Multi-dimensional SLA-based Resource Allocation for Multi

Report
Multi-dimensional SLA-based
Resource Allocation for Multi-tier
Cloud Computing Systems
Hadi Goudarzi and Massoud Pedram
University of Southern California, Los Angeles
in IEEE Cloud 2011
(The 4th International Conference on Cloud Computing)
Agenda
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Introduction
System model
Problem formulation
An upper-bound on the total profit
Profit maximization solution
Simulation results
Conclusion
2
Introduction
• Modern internet applications are complex software
implemented on multi-tier architectures.
– Each tier provides a defined service to the next tiers and uses
services from the previous tiers.
• The problem of resource allocation for multi-tier
applications is harder than that for single-tier applications
– tiers are not homogenous
– a performance bottleneck in one tier can decrease the overall
profit, even if the other tiers have acceptable service quality.
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Introduction (cont’d)
• The IT infrastructure provided by the datacenter
owners/operators must meet various SLAs established
with the clients.
• The problem of optimal resource provisioning is
challenging due to the diversity present in the client
applications being hosted and the SLAs.
– reduce the cost incurred on the datacenter operators
– meet the clients’ SLAs.
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Introduction (cont’d)
• This paper focus on the SLA based resource allocation in
the cloud computing system.
• Two types of SLA contracts:
– For Gold SLA class: response time is guaranteed and if this
constraint is violated, the cloud provider pays a penalty.
– For the Bronze SLA class, each client has a defined utility
function based on its response time.
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Introduction (cont’d)
• In this paper, we assume that servers are characterized
by their maximum capacity in three dimensions:
– processing power
– memory usage
– communication bandwidth
• Objective
– An SLA-based multi-dimensional resource allocation scheme for
the multi-tier services in a cloud computing system to optimize
the total expected profit.
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System model
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ui1
1
u
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wi6
vi4
i2
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2
vi5
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for each server j:
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Problem formulation
Profit
Total allocated
resource  1
Memory
If tij is not zero, the value of ztij is set
to one, otherwise the value of ztij is zero.
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Average response time estimation
• We consider generalized processor sharing at each queue.
• Based on the queuing theory, the output of each queue
with this characteristic has an exponential distribution
with a mean value of 1/(-).
– : average service rate, : average arrival rate
Average response time in tier t :
Average response time :
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Profit
• Utility function
– Gold SLA:
– Bronze SLA: Ubi(Ri) is a nonincreasing function of the average
response time.
• Cost (power consumption)
• Profit in a duration Te:
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Baseline solution
• Optimization parameters:
• Iterative method (IM) [12][13]
– fix the resource shares and
optimize the task distribution rates
– and then fix the distribution rates and
optimize the resource shares
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Profit upper-bound problem statement
• Release some constraints and fix tij
This problem can
be solved using
KKT condition.
– Find the best case of assigning the client’s requests to the servers tij .
– solve the problem for each client independently and
sum up the results of best profits for all clients
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Profit maximization solution
1. An initial solution based on the solution given for the
profit upper bound problem is generated.
2. Distribution rates are fixed and resource sharing is
improved by a local optimization step.
3. Finally a resource consolidation technique, inspired by
the force-directed scheduling, is applied to consolidate
resources, determine the active (ON) servers and
further optimize the resource assignment.
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Initial solution
• A greedy technique to rank the clients and the
application tiers for each client is used
• to determine the order of resource assignment
processing in our constructive approach.
• For each client, the following equation is used as its
ranking metric:
• For the selected client, tiers are ordered using a similar
metric:
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Initial solution (cont’d)
• Based on the solution given for the profit upper bound
problem, determine the ij and tij for each client.
Replaced by
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Force-directed Resource Assignment (FRA)
• Resource Consolidation using Force-Directed Search
• A client that has the highest force difference toward a
new server is picked and if the required server is
available, the load replacement is done.
• The definition of force is based on the partial profit
gained from allocating each portion of the clients’
request in a specific tier to a server with specific type.
Gold:
Bronze:
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Force-directed Resource Assignment (FRA)
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Force-directed Resource Assignment (FRA)
• After this replacement, forces are updated and the new
maximum force differential client-to-server assignment is
made.
• This algorithm continues until there are no positive force
differentials for any clients.
• Limit the number of tries in the algorithm and avoid
loops and lockouts
18
Simulations
• Consider 10 to 100 clients
– For each client the processing power and memory capacity
requirements are set with random variables.
• Ten different application tiers are considered.
• The number of application tiers for each client is selected
randomly to be between 3 and 5.
• The probabilities of going forward or backward
– average of 80% going forward and 20% backward for each tier.
• Service times for clients with different application tiers
are also modeled with random variables.
• Gold or Bronze SLA class with probability of 50%
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Simulations (cont’d)
• The first scenario (low server to client ratio)
– The average number of servers is 5n where n denotes the
number of clients.
• The second scenario (high server to client ratio)
– The average number of servers is set to 10n.
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Simulation results
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Simulation results (cont’d)
• An example of Resource Consolidation Improvement in
case of a bad initial solution.
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Simulation results (cont’d)
• Computation overhead
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Simulation results (cont’d)
• Average utilization factor of the servers
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Conclusion
• A closed-form formula for calculating the average response
time of a client’s request for multi-tier applications.
• A unified framework for handling both strong (Gold class) and
weak (Bronze class) SLAs.
• A provable upper bound on the total profit in the system for
given clients, resources and SLAs.
• A multi-dimensional resource allocation scheme to consider
communication resources for multi-tier applications that incur
large inter-server communications.
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Comments
• A well problem formulation.
• The upper bound is too loose.
• Computation overhead is high
– static dispatcher in this paper
• We can consider the effect of virtualization
– A host server has multiple application.
• Another issue
– How to design a dynamic dispatcher according to request arrival
and server condition ?
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