|Title of Invention||
A SYSTEM AND A METHOD TO ASSESS THE TRUSTWORTHINESS OF A RESOURCE PROVIDER
|Abstract||Conventional Grid security mechanisms for authentication and authorization are too rigid and they lack the ability to determine how "trustworthy" the result obtained from a specific provider is likely to be. This inefficiency can be mitigated through proper trust management mechanisms. In our invention, a trust management system has been developed that evaluates the trust value of various resource providers across the grid environment. Different classes of parameters that affects trustworthy of a resource providers have been considered for estimating the trust. We attempt to enhance the capability of a conventional Grid Metascheduler by integrating trust management system in it. The integration of trust management system with a grid metascheduler act as a Trust resource broker that discovers suitable resources matches the job requirements and selects single resource based on their trust value. We further propose a four layered grid architecture that performs trust based scheduling of the computational resources by validating the trust of the resource providers.|
|Full Text||1. Introduction
Grid supports dynamically evolving collections of resources spanning across multiple administrative domains. Ideally, a Grid environment provides its users with seamless access to every resource that they are authorized to access, enabling transparent sharing of computational resources. This dynamic and cross organizational aspects of Grids introduce challenging trust issues towards the trustworthy of the resource provider. Further, when the Grid is intended to be used for business purposes, it is necessary to share resources with unknown parties. Such interactions may involve some degree of risk since the resource user cannot distinguish between high and low quality resource providers on the Grid. Hence, there is always a demand for some mechanisms that validates the trustworthiness of the resource providers. However, current grid security mechanisms for authentication and authorization are too rigid and they lack the ability to determine how "trustworthy" the result obtained from a specific provider is likely to be. This inefficiency can be mitigated through proper trust management mechanisms. In this section, we discuss necessary backgrounds to understand various types of grid and trust, and research works carried out in trust management system.
This invention relates in general to job scheduling for computational grid resources available for the job execution, in particular, how the metascheduler selects a suitable grid resources based on the trust value and submit jobs to resources in grid environment.
Background of the Invention
The Grid computing provides the ability to access, utilize, and control a variety of underutilized heterogeneous resources distributed across multiple administrative domains. Grid Metascheduler is incorporated to manage and negotiate with these distributed resources to identify the suitable resource for the submitted job. In order to compromise both application performance and fault tolerance, the resource selection is made based on their availability and the performance of the resources at that moment. Gridway is a Globus-based submission framework that performs all the job scheduling steps, provides
fault recovery mechanisms, and adapts job scheduling and execution to the changing Grid conditions. The conventional scheduling algorithms do not consider the previous service history (of every grid resource) while selecting a suitable resource. For example, if the resource consumer wants to submit the job only to the resource providers that they trust and also the resource provider offer his resource based on the consumer's past behavior with that provider. Unfortunately, Grid scheduler cannot handle this situation at present. We provide a solution to handle the above scenario by considering a trust relationship between the resource provider and the resource consumer while allocating resources based on their previous experience. We develop a trust model that allows entities (viz., consumer, resource provider and broker) to decide which other entities are trustworthy and also allows entities to tune their understanding of another entity's recommendations. The trust model developed is combined with the scheduling algorithm (trust based scheduling algorithm) to allocate the resources to consumers by considering the trust relationship between the entities. The trust based scheduling algorithm services a resource request strictly according to the trust in such a way that to counter the load unbalancing which is a serious issue while servicing the request based on the trust alone.
Based on the design objectives and target applications for a Grid, Grid systems are grouped into 3 categories in our context
• Computational grid
• Data grid
• Service grid
The computational Grid provides systems that have higher computational capacity available for single applications than the capacity of any machine in the system. Here, the resource (machine) is provided as a service where the algorithm (developed by the user) is submitted as a job by the user into the resource provided. Depending on utilization, these systems can be further subdivided into distributed supercomputing, high throughput, on-demand and collaborative categories. To reduce the completion time of a job, the distributed supercomputing grid executes the application in parallel on multiple machines such as weather modeling and nuclear simulation. In a high throughput grid, the completion rate of a stream of jobs gets maximized and is well suited for applications such as Monte Carlo simulations. A collaborative Grid connects users and applications into collaborative
workgroups. These systems enable real time interaction between humans and applications. An on-demand Grid category dynamically aggregates different resources to provide new services. For example, in a data visualization workbench, the fidelity of a simulation gets dynamically increased by allocating more machines to a simulation.
The data grid is for systems that provide an infrastructure for information from data repositories such as digital libraries or data warehouses that are distributed. Computational grids also need to provide data services but the difference is the specialized infrastructure provided to applications for storage management and data access in a data grid in contrast to computational grid where the applications implement their own storage management schemes. The data Grid initiatives, European Data Grid Project and Globus are working on developing large-scale data organization, catalog, management, and access technologies.
The service is for systems that provide software as services. Here, the algorithm is provided as grid service by the service provider and the inputs provided by the user who needs this service.
In Grids, a resource is a reusable entity that is employed to fulfill a job or resource request. It could be a machine, storage, or some software service. The resource provider is defined as an entity that controls the resource. Similarly, a resource consumer is defined as an entity that controls the consumer. A resource management system (RMS) is defined as a service that manages a pool of registered resource providers. Issues such as extensibility, adaptability, site autonomy, QoS, and co-allocation, resource management in Grid systems are more challenging than in traditional distributed computing environments (DCEs). In addition to the issues such as scalability, responsiveness, fault-tolerance and stability that are encountered by the RMSs of traditional DCEs, the Grid RMSs have to deal with the issues that arise due to distributed management and ownership of the resources. In addition, Grid RMS has to monitor the availability of registered grid resources to do the match making (using grid monitoring tools such as ganglia, network weather services) between job requirements and available resources. In Grids, due to resource heterogeneity and security concerns it may be best to execute a job only on a subset of resources. Traditional DCEs span a single administrative domain and are likely to handle jobs that originate from clients that belong to that organization. However, in a Grid with jobs from different owners, providing QoS is more challenging issue. It is essential for the RMS to consider the jobs'
access privileges, type of subscription, and resource requirements while determining the level of QoS. Depending on the type of QoS assurances given, a contract may be formed between the RMS and job. However, accurately predicting the resource requirements of network applications remains a hard problem. When a job overruns its resource usage predictions, the RMS should ensure that it does not affect the resource allocations of other jobs, i.e. the Grid RMSs should provide 'isolation' among the jobs for fair resource management. In general, a Grid application can have several components with the different components mapped onto different resources.
The scheduling is two fold: Metascheduler and Local scheduler. Here, the Metascheduler does the application level scheduling and local scheduler does the resource level scheduling. User applications use the services of grid toolkits to implement their functionality. This model contains three interfaces: (1) resource consumer interface (RCI), (2) resource provider interface (RPI), (3) resource manager interface (RMI). The resource dissemination and discovery protocols (here Monitoring and Discovery Services of Globus toolkit) provide a way for the RMSs to determine the state of the resources that are managed by it and other RMSs that interoperate with it. The resource dissemination protocol provides information about the resources and the resource discovery protocol provides a mechanism by which resource information can be found on demand. A Grid could maintain a central network directory/database (Lightweight Directory Access Protocol/Oracle lOg) where dissemination consists of advertising the resource status and discovery consists of querying the central directory. Instead of resource dissemination and discovery protocols, some RMSs use resource trading protocols which encapsulate the resource capabilities and statuses using price. The resource trading (i.e selection) takes place based on price differentials. The price itself may be fixed by auctioning mechanisms or price functions. Other peer protocols include resource resolution and co-allocation protocols. Once an RMS becomes aware of a 'remote' resource that is more suitable to service a job than any of the local resources, it should contact the remote RMS to schedule the job at the remote resource. The resource resolution protocol (Grid Resource Allocation and Monitoring) among the RMS supports this process. The operation of this protocol depends on the organization of the RMS, i.e. whether the RMS are hierarchically or flatly organized. The resource co-allocation protocol is used to simultaneously access multiple
resources. Gridway will automatically converts the resource requests into resource specification language (RSL) that will be understand by underlying middleware.
In a distributed environment like grid, resources are generally spanned across geographical locations and crossing organizational boundaries with varied administrative policies. Hence, discovering and accessing these resources are difficult. Resource brokers facilitate the user in the selection of an appropriate resource provider that matches the users' requirements for job execution. If more than one resource is matching with the user's requirements, the broker has to adopt some methodology to select a suitable resource. Therefore, various QoS parameters such as clock speed, distance, network characteristics, are considered for selection of suitable resource. In this system, we devise a Trust resource broker that selects the appropriate resource based on the trust value of resource providers. The Trust Management System implemented in this research work computes and maintains the trust value of various grid resources which is then used to discover suitable resource. This approach not only addresses the problem of grid resource selection but also maintains the trust relationship between the user and the resource provider over the period of job execution.
In this system, we develop a trust management system that evaluates trust value of various resource providers across the grid environment. Though the literatures discuss many parameters that need to be considered for trust calculation, there are some more parameters that affect the trust value of a resource provider to a considerable extent. We attempt to identify those parameters and we formulate a methodology to compute their values.
We consider different classes of parameters that affects trustworthy of a resource providers. The user feedback, resource providers' infrastructure and parameters related to job execution are considered while computing trustworthiness of a resource provider. We use a Grid Metascheduler for scheduling of jobs and monitoring job execution. With this approach, we attempt to enhance the capability of a conventional Grid Metascheduler by integrating trust management system in it thereby enabling the scheduler to select the resource for job execution based on its trust value.
Summary of the invention
A system and method for convergence solution to popular Grid technologies and concepts includes Development of Trust Management System (TMS) for Grid Resource Providers. The newly built Trust Management System is integrated with the existing Metascheduler namely Gridway Metascheduler.
Advantages of Invention
The trust management system computes the trust and stores in the database. This value is used for selecting most trustworthy resources by the metascheduler. Currently, no scheduler is able to select one of the matching resources based on its trust value. That is, if the capabilities requested by the job are matched by more than one computational resource, then the metascheduler can further capable of doing co-scheduling such as matching the QoS etc to select one of the resources for job scheduling. However, they are not able to select the resource based on their trust value. This is because, the metaschedulers are not equipped with a trust management system that can compute the trust value and provides trust information to the metascheduler for scheduling. To address this lack of ability, in this system, we integrated our trust management system with Gridway so that Gridway consults the trust management system for identifying the trust value of matching resources thereby enabling it to select most trustworthy resource.
Description about the drawing
In Figure.l Lifecycle of Trust Management System
• Trust Metric Identification is the first stage of a trust management system in which the
required trust metrics from which the given trust of an entity can be defined is identified.
• Trust Metric Evaluation In the next stage, a suitable methodology is applied to determine the value of those metrics.
• Trust Metric Calculation Once the values for all the metrics are computed, the overall trust value is determined using the values. It requires formalization of trust model
expressed in terms of the metrics identified. The calculated trust is then stored in the database for further use.
• Trust Value updation Since, to reflect the dynamic nature of grid environment where trust value will change rapidly as the resources and users come and go, it is mandatory to monitor and compute the metrics periodically and calculate the trust value. This value is updated in the database to ensure that the trust management system always uses the current trust value of the entity.
• Trust Integration The calculated trust value is then used for making decisions towards job scheduling, service access and for other purpose depending on the type of the trust established.
Figure. 2. Block diagram of Trust Management system evaluates the trust value of all grid resource providers and facilitates the selection of suitable resource for job execution based on their trust value. It computes trust value of a resource provider based on the following three factors:-
• Infrastructure of the organization that provides a grid resource to the grid
• Feedback from the user after accessing the resource and
• Performance metrics of the particular grid resource. Resource Performance Module
This module obtains the performance metrics of every resource provider that are used in evaluating their trust. The performance metrics considered are as follows:-Affordability
Consider the scenario where the resource provider has agreed to contribute certain number of nodes to the grid. However, at a period of time, it is discovered that the provider has given number of nodes less than the agreed number of nodes. To measure such behavior of the resource provider we introduce a parameter called affordability which can be defined as the ratio between the number of nodes currently committed to the grid and the agreed number of nodes to be contributed to the grid during the time of registration. This parameter will be helpful to measure the reliability and commitment of the resource provider towards the proper functioning of the grid. Hence, the value of this parameter directly affect the trust over the resource provider Greater the value reveals more trust over the resource provider.
Trust a Affordability
The parameter success rate is introduced in this system to measure the uninterrupted performance of the selected computational resource. The metascheduler matches the job requirements against the available resources in the grid and submit the jobs to a single selected resource. Once, all the required files are staged to the resource, it is the responsibility of the resource to successfully execute the job. The resource provider has to ensure proper functioning of the computational resources and if any interruptions, such as sudden power supply failure, failure of local job manager, ceases the job execution leading to failure of job execution which in turn results in the reduced trust over the computational resource. The Success rate parameter is used to record the number of successful execution of job by a computational resource against the total number of jobs submitted to the resource. Similar to the affordability parameter, the success rate parameter also directly affects the trust of the computational resource.
Trust a Success Rate Bandwidth
In typical grid environment, all the resources will be connected together based on some topologies and assume a centralized resources to take up the role of grid coordinator that the user make use to submit the job to the grid. In this machine, the metascheduler will be installed and does brokering/scheduling of jobs to suitable resources. All the computing resources will be connected to this centralized machine through various communication channels with various bandwidths. Similar to the affordability parameter, the network bandwidth parameter directly affect the trust of a computational trust. The speed of connectivity leads to faster staging of files from the user to the computational resource and enables faster transfer between the grid nodes. To distinguish between the resources that has high speed connectivity, we model this parameter to directly affect the trust value thereby ensuring that the trust based scheduling mechanism tries to select the resource that has high speed connectivity over the other resources. Also, while submitting the job to the resource, if the measured bandwidth between the metascheduler and the resource is found
to be less than that of the committed bandwidth during the time of registration, the trust module lowers the trust of the computational resource.
Trust a Network Bandwidth
B. Resource Registration Module
This module obtains infrastructure information of the resource provider during registration of the resource in to the grid. We express this information in terms of the following parameters:-
• Governing body of the organization. This parameter allows us to classify the organization
into public or private assuming public organization has a greater trust over the private organization.
• Registration number of the private organization. These parameters are collectively
called as resource registration parameters and they reflect the reputation of the resource provider in the user community.
C. User Feedback Module
This module obtains the user's feed back about a particular resource provider by prompting him to mention the level of satisfaction and willingness to recommend the resource to others.
Trust a feedback Trust Metrics Computation
This module applies various methodologies to compute the value of trust metrics received from underlying resources. The values are sent to the trust computation module to calculate overall trust value. The resource performance parameters namely such as success and affordability is obtained from Gridway metascheduler. The resource registration parameter can be obtained directly from the resource provider and the feedback is obtained from the user after every job execution. Ganglia network monitoring tool is used for cluster resources monitoring across the grid and Network Weather Service is used to monitor the bandwidth provided by the resource provider.
This module gathers the input from all the above modules and computes the overall trust of a resource provider and stores it in the database. The trust represents the trustworthy of the resource provider at a given instant of time. Trust Updation
This module periodically monitors the resource performance metrics and computes the overall trust value and updates in the database. Similarly, at the end of every resource access, the user feedback and resource performance metrics are obtained, and overall trust value is computed and updated in the database. The trust value obtained from the trust management system can be used for making decisions in the grid environment. In this system, the trust value of the resource provider is used to identify suitable and most trusted resource for job execution. Hence, the trust management system is proposed to be integrated with a grid metascheduler and thereby developing a grid resource broker that can discover a suitable trustworthy grid resource for job execution. The proposed four layered trusted grid architecture is shown in the figure 3. Figure. 3. Layered Architecture of Trusted Grid. Fabric Layer
The Fabric layer deals with the resources available in grid environment and defines the interface to local resources, which may be shared. This includes computational resources, data storage, networks, catalogs, software modules, and other system resources. Grid Middleware Layer
This layer refers to the grid middleware that incorporates necessary components for authentication, monitoring and discovery of grid resources, execution of job in grid resources, file transfer between grid resources. Trust Layer
The trust layer is responsible for evaluating the trust value of all the grid resource providers. This layer periodically monitors the trust metrics and obtains the values of those metrics from MDS through various tools such as Ganglia, NWS, Metascheduler etc., It computes the overall trust value using the metrics and stores them in the database. This trust value is used to identify the most trusted resources for job execution. Suitable grid
resources that match the job requirements are discovered and they are ranked on the basis of their trust value. The resource that has most trusted value is selected for job execution. Application layer
The application layer enables the use of resources in a grid environment through several portlets. It includes portlets for providing user feedback and resource registration information. This information is useful for evaluating trust value of the grid resource provider. In addition to that, this layer may include portlets that display availability of resources, results of job execution and necessary user interface components for job submission and resource request.
The known issues in the Trust management system are the evaluation of the trust parameters. The two categories of parameters namely Resource registration parameter and the User feedback parameter can be obtained directly from the Resource provider and the user respectively. However to obtain the Resource performance parameters a separate methodology must be followed. The parameters namely the Actual Execution time, Success and the failure of the job are calculated using the information obtained from a grid metascheduler. The other parameters such as the Availability, Latency and Bandwidth can be calculated using the information obtained from the Network Monitoring tools such as NWS, Ganglia. We also propose a Standard methodology in computing the estimated execution time, which plays a vital role in effective scheduling. Figure. 4. Trust Resource Broker The sequence of the operations of Trust Resource Broker
(1) User submits the job to the portal
(2) Portal directs the user's job request to the Gridway
(3) The information Manager of Gridway queries the list of the Nodes available in the grid.
(4) Gridway retrieves the available list of resources
(5) Gridway invokes the TMS for the selection of trusted Resource
(6) TMS selects the Resource on the basis of trust value using the Database
(7) TMS sends the highest trusted Resource Id to the Gridway
(8) Gridway submits the job to the selected Resource Id
(9) Gridway invokes the TMS for updating trust metrics
(10) TMS updates the Number of nodes, and Bandwidth of the selected Resource
(11) The Output of the job is obtained from the Resource to Gridway
(12) Gridway invokes TMS for the updating Job status
(13) TMS updates the Job final status in the database
(14) Gridway results the output to the User
(15) User submits the feedback to the TMS
(16) TMS computes the Trust value per job and the hence the final trust of a Resource
Closest prior Art
Akogrimo will bring together the Grid world with the mobile Internet. Within this context it should be mentioned that a lot of currently deployed security mechanisms provided by the network have not been developed for the mobile Internet where e.g. a user might change the Internet Protocol address e.g. once each 10 seconds. In the current Grid world a lot of security mechanisms have been deployed and are under development which do no directly communicate and interact with security mechanisms from the lower layer. Within Akogrimo a cross layer security framework will be developed providing the security support for users connected to a "commercial11 mobile Internet and accessing commercial Grid services in a dynamic way. The potential contribution of Akogrimo to the related Grid projects in the community are first the provision of new requirements coming from a commercial mobile Internet which immediately come to the concept of Mobile Virtual Dynamic Organizations (MVDOs) and the distribution of overall security features across the overall protocol stack. Daidalos
Daidalos is an IP focusing on network infrastructure but also with service aspects. It is driven by operators and already incorporates rather new and emerging concepts like mobility and context-awareness. Security and privacy are inherent parts from the beginning on. Grid systems have to rely heavily on communication. Moreover, they are in need of a huge infrastructure being potentially provided by operators that need to earn money with it. Therefore, a close interaction of Grid systems with the network is necessary. EGEE
The EGEE security activities comprise three independent but interrelated topics: global trust establishment for authentication, operational security responsibilities and incident procedures and increasing the robustness and deployability of grid middleware security mechanisms. Global trust building is accomplished through the European Grid authentication policy management authority for e-Science (EUGridPMA for short). This body defines common guidelines for authenticating entities in the Grid and accredits authentication authorities according to those guidelines. EGEE has established a Joint (Operational) Security Group to consider other operational aspects such as authorization responsibilities, common Acceptable Usage Policies (AUPs) and distributed security incident response. Finally, EGEE is also re-engineering its current middleware to use a service oriented architecture (SOA) built using Web Services. This includes a new Authorization model in which delegation is tokenized and no longer depends on user identity authentication. HPC4ZJ
The objective of the HPC4U project is to expand the potential of the Grid approach to Complex Problems Solving through the development of software components for a dependable and reliable Grid environments and combining this with Service Level Agreements (SLA) and commodity based clusters providing Quality of Service (QoS). Development of HPC4U will take place in a Grid context following standards of the Global Grid Forum (GGF). HPC4U will not focus on developing security mechanisms, but leverages trust and security work of other projects to achieve reliability, predictability and dependability. NextGRID
The goal of NextGRID is to develop architectural models and components that will lead to the emergence of the Next Generation Grid that is economically viable and useful to business and society. To achieve its goals NextGRID has integrating activities covering Grid architecture, business and operational issues, applications and standards and development activities covering Grid foundations and core services, Grid dynamics and federation models and Grid user interaction models.
Security and Trust are key issues in NextGRID, without which it cannot meet the needs of business or society. Privacy is also important to enable participation by the public.
To address these issues, security will be built into the NextGRID architecture at all levels and will be a focus for the architecture design activity from the beginning of the project. This will cover secure communication, authentication, authorization, roles, firewall management and security policy enforcement. NextGRID addresses these aspects at the level of services (through its Foundations work) and in service federations (through its Dynamics work). The interaction between security and management (expressed through VO models), including de-centralised and P2P management mechanisms and VO lifecycles, is of considerable interest in dynamic federation scenarios. NextGRID is also concerned with operational security requirements from business, including mechanisms and policies internal to a site for protecting resources and recovery strategies following a breach. This work will focus on extending risk management methods to users of the Grid and using this to generate operational policies that are relevant to business and societal scenarios. EU-Provenance
The overarching aim of the Provenance project is to design, conceive and develop an industrial-strength, open provenance architecture for grid systems and to deploy and evaluate it in complex grid applications, namely aerospace engineering and organ transplant management. This support includes a scalable and secure architecture, an open proposal for standardizing the protocols and data structures, a set of tools for configuring and using the provenance architecture, an open source reference implementation and a deployment and validation in industrial context. SIMDAT
The goals of SIMDAT are to test and enhance data grid technology to enable and support product and process design and service provision across four important industrial sectors: automotive, aerospace, pharmaceuticals and meteorology. The main outputs will be a set of generic application enabling tools produced through transfer of technology between sectors and from underlying Grid developments, applied to enable Grid applications in the target sectors. Trust and security are fundamental to SIMDAT, as they provide the basis for federating resources (including data and knowledge) between collaborating organizations in these highly competitive industrial sectors. The bulk of the work on Trust will focus on how to represent and manage Trust in the context of VOs. It is expected that this will be stimulated by the aero application sector, where collaboration is well established even from
the early design stages for a new product. Security technology will be developed mainly at
the Grid infrastructure, and at the data access and integration.
TrustCoM is developing an integrated framework for trust, security and contract management for collaborative business processing in dynamically-evolving Virtual Organizations (VOs). A realization of the TrustCoM framework will be delivered by means of open-standards, web services based specifications and a reference implementation. Validation will take place within testbeds in the areas of collaborative engineering (CE) and provision of ad-hoc, dynamic processes for aggregated electronic services (AS).
TrustCoM addresses trust and security issues across the complete VO life-cycle, including discovery and justified identification of credible, trusted partners (VO Identification), establishment of trust between VO members (VO Formation), maintenance of trust, autonomic security management, adaptive deployment of security policies (VO Operation and Evolution), and termination of trust relationships and maintenance of trust knowledge (VO Dissolution).
Security, the protection of sites and users from malicious users and delegation, users authorizing servers to perform actions on their behalf, are of fundamental importance to Grid Computing. An effective Grid infrastructure will strike the appropriate balance between good security and flexible delegation.
The UNICORE approach to security and delegation is known to be strong, but this strength creates a tension with the flexible deployment of OGSA based Web Services. For example, the Generic Service Portal will create a job description for a user but, under the current model, it is unable to obtain the explicit authorization of the work that is required by the UNICORE servers. UniGridS will extend the UNICORE security architecture to support explicit statements of trust, to give the level of flexibility needed to support dynamic delegation, but without undermining the basic UNICORE security architecture. This increased flexibility will also facilitate the incorporation of emerging standards in Web Service and Grid security.
We claim, a method for Trust Management System, the system that optimally selects the grid resources being part of a grid computing system, the method comprising:
 We claim, Design and Development of Trust Management System (TMS) for Grid Resource Providers in a computational grid environment.
 We claim, Trust as "the degree of belief in the resource provider's competence to complete user's task dependably, securely and reliably in a specific context at a given time".  We claim, A Grid computing system, Equipment provision trust/Resource provision trust - It describes trust in principals for the purpose of accessing resources owned by the relying party. A trustor trusts to use resources that he owns or controls. It measures whether a resource provided by the resource provider is trustworthy. The QoS offered by the resource will determine this trust.  We claim, Lifecycle of Trust Management System has 5 steps
As per Claim 4, Trust Metric Identification is the first stage of a trust management system
in which the required trust metrics from which the given trust of an entity can be defined is
As per Claim 4, Trust Metric Evaluation
In the next stage, a suitable methodology is applied to determine the value of those
metrics. As per Claim 4, Trust Metric Calculation Once the values for all the metrics are computed, the overall trust value are determined using the values. It requires formalization of trust model expressed in terms of the metrics identified. The calculated trust is then stored in the database for further use. As per Claim 4, Trust Value updation
This module periodically monitors the resource performance metrics and computes the overall trust value and updates in the database. Similarly, at the end of every resource
access, the user feedback and resource performance metrics are obtained, and overall trust value is computed and updated in the database.
 We claim , The method for computing Trust Management System evaluates the trust value of all grid resource providers and facilitates the selection of suitable resource for job execution based on the trust value. It computes trust value of a resource provider based on the following three factors :-
• Infrastructure of the organization that provides a grid resource to the grid
• Feedback from the user after accessing the resource and
• Performance metrics of the particular grid resource.
As per claim 5, Resource Performance Module obtains the performance metrics of every resource providers and uses them in evaluating their trust. The performance metrics considered are as follows:-
• Actual Execution Time Actual Execution time is defined as the time taken by the resource provider in executing a job. It is the sum of the CPU time and the I/O waiting time of a job. Actual Execution time reflects the capability of resources in executing a particular job.
• Affordability Availability is defined as the time during which the resource provider is available over a period of time. It brings out the difference between the Uptime (the period of time, the resource provider is ready for execution of job) and the Downtime (the period of time, the resource provider is not ready for the execution of job). Hence this plays an important role in evaluating the consistency of the Resource provider.
• Success denotes the state of the job after being executed by a particular resource provider. The accumulation (total) of success jobs helps us in determining the success rate of the resource provider's success rate in executing jobs.
• Failure denotes the state of the job after being executed. More failure in executing a job reflects the inefficiency of a resource provider. The accumulation (total) of failure jobs helps us in determining the resource provider's failure rate in executing jobs.
• Estimated Execution time Estimated Execution time is defined as the time that will spend on the CPU and I/O operations without actually executing a job. Estimated Execution time reflects the capability of resource in executing a particular job well in advance, without actually executing a job. The difference between the Estimated Execution time and the Actual Execution time reflects the efficiency of the resources in executing jobs.
• Bandwidth can be defined as the speed with which data can be sent to a target resource. It is measured in megabits/seconds. The purpose of considering this parameter is to determine the resource provider's network performance since it reflects the throughput of the communication link.
• Latency is the amount of time in milliseconds, required to transmit a tcp message to a target resource. The purpose of considered this parameter is to determine the resource provider's network performance since it reflects the round trip time of the communication link.
As per claim 5, Resource Registration Module obtains infrastructure information of the resource provider during registration of the resource in to the grid. We express this information in terms of the following parameters:-
• Governing body of the organization. This parameter allows us to classify the organization into public or private assuming public organization has a greater trust over the private organization.
• Registration number of the private organization.
• Security Level that the organization supports in the resource for job execution. These parameters are collectively called as resource registration parameters and
they reflect the reputation and confidentiality of the resource provider in the user
As per claim 5, User Feedback Module This module obtains the user's feed back about a
particular resource provider by prompting him to mention the level of satisfaction and
willingness to recommend the resource to others. With this information, we classify the
trust level of resource providers in to following seven categories namely Excellent, Very
High, High, Medium, Low, Very Low. The two parameters namely the level of satisfaction
and willingness to recommend are collectively called as user feedback parameters and they
reflect the behavior of resource provider with user community.
As per claim 5, Trust Metrics Computation module applies various methodologies to
compute the value of trust metrics received from underlying resources. Several tools such
as Ganglia, Network Weather Service were identified to determine the values of the metrics
and they are integrated with the trust management system. The values are sent to the trust
computation module to calculate overall trust value.
As per claim 5, Trust Computation module gathers the input from all the above modules
and computes the overall trust of a resource provider and stores it in the database. The trust
represents the trustworthy of the resource provider at a given instant of time.
 We claim, Trust based Scheduling is working based on Job submission portal has
the provision to enter the user's name and the password is verified in login phase. After the
successful login, the user can submit the job upon their requirements. The portal has the
provision for the users to view the submitted job and the corresponding details.
 Gridway identifies the highest trust valued service provider.
Once the job has submitted, the Gridway Metascheduler searches for the most trusted Service provider and submits the job. Thus, Gridway eliminates the user's resource selection process, and to submit the job in a most trusted Service provider to enhance the job accuracy. After every job submission, Gridway updates the trust metrics in the database along with user's details.  Feedback of the service provider by the user is obtained using grid portal
Once the job has been successfully executed, the user can express their views about the service provider. A portal is developed to obtain the user's feedback on the Service provider updates the feedback in the database. This helps in maintaining the feedback history of a Service provider
 We claim, Trust Resource Broker is a system that works based on the integration of Trust management system with Gridway Metascheduler.
In our invention, we have designed a system for trust management system that computes the trustworthiness of the grid resources using several parameters. This system is integrated with gridway metascheduler to select resources based on their trust value. This system integration resulted in enhancement of capability of gridway metascheduler to perform either trust based scheduling or conventional scheduling thereby giving a flexibility to the user to select the functionality of his interest.
|Indian Patent Application Number||593/CHE/2007|
|PG Journal Number||24/2010|
|Date of Filing||23-Mar-2007|
|Name of Patentee||ANNA UNIVERSITY|
|Applicant Address||PROFESSOR, GRID COMPUTING LAB, DEPARTMENT OF IT, MIT, CHROMPET CHENNAI-44|
|PCT International Classification Number||G06F 17 /30|
|PCT International Application Number||N/A|
|PCT International Filing date|