network aware resource allocation in distributed clouds

Network Aware Resource Allocation in Distributed

Clouds

Contribution

Develops efficient resource allocation algorithms

The developed 2-approximation algorithm for optimum Data Center(DC) selection is found to be quite efficient

Develops a heuristic for partitioning the requested resources among the chosen DCs and racks

Minimizes distance (latency) between the selected DCs

Simulations show that this approach yields significant gains

Introduction

Resource allocation – a key function of cloud management and automation

Resource allocation algorithms have high impact on performance of applications

Also affects the efficiency of DCs in accommodating requests

User requests require allocation of Virtual Machines(VMs)

To satisfy these requests, resource allocator maintains updated list of resources available at DCs, current allocations and future requirements.

Introduction

User requests include number of VMs and the communication links required between the VMs

Automation software’s objective is to choose the DC and rack such that overall resource usage is minimized and optimal performance is achieved

These two goals are complimentary Usually involve attempts at allocating all requested

resources onto a single rack – not always possible Thus, for best results, resource allocation algorithms

that are capable of handling many scenarios are required

Introduction

Fragmentation of user requests reduces performance

Difficult to solve fragmentation

This paper focuses on resource allocation problem in distributed cloud systems spread out geographically over WANTarget : latency

System ArchitectureDistributed Cloud

Requests should be handled by DCs close to them – helps improve performance

Racks consist of blade servers, each containing many cores

Communication between multiple blade servers within the same rack happen via TOR switch

Two different racks communicate using aggregator switch

DC networks designed with assumption of locality of communication


As distance between machines increases, the bandwidth decreases

Bandwidth depends on physical machines that the Virtual Machines(VM) are assigned to- Overall efficiency of a DC also depends on this- Number of requests serviceable by the DC also

depends on this

System ArchitectureCloud Management and Automation

S/W

Prior knowledge about communication links may not be available

Automation S/W have to assign resources based on worst case conditions and then re-optimize

There are also other conditions that need to be satisfied- Number of VMs / DC (for fault tolerance)

Automation S/W computes mapping of user requests to physical machines


S/W

The output of the cloud automation software is a mapping of VMs to physical resources

The software interacts with Network Management System (NMS) and the local Cloud Management System (CMS)

The cloud optimization software has two functionalities- Track resource usage- Optimize assignment of user requests

Assignment of user requests consists of identifying DCs and machines

Goal: To reduce inter-DC, intra-DC traffic


S/W


S/W Assignment of DCs is done in 4 stepsI. DC Selection

• Identify DCs based on user constraints and availability• Identify subset of DCs that minimize latency

II. Partitioning Across DCs• Minimize inter-DC traffic• Adhere to given constraints and partition VMs accordingly

III. Rack, Blade, Processor selection• Identify physical computational resources in the DCs• Goal : Identify machines with low inter-DC traffic

IV. VM Placement• Assign individual VMs to physical resources• Minimize inter-rack traffic

System ArchitectureData Center Selection

Select DCs that meet All specifications and constraints Optimize network resources Maximize application performance

Use an algorithm that selects a subset of DCs with least hops

Handle other constraints such as maximum or minimum VMs / DC


DC selection problem – sub-graph selection problem Given G = (V,E,w,l)

- V – Data Centers- E – Path between DCs- w – number of available VMs at DC- l – distance of these paths

Note : - If there are constraints on maximum number of VMs / DC, w

takes this value instead- If there is a constraint of the minimum number of VMs / DC,

DCs with fewer VMs are omitted


Let ‘s’ be number of VMs requested Problem : Find sub-graph of G whose sum is at least

‘s’ with minimum diameter Goal : Find sub-graph with minimum length of

longest edge NP-hard problem


This algorithm finds a star topology centered at v

Diameter of output sub-graph is at most 2x diameter of optimal sub-graph


Running Time FindMinStar has to be sorted O(nlogn)

- N number of DCs Computing diameter O(n2) O(FindMinGraph) = n * O(FindMinStar) = O (n3)

System ArchitectureMachine Selection within DC

Goal : Find machines that reduce inter-rack traffic DC topology is a tree topology

- Root – core switch- Children – top-level switches- Leaf – racks

Given the tree representation of the DC (T) and total number of VMs (s) to be placed

Find sub-tree with minimum height that has weight at least equal to ‘s’

System ArchitectureMachine Selection within DC

System ArchitectureVirtual Machine Placement

Heuristic algorithms required for assigning individual VMs to DCs and CPUs within DCs

Problem is a variant of graph partitioning and k-cut problem

User request represented as graph G = (V,E)- Nodes represent VMs to be placed- Edges represent connections between them

Goal : Partition G into disjoint sets c1, c2…cm such that communication along vertices is minimized

If traffic is asymmetric, take the average


Algorithms 4,5 give heuristic solution to partition problem

Optimized using Keringhan–Lin heuristics Runtime :

- O(n2logn)

Simulation Results

Results compared to random approach and greedy algorithm

Random approach selects random DC and places as many VMs as possible in the DC

Greedy selects DC with maximum VMs To measure performances

- Random topology created- Random user requests generated- Maximum distance between any two VMs measured

Simulation Results

Location of DCs randomly selected within a 1000x1000 grid

Distance between DCs is the Euclidean distance between points

Five different distributed cloud scenarios- 100 DCs- 75 DCs- 50 DCs- 25 DCs- 10 DCs

However, average machines on each cloud is the same

Simulation ResultsI Experiment

Measuring diameter of placement for a single request of 1000 VMs

Approximation algorithm performs 79% better Note : Diameter decreases as number of DCs

decreases

Simulation ResultsII Experiment

Study cloud systems with series of user requests Two experimentsI. 100 requests for 50 – 100 VMs

- Requests are uniformly distributed- Large requests

II. 500 requests for 10 – 20 VMs- Small requests

Note : In both experiments, average VMs requested is the same


Greedy performs better than random by 32.6% and 66.5%

Approximation algorithm performs better than greedy by 83.4% and 86.4%

Why do larger requests require higher diameter?

Simulation ResultsIII Experiment

Studies performance of cloud system when additional constraints are given

Same requests as previous experiment Resilience is defined as ratio of total VMs to

maximum VMs at any DC Requests need to be placed in at least resilience

number of DCs


Larger requests have longer diameter As resilience increases, diameter increases What is different about these results?


Performance of heuristic algorithm Given communication requirements and available

capacity of DCs, algorithm computes optimal placement of VMs that minimizes inter-DC traffic

Comparison of heuristic algorithm with greedy and random algorithms

Random assigns random DC to each VM Greedy selects DCs in decreasing order of

availability While selecting VMs, it chooses VMs with maximum

total traffic first


Experiment assigns a request of 100 VMs to DCs Bandwidth fixed randomly between 0 and 1 Mbps Inter-DC traffic for assignment of these VMs to k DCs

(k = 2,…,8) was studied Available resources at each DC were between 100/k

and 200/k Hence 100 VMs were being assigned to DCs

consisting of 100 – 200 VMs


For all algorithms inter-DC traffic increases as number of DCs increase…Why?

Greedy algorithm performs better than random by 10.2%

Heuristic algorithm performs better than greedy by 4.6%


When the DCs did not have excess capacity, inter-DC traffic was higher for heuristic algorithm by 28.2%

Heuristic algorithm performed better than the other two algorithms by 4.8%

Greedy and Random had similar performances

Simulation ResultsIV Experiment

In this experiment, effect of VM traffic on inter-DC traffic is studied

The percentage of links with traffic is varied between 20% and 100% and inter-DC traffic is measured

The DCs have no excess capacity in these experiments

Result: inter-DC traffic grows linearly with percentage of links with traffic for all algorithms

Conclusions

Main contribution is development of algorithms for network-aware resource allocation of VMs in distributed cloud systems

Need for these efficient algorithms :Inter-DC traffic may be very expensive

2-approximation algorithm provided for selection of DCs

This algorithm can also be used for rack selection within DC but using prior knowledge about network topology within DC gives better results

Heuristic algorithm for mapping VMs to resources within DC

Related Work

Graph partitioning problems K-cut problem Maximum sub-graph problem Assigning VMs inside DCs studied in

Improving the scalability of data center networks with traffic-aware virtual machine placement

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network aware resource allocation in distributed clouds

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