1. Network Aware Resource Allocation in Distributed Clouds

2. 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

3. 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.

4. 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

5. 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 WAN Target : latency

6. System Architecture Distributed 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

7. System Architecture Distributed Cloud 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

8. System Architecture Distributed Cloud

9. System Architecture Cloud 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

10. System Architecture Cloud Management and Automation 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

11. System Architecture Cloud Management and Automation S/W

12. Assignment of DCs is done in 4 steps I. 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

13. System Architecture Data 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

14. System Architecture Data Center Selection 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

15. System Architecture Data Center Selection 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

16. System Architecture Data Center Selection

17. This algorithm finds a star topology centered at v Diameter of output sub-graph is at most 2x diameter of optimal sub-graph

18. System Architecture Data Center Selection

19. Running Time FindMinStar has to be sorted  O(nlogn) N  number of DCs Computing diameter  O(n 2 ) O(FindMinGraph) = n * O(FindMinStar) = O (n 3 )

20. System Architecture Machine 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’

21. System Architecture Machine Selection within DC

24. System Architecture Virtual 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 c 1, c 2 …c m such that communication along vertices is minimized If traffic is asymmetric, take the average

25. System Architecture Virtual Machine Placement

27. Algorithms 4,5 give heuristic solution to partition problem Optimized using Keringhan–Lin heuristics Runtime : O(n 2 logn)

28. 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

29. 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

30. Simulation Results I 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

31. Simulation Results II Experiment Study cloud systems with series of user requests Two experiments I. 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

32. Simulation Results II Experiment

33. 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?

34. Simulation Results III 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

35. Simulation Results III Experiment Larger requests have longer diameter As resilience increases, diameter increases What is different about these results?

36. Simulation Results III Experiment 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

37. Simulation Results III Experiment 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

38. Simulation Results III Experiment 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%

39. Simulation Results III Experiment 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

40. Simulation Results IV 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

41. 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

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