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Deployment of Services in a Cloud Subject to Memory and License Constraints

Jim (Zhanwen) Li Carleton University. John Chinneck Carleton University. Marin Litoiu York University. Murray Woodside Carleton University. Deployment of Services in a Cloud Subject to Memory and License Constraints. IEEE International Conference on Cloud Computing, 2009.

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Deployment of Services in a Cloud Subject to Memory and License Constraints

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  1. Jim (Zhanwen) Li Carleton University John Chinneck Carleton University Marin Litoiu York University Murray Woodside Carleton University Deployment of Services in a Cloud Subject to Memory and License Constraints IEEE International Conference on Cloud Computing, 2009 Presented by: Yun Liaw

  2. Outline • Introduction & Related Works • Service Systems and Deployment • Heuristic Packing (HP) • Network Flow Model (NFM) • Computational Experiment • Conclusions and Comments

  3. Introduction (1/3) • Cloud support flexible deployment as an application’s need change, hide management details from the user and the service provider, and require payment only for resources used • An economic driver for clouds is the efficiencies achieved by sharing resources among deploying applications • Must be able to scale to thousands of services running on thousands of hosts • Be cheap enough to re-run frequently as load changes

  4. Introduction (2/3) • Deployment solution should be based on the execution demands of applications • The execution demand may be obtained from performance tests or from operating data(thus the change of demands can be tracked)

  5. Introduction (3/3) • In the previous works, deployment optimization algorithms have been proposed, but none of them have respected the memory constraints on the hosts and the license constraints on the applications • If the license constraint violates, additional cost would be reflected in the optimization cost function (soft constraint) • This paper is motivated by multi-dimensional bin-packing with 3 dimensions: • Memory • Execution demands (processor) • License • Also add NFM to minimize the cost

  6. Related Works • Deployment optimization: • Minimize the communications between the distributed hosts • Minimize the deployment changes on incremental deployment • LQN + NFM • Bin-Packing heuristics

  7. Service Systems and Deployment

  8. Service System Meta-model

  9. Attributes (1/2) • fUser,c: request rate of UserClassc • fUser,c,SLA : minimum required value of fUser,c • SUser,c : the set of services used by UserClasslassc • STask,t: the set of services provided by task t • Ycs : mean requests by UserClassc to Service s, during one response to a user request, including direct and indirect requests • ds : mean host demand of Service s, per invocation, in CPU-second • Mt : memory requirement of task t (assume the same for all hosts) • Lt : max license available for task t

  10. Attributes (2/2) • CLt: pay-per-use cost of extra licenses for task t, beyond Lt • Ch : execution cost of host h, a cost factor for a unit of execution on this host. In a homogeneous cloud these are all equal • Mh: memory capacity of host h • Ωh : the capacity limit of host h, given as a processing rate relative to a “standard” host. • Uh: utilization of host h = Ωh/saturationCapacity of h

  11. Example - Ycs • Ycs Example: YUser1,DB2Serv= 1 * 0.3 * (0.7+0.3 * 0.4) = 0.336

  12. Task demands : dt • At the required throughputs, the CPU demand dt, in CPU-seconds per second for task t (1)

  13. Example - dt • dt Example: • Assume ds = 1 except for task 4, which ds = 0.01 • If fUser1,c,SLA =20/sec fUser2,c,SLA=30/sec • dt1 = 50 • dt2 = 15 • dt3 = 4.5 • dt6 = 16.8

  14. Constraints (1/2) • Define αht be the part of dt which is executed by host h • , for all h(2) • , for all t(3) • The cost of execution per unit of time • (4) • Define Aht be the allocation variable s.t. • Aht =1 if αht> 0, Aht = 0 otherwise (5)

  15. Constraints (2/2) • Memory and License constraints • Memory : , for each h (6) • License : , for each t(7) • Cost of exceeded licenses • For a task t with , order the flows αht from largest to smallest • For the first Lt of these flows, set C*ht = Ch • For the remainder, set C*ht = (Ch+CLt) • (8)

  16. Node Utilization and Quality of Service • QoS is often stated in terms of response delay or throughput. However, these metrics are replaced here by constraints on host utilization • In equality , for all hstates that every host has a utilization less than the maximum utilization • In [*] it shows that if the host utilization is low enough, the contention will be moderate. • Response time will be limited to a low multiple of execution demands to satisfy the request C. Tang, et al., “A Scalable Application Placement Controller for Enterprise Data Centers,” in Proc. WWW, 2006

  17. Heuristic Packing (HP)

  18. Heuristic Packing – Intro • Bin-packing problem: • Given a set of items, each having a single dimension of its own “size” • The task is to pack all of the items into the smallest number of identical bins • In the proposed heuristic, the best-fit packing heuristic is used • Sort items from the largest to the smallest • Items are taken from the list in order and packed into the bin that leaves the smallest amount of unused space

  19. Heuristic Packing Formulation • The HP heuristic determines the allocation variables Ahtwith the goal to minimize the number of hosts usedsubject to the constraints mentioned previously • The packing process goes through a sequence of states in which tasks are partially allocated • : remaining memory space of host h • : remaining execution demand space of host h The available part of for allocation to a single task • : remaining execution demand of task t • Reservation factor: to prevent a host from being reserved for a single task

  20. Heuristic Packing – Algorithm (1/3) • Initialize: • ,, • Algorithm: • Sort tasks by Lt (# of license) in increasing order (break ties by sorting by CPU demands , largest first)

  21. Heuristic Packing – Algorithm (2/3) • For each task t in order • Sort the hosts that have greater than Mt, by their CPU execution demand space, largest first • If for the host h(1), then the remaining demand of task t will fit into one host • Find the best fit for , i.e., the host h with the smallest • Allocate all of to host h, that is to say:set , decrement by , set • Proceed to allocate the next task

  22. Heuristic Packing – Algorithm (3/3) • For each task t in order(cont’d) • Else if , for the first host, the remaining demand will not fit into one host • Allocate as much of as possible to h(1), that is to say:set , decrement by , and decrement by • Repeat step 2 for the same task t

  23. Effectiveness of HP • A scalable example conforming to the service was defined as follows: • A list of tasks, each with dt in [10,70] ms, Mt in [5,25], and Lt in [1,5] chosen randomly • A list of host processors, each with Ωh in [80,120], Mh in [50,100] Ch in [1,3] chosen randomly Note: The cost is not minimized in HP

  24. Network Flow Model (NFM)

  25. Network Flow Model (1/2) • A network flow model (NFM) is a graph with arcs which carry flows and nodes which operate on the flows • The flow into a host node is its total execution supply fHost,h. These flow are divided between hosted tasks, then the service entries, and finally the user classes • The input flow and output flow are balanced by NFM (total input = total output)

  26. Network Flow Model (2/2) • Arcs: represents the flow of execution demand/supply • If a task t is permitted to be deployed on h, there is an arc from h to t with the flow αht • Each arc is labeled with a triple of parameters [l,u,c] • l : the lower flow bound (default 0) • u : the upper flow bound (default infinite) • c : the cost per unit of flow (default 0) • The input flow fHost,hare labeled by [0,Ωh,Ch]

  27. Solve-NFM • To solve this NFM problem is a standard linear programming (LP) problem to minimize the COST* defined by (8), subject to the constraint : • , for all h(2) • , for all t(3) • However, NFM does not scale well. There are n*m arc flows to be determined by an LP with nm variables

  28. The Combined Algorithms

  29. The Combined Algorithms • Because HP alone does not minimize cost, thus it was combined with an NFM solution • HP-NFM: • Firstly run HP to prune host-to-task arcs • Then use NFM to determine the αht to minimize the COST* • In order to provide some freedom at NFM step, all demands are multiplied by a looseness factor > 1 at HP step • HP-NFM-HP • If the license constraint is failed by running HP-NFM, it is a final packing step succeeded in re-imposing the constraint • Two variant are provided, described later

  30. HP-NFM • A small number of processors is first found by HP • satisfying the memory constraint and • if possible the license constraint • Then the minimum-cost rates of processing the tasks are found by NFM • Looseness factor : set to be 1.1

  31. HP-NFM – Algorithm • Apply HP, after augmenting the all of the dt by the looseness factor 1.1 • Construct a pruned NFM with: • Nodes for the tasks, and for the hosts that have been used in Step 1 • The task demands dt without looseness factor • Arcs from each host to tasks allocated by HP, with cost C*ht on arc (h,t) • Apply Solve-NFM to minimize the COST*

  32. HP-NFM-HPx • The HP-NFM may spread the allocations out over more processors than necessary, due to the looseness factor • Thus a final HP step without looseness factor is introduced to reduce the number of processors • To counter under-utilized hosts, choose an idleness threshold for hosts. Hosts withare de-allocated, and repacked by HPagain • To counter license violations, tasks which exceed their limit had the excess replicas de-allocated • HP-NFM-HP1 : considers both 2 criteria • HP-NFM-HP2 : considers only license criterion

  33. HP-NFM-HPx – Algorithm (1/2) 1 – 3 executes algorithm HP-NFM • (In the HP1 variant only) Unpack under-utilized hosts: for each host h with • For each task t, increment by αht • Set =

  34. HP-NFM-HPx – Algorithm (2/2) • In both variants, unpack for license violations: For each task t which has • Sort the demand allocations αhtin increasing order • For the first Vt of these demands, increment both and by αht • Apply algorithm HP without looseness factor to repack the remaining execution of the tasks with > 0

  35. Computational Experience

  36. Control Parameter Settings • Maximum capacity: 80% of saturation capacity • License violation penalty : 10 * the number of license violations • Looseness factor : 1.1 • Reservation factor : 20% • Idleness threshold : 20% of the maximum capacity

  37. Results

  38. Looseness and Reservation Factor • Reducing reservation factor reduces cost • Increasing looseness factor only reduces the cost a little-> freedom it provide to NFM is not valuable

  39. Conclusions • This paper presents an algorithm for heuristic optimization of task deployments in the Clouds • Combining packing heuristic and linear programming • Considers CPU, memory and license constraints • The numerical experiments shows that HP-NFM-HPx is proffered • Less hosts are used • Lower economic cost and solution time

  40. Comments • The experiment is only at the computational level • QoS? • Does the resource demand grows linearly with the request rate? (need verify)

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