Date of Award

8-2011

Document Type

Campus Access Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Computer Science

First Advisor

Junichi Suzuki

Second Advisor

Bill Campbell

Third Advisor

Marc Pomplun

Abstract

Large-scale network systems, such as Internet data centers, grid/cloud computing systems, are increasingly expected to be autonomous, scalable, adaptive to dynamic network environments, survivable against partial system failures and simple to implement and maintain. Based on the observation that various biological systems have overcome these requirements, the proposed architecture, SymbioticSphere, applies biological principles and mechanisms to design network systems (i.e., application services and middleware platforms). SymbioticSphere follows key biological principles such as decentralization, evolution, emergence, diversity and symbiosis. Each application service and middleware platform is modeled as a biological entity, analogous to an individual bee in a bee colony, and implements biological mechanisms such as energy exchange, migration, replication, reproduction and death. Each service/platform possesses behavior policies, as genes, each of which defines when to and how to invoke a particular behavior. SymbioticSphere allows services and platforms to autonomously adapt to dynamic network conditions by optimizing their behavior policies with a genetic algorithm. Through the evolutionary process, services and platforms also strive to satisfy given constraints for quality of service (QoS) such as response time, throughput and workload distribution. SymbioticSphere also allows services and platforms to autonomously seek stable adaptation decisions as equilibria between them and yield stable performance results that contain a very limited amount of fluctuations. This dissertation describes the design of SymbioticSphere and evaluates how the biologically-inspired mechanisms in SymbioticSphere impact the autonomy, adaptability, scalability, survivability, and simplicity of network systems.

Comments

Free and open access to this Campus Access Dissertation is made available to the UMass Boston community by ScholarWorks at UMass Boston. Those not on campus and those without a UMass Boston campus username and password may gain access to this dissertation through resources like Proquest Dissertations & Theses Global or through Interlibrary Loan. If you have a UMass Boston campus username and password and would like to download this work from off-campus, click on the "Off-Campus UMass Boston Users" link above.

Download
Off-Campus UMass Boston Users

Share

COinS