M. Bjorkqvist

Publications (2)0 Total impact

• Source

  Available from: 20.8

  Conference Proceeding: Minimizing Retrieval Cost of Multi-Layer Content Distribution Systems

  M. Bjorkqvist, L.Y. Chen, Xi Zhang
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  ABSTRACT: Content distribution systems, such as on-demand video services [6], file-sharing networks [8,1], and content clouds [2], provide ubiquitous data access and data sharing for large numbers of end-users. To efficiently provide content access across geographic locations, content storage nodes with limited capacity are conventionally organized in a multi-layer architecture to facilitate vertical as well as horizontal peer content retrievals, each of which may have different bandwidth constraints and transport costs. Content management, i.e., caching strategies, is deployed to efficiently utilize storage nodes and further reduce network retrieval traffic and cost. An optimal system design of such a multi-layer system needs to accommodate the trade-offs between vertical communication, peer communication, storage capacity and the users' retrieval traffic, driven by caching policies. In this paper, we propose a generic optimization framework based on steady-state content diffusion to minimize the total content retrieval cost in multi-layer content distribution systems, while considering the aforementioned trade-offs. The derived optimal content diffusion can evaluate the optimality of caching policies, and dimension the size of the system and the node caching capacity. Furthermore, we develop Peer Aware Content Caching (PACC) policies based on the derived optimal content diffusion. Our simulation results show that PACC effectively caches content and minimizes vertical and horizontal content retrieval costs under different system scenarios.
  Communications (ICC), 2011 IEEE International Conference on; 07/2011
• Conference Proceeding: Load-Balancing Dynamic Service Binding in Composition Execution Engines

  M. Bjorkqvist, L.Y. Chen, W. Binder
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  ABSTRACT: Performance and scalability of service-oriented applications, such as Web service compositions or business processes, depend on the dynamically bound services. In order to handle an increasing number of clients, load-balancing techniques are important. In this paper we assume the presence of multiple functionally equivalent services and explore different load-balancing algorithms to dynamically select service bindings with the goal to reduce average service response time. Using mathematical queueing models of service performance and simulation, we compare different service selection algorithms, including Static Lottery, Round-Robin, and Shortest-Queue. Furthermore, we propose linear and quadratic Dynamic Lottery service selection algorithms, which assign and periodically update service selection probabilities according to monitored average service response time. Our simulation environment models both stateless and stateful services and offers a wide range of service performance models with different degrees in the variation of service response time. While the Shortest-Queue algorithm performs best in simulation settings with only stateless services or low variance of service response time, the Round-Robin and Dynamic Lottery algorithms work best in settings with stateful services and high variance of service performance.
  Services Computing Conference (APSCC), 2010 IEEE Asia-Pacific; 01/2011