Risk-Based Resource Allocation for Collaborative System Design in Distributed Environment

Author(s):  
Yuming Qiu ◽  
Ping Ge ◽  
Solomon C. Yim

Risk is becoming an important factor in facilitating the resource allocation in engineering design because of its essential role in evaluating functional reliability and mitigating system failures. In this work, we aim at expanding existing quantitative risk modeling methods to collaborative system designs regarding resource allocation in a distributed environment, where an overlapped risk item can affect multiple stakeholders, and correspondingly be examined by multiple evaluators simultaneously. Because of different perspectives and limited local information, various evaluators (responsible for same or different components of a system), though adopting the same risk definition and mathematical calculation, can still yield unsatisfying global results, such as inconsistent probability and/or confusing consequence evaluations, which can then cause potential barriers in achieving agreement or acceptable discrepancies among different evaluators involved in the collaborative system design. Built upon our existing work, a Risk-based Distributed Resource Allocation Methodology (R-DRAM) is developed to help system manager allocate limited resource to stakeholders, and further to components of the targeted system for the maximum global risk reduction. Besides probability and consequence, two additional risk properties, tolerance and hierarchy, are considered for comprehensive systematic risk design. Tolerance is introduced to indicate the effective risk reduction, and hierarchy is utilized to model the comprehensive risk hierarchy. Finally a theoretical framework based on cost-benefit measure is developed for resource allocation. A case study is demonstrated to show the implementation process. The preliminary investigation shows promise of the R-DRAM in facilitating risk-based resource allocation for collaborative system design using a systematic and quantifiable approach in distributed environment.

2008 ◽  
Vol 130 (6) ◽  
Author(s):  
Yuming Qiu ◽  
Ping Ge ◽  
Solomon C. Yim

Risk analysis is important in system design because of its essential role in evaluating functional reliability and mitigating system failures. In this work, we aim at expanding existing risk modeling methods to collaborative system designs: specifically, to facilitate resource allocation among distributed stakeholders. Because of different perspectives and limited local information, inconsistent and/or incoherent risk assessments (such as different probability and confusing consequence evaluations) may occur among stakeholders, who are responsible for same or different risk components of a system. The discrepancies can become potential barriers in achieving consensus or acceptable disagreement for distributed resource allocation. Built upon our previous work, a risk-based distributed resource allocation methodology (R-DRAM) is developed to help a system manager allocate limited resources among collaborating stakeholders based on a cost-benefit measure of risk. Besides probability and consequence, two additional risk aspects, tolerance and hierarchy, are considered for system risk modeling in a collaborative/distributed environment. Given a total amount of resources to be allocated, the four risk aspects are combined to form the cost-benefit measure in a multiobjective optimization framework for achieving a desired risk reduction of a targeted system. An example is used to demonstrate the implementation process of the methodology. The preliminary investigation shows promise of the R-DRAM as a systematic and quantifiable approach in facilitating distributed resource allocation for collaborative system design.


2010 ◽  
Vol 11 (1-2) ◽  
pp. 58-83 ◽  
Author(s):  
Paul M. Salmon ◽  
Neville A. Stanton ◽  
Guy H. Walker ◽  
Daniel P. Jenkins ◽  
Laura Rafferty

2021 ◽  
Author(s):  
◽  
Kyle Chard

<p>The computational landscape is littered with islands of disjoint resource providers including commercial Clouds, private Clouds, national Grids, institutional Grids, clusters, and data centers. These providers are independent and isolated due to a lack of communication and coordination, they are also often proprietary without standardised interfaces, protocols, or execution environments. The lack of standardisation and global transparency has the effect of binding consumers to individual providers. With the increasing ubiquity of computation providers there is an opportunity to create federated architectures that span both Grid and Cloud computing providers effectively creating a global computing infrastructure. In order to realise this vision, secure and scalable mechanisms to coordinate resource access are required. This thesis proposes a generic meta-scheduling architecture to facilitate federated resource allocation in which users can provision resources from a range of heterogeneous (service) providers. Efficient resource allocation is difficult in large scale distributed environments due to the inherent lack of centralised control. In a Grid model, local resource managers govern access to a pool of resources within a single administrative domain but have only a local view of the Grid and are unable to collaborate when allocating jobs. Meta-schedulers act at a higher level able to submit jobs to multiple resource managers, however they are most often deployed on a per-client basis and are therefore concerned with only their allocations, essentially competing against one another. In a federated environment the widespread adoption of utility computing models seen in commercial Cloud providers has re-motivated the need for economically aware meta-schedulers. Economies provide a way to represent the different goals and strategies that exist in a competitive distributed environment. The use of economic allocation principles effectively creates an open service market that provides efficient allocation and incentives for participation. The major contributions of this thesis are the architecture and prototype implementation of the DRIVE meta-scheduler. DRIVE is a Virtual Organisation (VO) based distributed economic metascheduler in which members of the VO collaboratively allocate services or resources. Providers joining the VO contribute obligation services to the VO. These contributed services are in effect membership “dues” and are used in the running of the VOs operations – for example allocation, advertising, and general management. DRIVE is independent from a particular class of provider (Service, Grid, or Cloud) or specific economic protocol. This independence enables allocation in federated environments composed of heterogeneous providers in vastly different scenarios. Protocol independence facilitates the use of arbitrary protocols based on specific requirements and infrastructural availability. For instance, within a single organisation where internal trust exists, users can achieve maximum allocation performance by choosing a simple economic protocol. In a global utility Grid no such trust exists. The same meta-scheduler architecture can be used with a secure protocol which ensures the allocation is carried out fairly in the absence of trust. DRIVE establishes contracts between participants as the result of allocation. A contract describes individual requirements and obligations of each party. A unique two stage contract negotiation protocol is used to minimise the effect of allocation latency. In addition due to the co-op nature of the architecture and the use of secure privacy preserving protocols, DRIVE can be deployed in a distributed environment without requiring large scale dedicated resources. This thesis presents several other contributions related to meta-scheduling and open service markets. To overcome the perceived performance limitations of economic systems four high utilisation strategies have been developed and evaluated. Each strategy is shown to improve occupancy, utilisation and profit using synthetic workloads based on a production Grid trace. The gRAVI service wrapping toolkit is presented to address the difficulty web enabling existing applications. The gRAVI toolkit has been extended for this thesis such that it creates economically aware (DRIVE-enabled) services that can be transparently traded in a DRIVE market without requiring developer input. The final contribution of this thesis is the definition and architecture of a Social Cloud – a dynamic Cloud computing infrastructure composed of virtualised resources contributed by members of a Social network. The Social Cloud prototype is based on DRIVE and highlights the ease in which dynamic DRIVE markets can be created and used in different domains.</p>


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