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QoS-GRAF: A Framework for QoS-based Grid Resource Allocation with Failure Provisioning

QoS-GRAF: A Framework for QoS-based Grid Resource Allocation with Failure Provisioning. Resource. Revenue. Objective: Business revenue maximization based on service differentiation. Base Storage. 1000$ for 200 MB. Incremental Storage. 100$/MB upto 1 GB. Grid as a utility-based service

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QoS-GRAF: A Framework for QoS-based Grid Resource Allocation with Failure Provisioning

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  1. QoS-GRAF: A Framework for QoS-based Grid Resource Allocation with Failure Provisioning Resource Revenue Objective: Business revenue maximization based on service differentiation Base Storage 1000$ for 200 MB Incremental Storage 100$/MB upto 1 GB • Grid as a utility-based service • Resources offered by grid providers and applications (jobs) executed by consumers • QoS-GRAF: Resource Provisioning Framework for Next Generation Grids • SLA-based service differentiation • Advanced reservations to handle failures Base Bandwidth 1500$ for 10 Mbps Incremental Bandwidth 25$/Mbps upto 1 Gbps Storage Bandwidth Revenue Approximate with discrete step function Demand Batch of Jobs 200, 1000 Primary Revenue = 1000$ Primary Revenue = 1000$ QoS-GRAF Architecture SLA Manager SLA Manager Admission Admission Admission SLA Manager Admission Control 100, 750 Control Control Control Application Application Revenue Application level objectives level objectives level objectives R1 50, 250 R2 Worst Case Loss:250$ Resource level Resource level Batch Batch Batch Batch Scheduler Resource level Objectives Objectives Objectives Scheduler Scheduler Scheduler 200 Units 200 Units R3 Demand SLA DB SLA DB QoS-GRAF Controller 100 Units Utility DB Primary Job 1 Primary Job 2 Primary Primary Backup Backup Primary Allocator Backup Allocator Allocator Allocator Allocator Allocator Monitoring Information 200, 1000 Shared Backup (Job1,Job 2) 100, 800 Resource Provisioning Manager Resource Provisioning Manager Revenue 50 Units Available 50 Units Available 50, 100 REDUCE OVERBOOKING MINIMIZE REVENUE LOSS Demand Physical Resource Pool Physical Resource Pool Job k, Resource j, QoS level i, Dependency δ Uδ(i, j, k) : Indicator variable Rδ(i, k) : Revenue αδ(k) : Loss (difference of primary and backup revenues) INPUT (1) Batch of K jobs (2) SLA specifications for each resource dependencies (CPU,network,storage,DB) (3) SLA captured by a discrete step function with multiple QoS levels (4) Available resource capacities Maximum Revenue Primary Allocation (MRPA) AND Minimum Loss Backup Allocation (MLBA) Maximize ∑i∑j∑k∑δUδ(i, j, k)*Rδ(i, k) Minimize ∑k∑δαδ(k) MRPA MLBA • Backup capacities must not be exceeded • Disjoint primary and backup for each resource • Jobs that do not have primary allocations on same resource can share backup resources • No job is allocated a backup at a QoS level higher than its primary • Resource capacities must not be exceeded • All jobs must be assigned primary and backup • Each job is allocated on a resource at a single QoS level MRPA GIVES 30% GAIN IN REVENUE SMALL JOBS MEDIUM JOBS LARGE JOBS Better QoS Levels For Medium/Large Jobs SLA based Allocation + Failure Reservation QoS LEVELS NEAR-OPTIMAL PERFORMANCE SCALABLE WITH LARGE BATCHES Increased $$ SMALL JOBS LARGE JOBS MEDIUM JOBS Prototype Implementation SLA-driven Scheduling Integrate with Grid Middleware Backup QoS closer to Primary QoS Level QoS LEVELS MLBA SAVES 20% LOSS IN REVENUE

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