Trust Based Grid Scheduling Algorithm for Commercial Grids

Transcription

1 International Conference on Computational Intelligence and Multimedia Applications 2007 Trust Based Grid Scheduling Algorithm for Commercial Grids S.ThamaraiSelvi, P.Balakrishnan, R.Kumar, K.Rajendar Department Of Information Technology, Anna University Abstract Commercial grids are the use of grid computing within the context of a business or enterprise. In Commercial grids, users are regularly engaged with the distributed resource providers with whom they have little or no prior experience. The interaction between these distributed resource providers requires resource management and scheduling solutions. The allocation and scheduling of applications on a set of heterogeneous, dynamically changing resources is a serious problem. Current Grid schedulers schedule a given job according to the availability and the performance provided by resources at that moment without considering the previous knowledge of their performance. We have proposed a trust model and evaluated the trust based on affordability, success rate and bandwidth. This proposed trust model has been incorporated with Gridway meta scheduler and tested. While scheduling, the resources with higher trust values are selected by this proposed model. It has been observed that the success rate of the jobs submitted by the user is very much increased by this proposed trust based scheduling. 1. Introduction The Grid computing provides the ability to access, utilize, and control a variety of underutilized heterogeneous resources distributed across multiple administrative domains [1],[2],[3]. Grid Meta scheduler [4],[6],[7],[8] and grid middleware [10] is incorporated to manage and negotiate with these distributed resources [12] to identify the suitable resource for the submitted job. The conventional schedulers do not consider the previous service performance (of every grid resource) while selecting a suitable resource [4],[5],[6],[9]. We develop a trust model for evaluating the resource provider s trust and the same is combined with the scheduling algorithm (trust based scheduling algorithm) to allocate the resources to consumers by considering the trust relationship between these entities. 2. Proposed Trust Model and Rules for Evaluating the Trust The Proposed trust model [14-23] for computational trust is shown in Fig 1.The three modules of the Trust model are explained as follows 2.1. Resource Performance Module This module obtains the performance metrics of every resource provider and uses them in evaluating their trust by considering affordability, success rate and bandwidth Affordability: The ratio between the number of times resource available to the grid and number of attempts made to access the resource Success Rate: The number of successful execution of job by a computational resource against the total number of jobs submitted to the resource that indirectly reveals the expertise of resource provider /07 $ IEEE DOI /ICCIMA

2 Bandwidth: The speed of connectivity of the resource with the metascheduler. These parameters are collectively called as resource performance parameters. The resource provider s trust relation with the parameters are as follows. Rule 1: Resource Provider s trust is positively related to Affordability of Resource Provider. Rule 2: Resource Provider s trust is positively related to success rate of Resource Provider. Rule 3: Resource Provider s trust is positively related to Bandwidth of the Resource Provider. Fig 1: Block diagram of the Trust management system 2.2. Resource Registration Module This module obtains security infrastructure provided by the resource provider for execution. Rule 4: Resource Provider s trust is positively related to security level provided by the Resource Provider User Feedback Module This module obtains the user s satisfaction about a resource provider s service Rule 5: Resource Provider s trust is positively related to feedback about the Resource Provider. 3. Trust Implementation in a Metascheduler [11] The Trust Management system is coupled with the Gridway Metascheduler by modifying some of its components in such a way that the selection of resource is based on the Trust value calculated by the trust component and not based on the conventional Rank value of the resource. 3.1 Information Manager (IM) IM is used to discover the grid resources and also evaluate the trust metrics. Its workflow is modified with the algorithms. // Algorithm for the evaluation of the number of nodes in the Service provider for each selected resource 1) Status Detection 546

3 (a)query the Status of Service provider (b) if (Status = free) then Step 2 else Log message and exit 2) Number of nodes identification (c) if (number of nodes <> null) Step 3 else Log message and exit 3) Update the number of running nodes return number of nodes // Algorithm for the evaluation of Bandwidth for each selected resource 1) Network Status Detection (a)query the Network characteristics of Service provider (b) if (connectivity exists) then step 2 else Log and Exit 2) Update Bandwidth Update the Bandwidth in database return Network characteristics // Algorithm for the Job completion status 1) Job Status Detection (a)query the job status (b) if Status == finished step 2 elseif Status == pending step 1 elseif Status == failed Migrate job 2) Update job completion status Update success / failure in database 3.2. Execution Manager (EM) The Execution manager is modified to include the trust value. Trust metric computation: The affordability of a service provider is defined as the ratio Affordability (A R ) = N t / N c (1) Where N t = Number of times resource available to grid Resource provider (R), N c = Number of attempts made to access the Resource. Success Rate of a Service provider (R) can be obtained using the formula (2) as follows Success Rate (S R ) = J s /J t (2) J s = Number of jobs successfully executed by Resource provider J t = Total Number of jobs executed by Resource Provider. Network bandwidth is obtained using Table1. The Bandwidth of a Resource provider during the job submission is obtained using the Metascheduler and assigns the value for N R. The user can express the satisfiability through the feedback with the following levels as specified in Table

4 The level of security (SL) provided by the resource provider [13] is specified in Table 3. Trust updation: The trust per job j T is computed as follows:- Trust per job ( T j ) = A R N R S R F K S L (3) Where k may be 1, 2,..or 6 Using the equation (3), the overall trust of a Resource provider is then evaluated as follows (4) Where n is the total number of user s accessed the Resource provider (RP). This overall trust value is then updated in the database that will be considered for next iteration of job scheduling in a dispatch manager. 3.3 Dispatch Manager (DM) DM performs job scheduling based on the trust value integrated with the resources. Trust integration: Resource selector (RS) is used by the dispatch manager to select the most suitable host to run the job according to the host s over all trust value, architecture and other parameters specified in the user request. The resource selection in a dispatch manger follows resource filter based approach shown in Fig 2. The grid information provider (MDS) gives the information about all the resources in a community. From this, the resources that are not satisfied the user's requirements are filtered out. Remaining resources are arranged in a descending order of trust and the resource having highest trust value is selected for job submission. Trust aware Fig 2: Resource filter resource management and scheduling offer Quality of Service at application layer in grid environment. 548

5 Pseudo code for Trust based scheduling: 1. Scheduling while (task queue not empty): for each task in task queue: a) identify all the nodes available in grid environment; b) filter out the # of nodes that satisfies user requirements from (a); c) arrange the nodes that are available from (b) in their descending order of trust available from database; d) fetch a most trusted free host H from (c) and submits the job to that node (2); 2. Job submission If (job submitted successfully) monitor the job: if (number of submission < number of retries) re-submit the job to Host H; else re-schedule the job; if (Host H failure or pending state over a threshold period) opportunistic migration; if (job completed successfully) go to (3); 3. Updating the Reputation System for each task scheduled: a) forward the result to the respective clients; If (Client Receiving The Result) b) submit the feedback to the broker about that transaction; c) update reputation system and calculate the trust value(4); 4. Trust value calculation for each Host H: a. calculate the trust value; b. update the trust value into database; 4. Results and Discussion We evaluate the impact of trust in a scheduling algorithm by comparing the failure rate of jobs of trust aware scheduler against trust unaware scheduler. From Fig.3, we conclude that the number of failure of jobs in trust aware Gridway scheduler is minimum than the trust unaware scheduler. Since trust unaware scheduler follows simple match making algorithm, they do not pay attention to the past service history of the resource selected. Even though the resource continuously not service well, if it has the highest rank, it will be selected repeatedly in a trust unaware scheduler, which leads to increase in the failure rate. 549

6 Fig 3: Percentage failure Analysis 5. Conclusion In this paper, we identify the metrics to be considered for evaluating the trust of the resource provider in a commercial grid environment. We embed this trust value in a Meta scheduler s scheduling algorithm in such a way that to select the most trusted resource provider to the requestor. We could not evaluate the byzantine behavior of the user if we consider only subjective parameter i.e. feedback for evaluating the trust value of the resource provider. But our methodology to evaluate the trust value involves both objective and subjective parameters (feedback). Also the experiments on performance comparison of trust based scheduler against the trust unaware scheduler reveals that the percentage failure of jobs gets minimized by 40-70% as compared to the trust unaware scheduler. So we argue that the methodology explained in this paper gives a reasonable way of selecting the resource against its counterpart (i.e.) trust unaware scheduling algorithm that selects the resource based on simple matchmaking algorithm. 6. References [1] Foster, I., Kesselman C., Tuecke, S., The Anatomy of the Grid: Enabling Scalable Virtual Organizations, International Journal of Supercomputer Applications [2] Foster, I., Kesselman. C., Nick, J.M., Tuecke, S., The Physiology of the Grid: An Open Grid Services Architecture for Distributed Systems Integration, Open Grid Service Infrastructure WG, Global Grid Forum [3] Foster I, Kesselman C (editors). The Grid: Blueprint for a NewComputing Infrastructure. Morgan Kaufmann: San Fransisco, CA, [4] Ramin Yahyapour, Philipp Wieder (editors), Grid Scheduling Use Cases, GFD-I.064, Grid Scheduling Architecture Research Group (GSA-RG), March 26,

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