Modeling and Optimization of Resource Allocation in Cloud [PhD Thesis Progress 3]

IntroductionSummary of the Previous Work

Literature ReviewCache Placement for Mobile Cloud Computing

Conclusion

Modeling and Optimization of Resource Allocation in CloudPhD Thesis Progress – Third Report

Atakan Aral

Thesis Advisor: Asst. Prof. Dr. Tolga Ovatman

Istanbul Technical University – Department of Computer Engineering

January 7, 2016

Atakan Aral Modeling and Optimization of Resource Allocation in Cloud



Conclusion

Outline

1 IntroductionContribution to the ThesisTime Plan

2 Summary of the Previous Work

3 Literature Review

4 Cache Placement for Mobile Cloud ComputingProblem ModelingProposed Solution

5 Conclusion




Conclusion

Contribution to the ThesisTime Plan

Outline



3 Literature Review


5 Conclusion




Conclusion


Journal Submission

Submitted to Future Generation Computer Systems, ELSEVIER (IF: 2.786)SI: "Middleware Services for Heterogeneous Distributed Computing"First Decision Date: Nov 15, 2015 (Under review as of Jan 06, 2016)Also presented in IEEE 8th International Conference on Cloud Computing,CLOUD 2015




Conclusion


Literature Review and Problem Modeling

Areas of interest:Mobile Cloud ComputingFog ComputingCloudlets, NanodatacentersSelf- and Context-aware Resource Management

Optimal Placement of Data Object Caches onto the CloudletsA distributed and context-aware algorithm




Conclusion


Outline



3 Literature Review


5 Conclusion




Conclusion


Gantt Chart

2015

7 8 9 10 11 12

TBM Evaluation

Manuscript Preparation

Journal Submission

Literature Review

Problem Modeling




Conclusion

Outline



3 Literature Review


5 Conclusion




Conclusion

Topology Based Mapping (TBM)

Main Idea

Map VM Clusters onto the federated cloud infrastructure based on their topology.

Decreases deployment latency (by placing VMs close to the broker)Decreases communication latency (by placing connected VMs to theneighbour data centers)Shortens execution time and increases throughputReduces resource costs (by balancing load and avoiding overload in any DC)




Conclusion

UML Activity Diagram




Conclusion

Excluded Points

Geo-distributed user accessVirtual Machine or Data ReplicationUser mobilityVirtual Machine Migration

Topology Based Matching is a semi-centralized algorithmComplete utilization, capacity and topology information of the data centersand the network is available at all peers.




Conclusion

Outline



3 Literature Review


5 Conclusion




Conclusion

Mobile Cloud Computing

1 Computation is carried out in the cloud and the mobile device acts a thinclient.

Mobile elements are resource-poor relative to static elements.Mobile elements are more prone to loss, destruction, and subversion than staticelements.Mobile elements must operate under a much broader range of networkingconditions.

2 Nearby mobile devices form a cloud to assist each other in computationintensive tasks.




Conclusion

Nano Data Centers

Small computation entities provided by ISPs on gateways/modems.Managed in a P2P architecture by the ISP.Main motivation is to reduce data center energy consumption.

Reuse already committed baseline powerAvoid cooling costsReduce network energy consumption




Conclusion

Fog Computing

Main motivation is to leverage Internet of ThingsApplications that require very low latencyGeo-distributed applicationsFast mobile applications (vehicle, rail)Large-scale distributed control systems

Computation can be on high-end servers, edge routers, access points, set-topboxes, vehicles, sensors, mobile phonesCooperation between edge and core




Conclusion

Cloudlets

"Data center in a box"Provided and owned by local businesses (e.g. coffee shops, offices)Allows code offloading using Virtual MachinesFall back to distant cloud or own resources of the mobile deviceLAN latency and bandwidthStores only cached data




Conclusion

Problem ModelingProposed Solution

Outline



3 Literature Review


5 Conclusion




Conclusion


Motivation

As the volume and velocity of the data in cloud is increasing, geographicaldistribution of where it is produced, processed and consumed is also gainingmore significanceMobile cloud computing offers a solution for the low-latency access tohigh-capacity computing resources.However, data is still mostly central and it is not feasible to replicate it in largenumber of geo-distributed locations.

Due to economical factorsDue to the limited storage capacity of the edge entitiesTo keep it consistent and available for analysis




Conclusion


Definition

Create caches of data objects on data centers and edge entitiesDecide the number and location of the caches based on:

Magnitude of user accessLocations of user accessCloud storage pricing

In an attempt to reduce:Data access latencyStorage cost




Conclusion


Issues and Requirements

Cost-Latency TradeoffCustomer preference for the level of aggression should be considered.

Complete topology information is no longer feasibleA distributed solution is necessary.

User access is dynamic and mobileThe solution must also be context-aware.

Edge entities have limited storage capacityConstraints must be respected.




Conclusion


Outline



3 Literature Review


5 Conclusion




Conclusion


Centralized Solutions

k-Medians Given a node set V with pairwise distance function d and servicedemands s(vj), ∀vj ∈ V , select up to k nodes to act as medians so asto minimize the service cost C(V , s, k).

C(V , s, k) =∑∀vj∈V

s(vj)d(vj ,m(vj))

Facility location Given a node set V with pairwise distance function d and servicedemands s(vj), ∀vj ∈ V and facility costs f (vj), ∀vj ∈ V , select a set ofnodes F to act as facilities so as to minimize the joint cost C(V , s, f )of acquiring the facilities and servicing the demand.

C(V , s, f ) =∑∀vj∈F

f (vj) +∑∀vj∈V

s(vj)d(vj ,m(vj))




Conclusion


Distributed Solution

Replication algorithm for the central storage:1 Create a cache for a data object in one of the neighbours.

Replication algorithm in the cache locations:1 Migrate the cache to one the neighbours.2 Duplicate the cache to one the neighbours.3 Remove the cache.




Conclusion


Sample Scenario

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Conclusion


ITERATION 1d: User demand locations

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ITERATION 1d: User demand received from c and f

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Conclusion


ITERATION 1d: Cache creation decision

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Conclusion


ITERATION 2f: Migration decision

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Conclusion


ITERATION 2c: Duplication decision

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Conclusion


ITERATION 3e: Migration decision

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Conclusion


ITERATION 3a: Migration decision

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Conclusion


ITERATION 3c: Removal decision

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Conclusion


Inputs

Demand for each data object i from each neighbour j : Dij

Average latency for each data object i from each neighbour j : Lij

Latency from each node k to each neighbour j : Njk

Cost of storing each data object i at each neighbour and current location j : Cij

User provided level of aggression: A




Conclusion


Operation conditions

Create a cache of object i at neighbour j iff:

LijDijA > Cij

Remove the cache of the object i at k iff:∑∀j

(LijDijA) < Cik

Duplicate the cache of the object i from k to l iff:

LilDilA > Cil ∧∑∀j 6=l

(LijDijA) > Cik




Conclusion


Operation conditions

Migrate the cache of the object i from k to l iff:∑∀j

(LijDijA)−(∑

∀j 6=l

((Lij + Nkl)DijA

)+ (Lil − Nkl)DilA

)> Cil − Cik

A special case where ∃!j[Dij > 0]:

NklDilA > Cil − Cik




Conclusion


Possible Problems and Solutions

Multiple migrations/duplications are feasiblePrefer the option with the greatest benefit

Both migration and removal as feasiblePrefer migration

A costly node blocks the migration pathDynamic aggression level




Conclusion


Contribution

There exists distributed VM replication methodsThe whole entity is replicated which is not feasible for big data.

There also exists distributed data storage methodsIn our model data is still stored centrally while caches are distributed.

As far as we are aware, all other studies apply a centralized approach.Not feasible in the case of mobile cloud computing where the topology is toolarge and dynamic.




Conclusion

Outline



3 Literature Review


5 Conclusion




Conclusion

Publications

Aral, A. and Ovatman, T. (2014). Improving resource utilization in cloudenvironments using application placement heuristics. In Proceedings of the4th International Conference on Cloud Computing and Services Science(CLOSER), pages 527–534.Aral, A. and Ovatman, T. (2015). Subgraph matching for resource allocation inthe federated cloud environment. In Proceedings of 8th IEEE InternationalConference on Cloud Computing (IEEE CLOUD), pages 1033–1036.Aral, A. and Ovatman, T. (2016). Network-Aware Embedding of VirtualMachine Clusters onto Federated Cloud Infrastructure. (Submitted to FGCSon 15-September-2015, under review as of 06-January-2016)




Conclusion

Summary

Journal SubmissionLiterature ReviewProblem Modeling

Cache Placement for Mobile Cloud ComputingDistributed Context-Aware Algorithm

To reduce latencyTo decrease costs




Conclusion

Thank you for your time.
