Building Self-Configuring Data Centers with Cross Layer Coevolution
                                             Paskorn C...
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Building Self-Configuration Data Centers with Cross Layer Co-Evolution


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SymbioticSphere Project II

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Building Self-Configuration Data Centers with Cross Layer Co-Evolution

  1. 1. Building Self-Configuring Data Centers with Cross Layer Coevolution Paskorn Champrasert and Junichi Suzuki University of Massachusetts, Boston Objectives Behavior Policy Evolutionary Process • Make data centers (application services and middleware platforms) more Each agent/platform has its own policy for each behavior. • SymbioticSphere allows agents and autonomous, scalable, adaptable and survival to platforms to autonomously find • A behavior policy • Improve user experience appropriate values in an evolutionary • defines when to and how to invoke a particular behavior. • Expand system’s operational longevity manner, thereby adapting themselves • consists of factors (Fi), which evaluate environment conditions. • Reduce maintenance cost to network conditions. Both regular and • Each factor is given a weight value (Wi) relative to its importance. symbiotic behavior policies are encoded • Apply biological concepts and mechanisms as genes of agents and platforms. • Various biological systems have achieved these requirements. • A behavior is invoked if the weighted sum of its factor values exceeds a threshold. • Each gene contains one or more weight SymbioticSphere Factor ( F1) w1 values and a threshold value for a • Each application service and platform is designed as a biological entity, analogous w2 ∑ F W > Threshold? i i i particular behavior. Factor ( F2) to an individual bee in a bee colony. . Threshold Invoke behavior or not Internet Data Center Simulations . w3 •A simulated network system is modeled as Agents: Factor ( Fn) . User an Internet data center. Host access point •7x7 grid network topology. service requests • Application service is implemented as an autonomous and distributed software agent. Symbiotic Behaviors • 49 network hosts For example, an agent may implement a web service and contain web pages in •Each agent implements a web service in its body. • Each symbiotic behavior is defined as a sequence of regular behaviors that its body an agent and its underlying platform perform in order. (Simulated User) •There is one agent and one platform on Platforms: • There are two types of symbiotic behaviors: agent-initiated symbiotic each host at the beginning of simulation. Data Center • A platform runs on a network host and operates agents. behaviors (A1, A2 and A3 behaviors) and platform-initiated symbiotic • 49 agents and 49 platforms behaviors (P1, P2 and P3 behaviors) Energy Exchange 100 00 Service Request Rate Input: (# of requests / min) 80 00 • Agents and platforms store and expend This service request rate is taken 60 00 energy for living. from a workload trace of the 1998 40 00 Winter Olympic official website 20 00 • Agents gain energy in exchange for 0 performing their services to human 0 2 4 6 8 10 12 14 16 18 20 22 24 users, and expend energy to use Simulation time (hour) network and computing resources. Performance Ratio • Platforms gain energy in exchange Performance ratio is measured with seven performance metrics (response time, for providing resources to agents, throughput, Load Balancing Index, resource efficiency, platform resource availability, and evaporates energy to the agent energy level and platform energy level). network environment. PGi denotes the performance in the metric i when agents and platforms obtain their Regular Behaviors: behavior policies through evolution. Pi denotes the performance in the metric i when • Each agent and platform autonomously senses its surrounding environment agents and platforms use manually-configured behavior policies. conditions and adaptively invokes a behavior suitable for the conditions. 7 ⎛ PGi − Pi ⎞ For example, an agent may invoke the migration behavior to move toward a Performance Ratio = ∑ ⎜ ⎟ (7) For example: A1 i=1 ⎝ Pi ⎠ network host that receives a large number of user requests for its services. Conditions: Results Simulation Scenarios An agent wants to move toward a user but there is no platform running on a neighboring host closer to the user. 1 R vs RG R+S vs RG+SG R: Regular Behaviors Agents’ Regular Behaviors The agent may propose the local platform to replicate itself on the without evolutionary Process Performance ratio • Replication 0.5 neighboring host. 0 RG: Regular Behaviors • Reproduction If the local platform’s resource availability is low, the platform accepts the -0.5 with evolutionary Process • Migration agent’s proposal. -1 R+S: Regular + Symbiotic Behaviors • Death Actions: -1.5 without evolutionary Process The agent gives the platform the energy units of platform replication cost, -2 Platforms’ Regular Behaviors 1 2 3 4 5 6 7 8 9 10 RG+SG: Regular + Symbiotic Behaviors and the platform replicates itself on a host that the agent wants to migrate to. • Replication Simulation Time (day) with evolutionary Process Results: • Reproduction The agent can migrate to the child platform and improve response time. This result demonstrates that agents and platforms can successfully • Death The platform can improve its health level because resource availability improve the quality of their behavior policies by themselves. becomes higher.