Application-level Resource Provisioning Gurmeet Singh, Carl Kesselman, Ewa Deelman

Outline Motivation Model Comparison of 2 provisioning algorithms Current work

Motivation Resource Provisioning Reduces uncertainty associated with workflow performance Allows deterministic performance Enables user based workload scheduling Allows co-allocation of resources Improves performance as compared to scheduling alone for certain class of applications.

Outline Motivation Model Comparison of 2 provisioning algorithms Current work

System Model Ascertaining the resource availability is central to the provisioning model. r = 1,…,R Grid sites in the system. Query Model: Er(n,d) = {<s1,c1,f1>,…,<sk,ck,fk>} Publish Model: Er(.) = {<s1,n1,d1,c1,f1>,…,<sk,nk,dk,ck,fk>} Global Resource Availability

Slots with FCFS scheduling

Slots with backfilling

Provisioning Model Allocation Plan : set of resource slots a = { rs1, rs2, …., rsn } С A Workflow G = (V,E) to be scheduled. The Workflow can consist of a scheduled and an unscheduled part. V = Vs U Vu Both the AP and the schedule can be build incrementally

Allocation and Scheduling Available Slots Workflow Scheduled Workflow over allocated slots.

Goal The goal is to identify and allocate an allocation plan, a, in order to optimize the following costs Total allocation cost Total scheduling cost Scheduling cost = SCω(a) = makespan of workflow G over a using ω. A single cost metric is created using a weighted sum of the two costs.

Outline Motivation Model Comparison of 2 provisioning algorithms Current work

Min-Min provisioning algorithm Modify the level-based Min-Min algorithm to minimize the total cost at each step instead of only the makespan. Also, do the allocation along with the scheduling.

Cost Metric Performance

GA Use a genetic algorithm based search A solution is a n (= |A|) bit binary string. Simple encoding and decoding. Selection Crossover Mutation Elitism Search using a fixed population size and certain number of iterations. GA paired with a simple list scheduling algorithm.

% reduction in total cost

Findings GA does a better overall allocation optimization than Min-Min Min-Min outperforms GA for low value of α. Min-Min performs worse with high values of α due to its non backtracking nature. GA performance depends on the cardinality of the global set A. The cost metric performs considerable allocation optimization for a little loss in makespan. “Application level Resource Provisioning” 2nd IEEE Intl Conf on E-Science and Grid Computing, Dec 2006.

Outline Motivation Model Comparison of 2 provisioning algorithms Current work

Current Work Comparison of provisioned and non-provisioned approach with a trace driven simulation. Multi-Objective GA (MOGA) Slot Generation mechanisms for real Grid schedulers.