1. Dynamic and Decentralized Approaches for Optimal Allocation of Multiple Resources in Virtualized Data Centers Wei Chen, Samuel Hargrove, Heh Miao, Liang Hong College of Engineering, Technology & Computer Science Tennessee State University PDPTA 2011

2. Outline  Virtualized Data Center and Computation Resources  Problem Statement  Market Model and Control Objective  Decentralized Control Decisions Localized Optimal Decisions Heuristics and Global Optimization  Resource Allocation Algorithm  Simulation and Evaluation  Summary and Future Work PDPTA 2011

3. Virtualized Data Center and Computation Resources Virtualized Data Center model m servers n applications: Server j provides a virtual machine for each application Each virtual machine has a set of resources: CPU, bandwidth, disk, memory, … Request stream from each application arrive according to a random arrival process Requests are admitted into buffers via the network router Request streams from n applications Virtual Machines Buffers for admitted application requests PDPTA 2011

4. Problem Statement Challenges: Reduce the infrastructural and operational costs in the data centers while simultaneously increasing resource utilization to meet service requirements. Resources are dynamically shared and applications are unpredictably interacted across. Proposed Approaches: 1.A market based model that simplifies the control scheme and enable real- time control decision making based on each server's queue information. 2.A resource allocation scheme combines local optimization and heuristics for global optimization. 3.In order to avoid high complexity, a simple reinforcement learning method is used to achieve unknown optimal resource utility level. PDPTA 2011

5. Market Model and Control Objective Market Model Customers pay for the virtual machines: a specific set of resources at each virtual machine for the guaranteed throughput Profit of the data center = payments from customers – costs of used resources Profit at timeslot t in one server (hosting n applications) Profit from application i Total profit at timeslot t in the whole data center ( having m servers ) Profit from server j Control Objective Maximize Tporfit(t) at each timeslot t. PDPTA 2011

6. Decentralized Control Decisions (when should the resources be increased and decreased?) Localized Optimization Optimize, the profit at timeslot t from one application i using resource r at one server. PDPTA 2011

7. Decentralized Control Decisions (when should the resources be increased and decreased?) Theorem 1 When the performance is proportional to the resource, the function profit(t,i,r) is monotone increasing on in the penalty case and award case, and is a monotone decreasing on in the standard case. Control Decision at timeslot t: Calculate the real throughput (the number of the requests in the buffer at the beginning of timeslot t and at the end of the timeslot t) (1) Penalty case: if the real throughput is smaller than required throughput & there are enough requests in the buffer, increase the amount of resource r. (2) Award case (for supporting burst): if the real throughput is larger than the required throughput but it is smaller than the required throughput in the last T timeslots, increase the amount of resource r. (3) Others: reduce the amount of resource r. Localized Optimization – Continue Optimize, the profit at timeslot t from one application i using resource r at one server. PDPTA 2011

8. Decentralized Control Decisions (when should the resources be increased and decreased?) Heuristics and Global Optimization Heuristic I In order to avoid wasting the resource, if increasing resource r for application i didn’t raise the performance, the amount of r should be reduced in next timeslot so that the reduced resource can be used for other applications in the same server, or even for other servers. Action: Heuristic II If the buffer for application i in server p has fewer requests left, it indicates that p processed more requests. Therefore, it should admit more requests in the next timeslot. On the other side, in order to save the resources the server in sleep mode should keep sleep if possible Action: At every timeslot, the buffers for application i at the active servers admit the requests fully in increasing order of the buffer size; the inactive servers admit the remaining requests if any. PDPTA 2011

9. Resource Allocation Algorithm Using reinforcement learning to adaptively achieve the unknown optimal utility level Question: Though we know when the resources should be increased and decreased, but how much? PDPTA 2011 Resource r for application i at timeslot t = resource r for application i at timeslot t – 1 × estimated usage of resource r at timeslot t is the estimated usage adjusted as follows ( ):

10. Simulation and Evaluation PDPTA 2011 One of the experiments: power and bandwidth are both considered. 5 servers hosting 4 heterogeneous applications A1 through A4 with time-varying workloads. A1: each request gets 10KB files from the database and does encryption, and it uses 0.095% of the full power (240W). A2: each request gets/sends 80KB files from/to the database without doing encryption, and it uses 0.233% of the full power. A3 and A4: Web applications. A3 is an auction-web tier. Each request uses 0.53 % of the full power and 10.3KB bandwidth. A4 is an auction-database tier. Each request uses 0.2% of the full power and 5.2KB bandwidth. Required performance for each application is 400 requests per second. The bandwidths for the five application host servers, the web server and the database server are all set to 70MB. The arriving requests at the data center are scheduled as the following table. Number of Arriving Requests for Each Application in the Experiment 1 – 50s51 – 100 s101 – 150 s151 – 200s201 – 250 s251 – 300 s A1 300700500 250500750 A2 300500700500300750 A3 300300450600500750 A4 300500300500700750 Time Applications

11. Simulation and Evaluation Explanation: According to the request table, the workload is unbalanced and there are bursts. The left figure shows the performance of each application in all five servers that matches the numbers of arriving requests. For example, during 51-100 seconds, the number of arriving requests (700) for A1 is larger than the requested performance (400). However, since the average number of arriving requests is not larger than 400, all 700 requests are processed. In the last 50 second, the number of the arriving requests for each application is too large that it is beyond the power and bandwidth that each server can deal with. Therefore, only 675 requests on average for each application are processed instead of 750 arriving requests. On the other hand, during the first 50 seconds, only 300 requests arrive for each application. Therefore, the right figure shows that two servers are at sleep mode during this period to save power. PDPTA 2011

12. Summary and Future Work Summary  Dynamic and decentralized approaches for allocating multiple resources in virtualized data center that has time-varying workload and heterogeneous applications.  Tackled the problem with market based approaches that simplified the control scheme and enabled real-time control decision making.  Resource allocation scheme combines local optimization and heuristics for global optimization. Future Work More experiments in real data centers. PDPTA 2011