and GEPON, to manage the available bandwidth resources more efficiently. Using extensive simulations, we show that our proposed resource allocation mechanism improves the delay and jitter performance in the converged network while guarantying the QoS for various next generation broadband services provisioned for both wireless and wired end users. Moreover, we also analyze the dependency between different parameters and the performance of our proposed resource allocations scheme.

In this period, cloud computing is considered a very ultra-modern and eminent technology on the web. It provides on-demand computing services, such as Google, Microsoft, etc. by this world-wise category. Cloud computing is providing both provisioning and de-provisioning on-demand network services as well as assisting any organization to cut the capital costs of software and hardware. Cloud service providers require handling a huge demand for scaling up the size of the cloud. But now it faces grave challenges in its glorious future. These challenges create a problem for exploring its perfect realization. Resource allocation is one of the vital issues in that case. It is a procedure to distribute total workloads among individual systems of distributed web systems to get better the proper usage of web resources and job execution time. Resource allocation should be done properly as a breakdown in any node that can lead to the unavailability of data. Resource allocation mechanisms can be divided into different categories, such as centralized or distributed, dynamic or static, etc. There are already various research works that have been done on resource allocation using various formulating techniques to decrease the execution time and average waiting time for giving a better quality of services in cloud computing.

Cloud computing become a new technology in enterprises and markets. It allows us to access applications anywhere at any time. Companies rent the resources from cloud to reduce the cost. Further they can access the application based on pay as you go manner. The important issue in resource allocation is optimization of resources being allocated. The objective of resource allocation is to scale down the expenditure and increase revenue of cloud service provider. The main challenge in resource allocation is to meet the service level agreement of both customer and cloud service provider. In this paper various resource allocation mechanisms and the optimization techniques are discussed. In addition to that we also taught about a range of challenges involved and classification of applications based on Qos metrics.

Resource allocation and call admission control (CAC) are key functions in future 4G networks, as they can allow for efficient utilization of resources as well as provide Quality of Service (QoS) guarantees to users. In this work, a novel mechanism for resource allocation and CAC is proposed using the cost of each service and the user channel gains to partition the available bandwidth in accordance with a complete partitioning (CP)-structure. Numerical results demonstrate that the pricing model has a strong impact on the blocking probability associated with a particular traffic class.

The evolving small-cell (originally femtocell) networks are envisioned to provide improved capacity and coverage in next generation cellular systems. Small-cell deployments pose many challenges among which radio resource management (i.e., interference management, resource allocation, and power control) is the most significant one. The aim of this talk is to provide an overview of the radio resource management problem in small-cell networks considering OFDMA (e.g., LTE, WiMAX) femtocells, and the ongoing research on this topic. After a brief overview of the small-cell networks, we outline the major challenges in successful deployment of small-cells in next generation cellular wireless systems. In particular, the challenges related to resource allocation, co-tier/cross-tier interference management and power control are discussed. Then we provide a comprehensive overview of the different proposed techniques for resource allocation and interference management in small-cell networks. In this context, two different approaches are described: one based on clustering and semi-centralized computations, and one based on cooperative game theory. The requirements for strategic interference management via power control and resource allocation mechanisms are then discussed.

State of the art management products have until now focused on monitoring of resources such as networks, systems, servers and applications. Management usually does not deal with handling the complete resource life-cycle, i.e., of providing resources on-demand to application providers, matching and allocating resources to users so that application requirements are best met, providing guarantees through service level agreements, and monitoring and assuring these SLAs. Grid aims to provide seamless access to computational resources. In Grid based systems and the latest initiative of Open Grid Service Infrastructure (OGSI) [3] concepts related to discovery of network topology, allocation based on farms, monitoring towards certain goal (as specified in SLAs), and control to assure these goals are missing. However, there are parallels between grid and traditional management systems. A grid just like traditional management systems is comprised of grid nodes each of which manages a group of resources. The management+ [1] system that we propose, will be able to manage the complete life-cycle of resources assigned to applications, provided, Grid technologies are used for reserving, allocating, and handing over resources to applications and traditional management functionalities are incorporated that deal with monitoring and control.A management+ system will be comprised of grid nodes that communicate with each other to coordinate the tasks of resource allocation and management. The management+ system may be used for managing a resource pool inside the enterprise or spanning multiple enterprises. In order to do so, the management+ system needs capabilities to discover the resources so as to create a resource pool, maintain & model the resource information, be able to provide resources to application providers on receiving requests, be able to provide guarantees as agreed upon through Service-Level Agreements, and by monitoring and assuring them. This poses the following requirements for the management+ system: Resource Discovery: Under the realm of each grid node are collections of resources that are allocatable and manageable by that grid node, that need to be registered and discovered. The registration information may be maintained in a central repository that may be LDAP based (as in traditional Grid) or in a UDDI based registry. The other approach is to let each of the grid nodes manage their own repository, but to have protocols similar to p-2-p systems for searching resources amongst grid nodes. Resource Modeling: Resources that are managed by the grid or those even within a grid node are heterogeneous in nature. They are of different types, potentially distributed in different geographic locations and administrative domains (e.g., consolidated in racks vs. located on desktops). Modeling is therefore an important task for the management+ system. A relevant resource model is CIM. Requesting Resources and Guarantees: Application providers need to specify their requests to the management+ system in some form. The specification of the request may be done in terms of application level metrics (e.g. throughput, transactions/sec) or at a low-level, an enhanced RSL may be used. The guarantees may be provided in terms of reliability, availability, security, timeliness through SLA[5]. Resource Allocation and Deployment: Resource requests are sent to the management+ system that then undertakes resource reservation, allocation and deployment. Resource reservation is done when the resources are not immediately required. The management+ system has to keep a note of all the competing reservations and of ensuring that the resource pool is being properly utilized. As the reservations become current, requests for resources are satisfied through match-making [3]. Monitoring Guarantees: Traditionally, applications are installed and operated on a fixed set of associated, physical hardware. In management+ system, resources virtualize the physical hardware. This virtualization provides the capability of transparently switching the physical hardware (in case of degradations/failures) while maintaining the transparency of a continually running application. The metrics specified on the resources in SLAs have to be monitored and aggregated into higher-level metrics that business manager(s) may relate to. Assuring Guarantees: Once the SLA violations are detected, it is important for the management+ system to take corrective actions. Analysis tools are employed to analyze the violation data so as to decide on corrective action(s). The corrective action may range from taking one of the control actions like fail-over, reboot/rejuvenation of the resources to transparently switching the failed resources with new set of resources out of the resource pool. These new resources are obtained through the same resource allocation and deployment mechanisms as discussed earlier. This may enable closed-loop management.KeywordsCorrective ActionGrid NodeService Level AgreementResource PoolResource ReservationThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Cloud computing is a new paradigm that promises to move IT a step further towards utility computing, in which computing services are delivered as a utility service. Traditionally, Cloud employs dedicated resources located in one or more data centres in order to provide services to clients. Desktop Cloud computing is a new type of Cloud computing that aims at providing Cloud capabilities at low or no cost. Desktop Clouds harness non dedicated and idle resources in order to provide Cloud services. However, the nature of such resources can be problematic because they are prone to failure at any time without prior notice. This research focuses on the resource allocation mechanism in Desktop Clouds. The contributions of this chapter are threefold. Firstly, it defines and explains Desktop Clouds by comparing them with both Traditional Clouds and Desktop Grids. Secondly, the paper discusses various research issues in Desktop Clouds. Thirdly, it proposes a resource allocation model that is able to handle node failures.

General wireless network services such as multimedia streaming services require effective models to cost efficiently or fairly distribute limited resources to users in different network environments. The network manager should make decisions related to effective utilization and fair allocation of limited resources, and appropriate guarantee of service quality. In this paper, we introduce a comparative method to help the network manager using resource allocation and pricing mechanisms for wireless multimedia services by considering user qualities of service and service provider profits. To that end, two well-known models of game theory: auction and bargaining are adapted. In particular, progressive second price auction and Kalai- Smorodinsky bargaining solution are analyzed with regard to user cost and utility and service provider profit. While the network manager utilize the result of comparisons, it may be possible to make rational decision of selecting resource allocation and pricing mechanism by considering the status of network.

With the rapid increase of smart grid's intelligence degree, types and number of communication services are also increasing. It will be a challenge to design a resource allocation method meeting various QoS requirements and effectively using substrate resources. In this paper, a cost-and-QoS-based resource allocation mechanism is proposed to solve wireless resource slicing problem in access network. It virtualizes substrate wireless resources and builds a combined and non-convex optimization model, then uses simulated annealing optimization algorithm (SAOA) to complete allocation of virtual wireless resources. Simulation results show that compared with QoS-based wireless resource allocation algorithm (QoS-WRA) and wireless resource dynamic allocation algorithm (WRDA), SAOA has stronger advantages in saving energy consumption and ensuring transmission rate.

In a healthcare system, resource allocation mechanisms should promote efficiency, improve quality of care and reduce practice variation. This paper presents a framework that has been used at the US Department of Veterans Affairs, VA Healthcare Network Upstate New York, to guide resource allocation and productivity assessment since 1999. It synthesises the latest developments in the field of healthcare finance and stochastic frontier analysis and generalises the traditional concept of productivity to include overutilisation or over-consumption of resources as part of inefficiency. Objective rather than subjective information is employed to predict resource need at the hospital level and to assess the operational efficiency of hospitals.

Wireless networks are playing an increasingly important role for global communications. Many resource allocation mechanisms have been proposed to efficiently utilize the limited radio resources in wireless networks to support a large number of mobile users with a diversity of applications. Among them, pricing frameworks that provide incentives to users to maximize their individual utility while optimizing allocation of network resources have attracted a lot of attention recently. Nevertheless, most of these pricing schemes require dynamic charging rates and may be too complex for wide acceptance by users, as most users would prefer relatively simple charging schemes. Moreover, use of a pricing framework to facilitate resource planning and future expansion at the service provider's side has not yet been widely considered. In this paper, we propose Integrated Multiple Time Scale Control (IMTSC), a novel incentive engineering mechanism to facilitate resource allocation and network planning. Over different time scales, IMTSC combines the functions of network capacity planning, admission control for resource allocation, and tracking of users' instantaneous traffic demands. The proposed mechanism is applied for access control at a congested access point in a wireless network. By decomposing the original problem into distributed optimization problems that are solved locally by the service provider through adjusting charging rate and remotely by individual users by appropriately changing her service requests, we show that maximization of user's utility and increase of network efficiency can be simultaneously achieved. Results from extensive simulations demonstrate the effectiveness of the proposed IMTSC mechanism.

There is increasing interest in the study and improvement of health system resource allocation. The Adjusted Clinical Groups (ACG) System measures the morbidity burden of patient populations based on disease patterns, age and gender. This system can be used in primary health care settings. To report the use of the ACG system as a risk based patient classification system and its potential as a resource allocation mechanism in primary health care. Patient diagnoses from a sample of primary care establishments were processed using the Johns Hopkins ACG System. Linear regressions were used to measure the ACG System's ability to explain resource use and ambulatory visit rates. The ACG System had a statistically significant capacity to explain primary health care costs with an adjusted R² of 0.26. These results are significantly better, compared with risk assessment models based on patients' age and sex, whose adjusted R² is 0.05. The use of risk adjustment mechanisms will contribute to improve health care resource allocation as well to know the disease profile of the population.