Mitigating Multi-Tenancy Risks in IaaS Cloud Through Constraints-Driven Resource Scheduling

Mitigating Multi-Tenancy Risks in IaaS Cloud Through
Constraints-Driven Virtual Resource Scheduling
Khalid Bijon, 
Ram Krishnan
, and Ravi Sandhu
The University of Texas at San Antonio, USA
ACM Symposium on Access Control Models and Technologies
(SACMAT 2015)
Vienna, Austria
June 1-3, 2015
Cloud Service Models
2
Infrastructure as a Service (IaaS)
Platform as a Service (PaaS)
Software as a Service (SaaS)
 
Virtualized
hardware
infrastructure
 
App dev
environment with
cloud characteristics
 
Network accessible
software
IaaS Cloud: Virtual to Physical Mappings
VM
VM
VM
Hypervisor
VM
VM
VM
Hypervisor
VM
VM
VM
Hypervisor
VM
VM
VM
Hypervisor
Physical Machine
Physical Machine
Physical Machine
Physical Machine
Tenant 1: Need 3 VMs
Tenant 2: Need 3 VMs
Tenant 3: Need 2 VMs
Tenant 2: Need 3 VMs
Tenant 4: Need 1 VM
4
 
Multi-tenancy is unavoidable
in cloud platforms
Hypervisor provides isolation,
albeit tricky
E.g. Ristenpart et al
Level of Physical Resource Sharing
 
HIGH
 
LOW
 
PRIVATE
CLOUD
 
PUBLIC
CLOUD
 
$
HIGH RISK
 
$$$
LOW RISK
Constraints-Driven Co-location
Toward a programmable cloud platform for resource
isolation that can satisfy constraints such as:
“Do not co-locate 
sensitive 
VMs with 
low-sensitive
“Do not co-locate 
high-availability
 VMs in the same rack”
“Do not co-locate Exxon VMs with those of BP”
Must not co-locate
 vs. must co-locate
Scheduling problems
5
Attribute-Based Conflict Specification
for VM Co-location
Name-value pairs on VMs
E.g. sensitivity(vm
1
)=“high”, tenant(vm
2
)=“Acme”
Specified for VMs of each tenant
Intra-tenant (tenant-specified)
Varies from tenant to tenant
E.g. “sensitivity”, “group”, etc.
Inter-tenant (cloud service provider specified)
Available to VMs of all tenants
E.g. “tenant”, “flavor”, etc.
6
Sample Attributes for a Tenant
7
Conflict-Free Partitioning of Attributes
8
 
Step 1:
 
Step 2:
Finding MIN_PARTITION is similar to k-coloring: NP-Complete
O
(|ATTR
VM 
| x |PARTITION
att
|)
Co-Resident VM Scheduling
9
 
Step 3: 
Partitions of co-resident VMs
 
Step 4: 
Scheduling of co-resident VMs into physical hosts
Similar to bin-packing: NP-Hard
O
(|VM| x |ConflictFreeATTR| x |ATTR
VM
|)
 
Not a problem introduced by this work
Experimental Setup
OpenStack deployed on 5 physical machines
Each is a Dell R710 with 16 cores, 2.53 GHz and
98GB RAM
Each VM simulated as a physical host to simulate
100s of physical hosts
10
Conflict-Free Partition Using Backtracking
11
Small-ish scope and
conflict set
Large scope and
conflict set
Scheduling Latency After Partitioning
12
#Hosts
13
With varying number of
elements in Conflict-Set
With varying number of
maximum degree of
conflicts
Host Utilization
14
Conflict Changes
Conflict specification can change over time!
Changes can be of different types
Type 1: remove an element from the ConSet
att
Type 2: add an element to ConSet
att
PARTITION
att
 remains unchanged
Type 3: add an element to ConSet
att
PARTITION
att
 changes -> may need to migrate
15
Migrations
16
% of Conflict for a Given Scope
% of Total VMs that Require Migration
Ongoing/Future Directions
17
 Constraints that span further levels of abstractions
o
 PaaS and SaaS
Ongoing/Future Directions (continued)
Constraints involving other virtual resources
Storage, Network, etc.
Managing conflict changes over time
Incremental conflict specification
Attribute computation to inform conflict
specification
18
Summary
A conflict specification framework for
resources in IaaS
 Conflict-free partitioning is NP-Complete
Prototyped and experimented in OpenStack
19
Thank you!
20
Slide Note
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Addressing the challenges of multi-tenancy in Infrastructure as a Service (IaaS) clouds, this research focuses on constraints-driven virtual resource scheduling to enhance security and isolation among tenants. The study discusses co-location strategies, attribute-based conflict specifications, and conflict-free partitioning techniques for efficient resource management in cloud platforms.

  • Multi-Tenancy Risks
  • IaaS Cloud
  • Constraints-Driven Scheduling
  • Resource Isolation
  • Cloud Security

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  1. Mitigating Multi-Tenancy Risks in IaaS Cloud Through Constraints-Driven Virtual Resource Scheduling Khalid Bijon, Ram Krishnan, and Ravi Sandhu The University of Texas at San Antonio, USA ACM Symposium on Access Control Models and Technologies (SACMAT 2015) Vienna, Austria June 1-3, 2015

  2. Cloud Service Models Network accessible software Software as a Service (SaaS) App dev environment with cloud characteristics Platform as a Service (PaaS) Virtualized hardware infrastructure Infrastructure as a Service (IaaS) 2

  3. IaaS Cloud: Virtual to Physical Mappings

  4. PUBLIC CLOUD PRIVATE CLOUD $ HIGH RISK $$$ LOW RISK Level of Physical Resource Sharing HIGH LOW VM VM VM VM VM VM VM VM VM VM VM VM Hypervisor Hypervisor Hypervisor Hypervisor Physical Machine Physical Machine Physical Machine Physical Machine Multi-tenancy is unavoidable in cloud platforms Hypervisor provides isolation, albeit tricky E.g. Ristenpart et al Tenant 1: Need 3 VMs Tenant 2: Need 3 VMs Tenant 3: Need 2 VMs Tenant 2: Need 3 VMs Tenant 4: Need 1 VM 4

  5. Constraints-Driven Co-location Toward a programmable cloud platform for resource isolation that can satisfy constraints such as: Do not co-locate sensitive VMs with low-sensitive Do not co-locate high-availabilityVMs in the same rack Do not co-locate Exxon VMs with those of BP Must not co-locate vs. must co-locate Scheduling problems 5

  6. Attribute-Based Conflict Specification for VM Co-location Name-value pairs on VMs E.g. sensitivity(vm1)= high , tenant(vm2)= Acme Specified for VMs of each tenant Intra-tenant (tenant-specified) Varies from tenant to tenant E.g. sensitivity , group , etc. Inter-tenant (cloud service provider specified) Available to VMs of all tenants E.g. tenant , flavor , etc. 6

  7. Sample Attributes for a Tenant 7

  8. Conflict-Free Partitioning of Attributes Step 1: Finding MIN_PARTITION is similar to k-coloring: NP-Complete Step 2: O(|ATTRVM | x |PARTITIONatt|) 8

  9. Co-Resident VM Scheduling Step 3: Partitions of co-resident VMs O(|VM| x |ConflictFreeATTR| x |ATTRVM|) Step 4: Scheduling of co-resident VMs into physical hosts Similar to bin-packing: NP-Hard Not a problem introduced by this work 9

  10. Experimental Setup OpenStack deployed on 5 physical machines Each is a Dell R710 with 16 cores, 2.53 GHz and 98GB RAM Each VM simulated as a physical host to simulate 100s of physical hosts 10

  11. Conflict-Free Partition Using Backtracking Small-ish scope and conflict set Large scope and conflict set 11

  12. Scheduling Latency After Partitioning 12

  13. #Hosts With varying number of elements in Conflict-Set With varying number of maximum degree of conflicts 13

  14. Host Utilization 14

  15. Conflict Changes Conflict specification can change over time! Changes can be of different types Type 1: remove an element from the ConSetatt Type 2: add an element to ConSetatt PARTITIONatt remains unchanged Type 3: add an element to ConSetatt PARTITIONatt changes -> may need to migrate 15

  16. Migrations % of Conflict for a Given Scope % of Total VMs that Require Migration 16

  17. Ongoing/Future Directions Constraints that span further levels of abstractions o PaaS and SaaS 17

  18. Ongoing/Future Directions (continued) Constraints involving other virtual resources Storage, Network, etc. Managing conflict changes over time Incremental conflict specification Attribute computation to inform conflict specification 18

  19. Summary A conflict specification framework for resources in IaaS Conflict-free partitioning is NP-Complete Prototyped and experimented in OpenStack 19

  20. Thank you! 20

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