Design and evaluation of a scalable hierarchical application component placement algorithm for cloud resource allocation

In the context of cloud systems, mapping application components to a set of physical servers and assigning resources to those components is challenging. For large-scale clouds, traditional resource allocation systems, which rely on a centralized management paradigm, become ineffective and inefficient. Therefore, there is an essential need of providing new management solutions that scale well with the size of large cloud systems. In this paper a distributed and hierarchical component placement algorithm is presented, evaluated and compared to a centralized algorithm. Each application is represented as a collection of interacting services, and multiple service types with differing placement characteristics are considered. Our evaluations show that the proposed algorithm is at least 84.65 times faster and offers better scalability compared with a central approach, while the percentage of servers used and fully placed applications remains close to that of the centralized algorithm.

[1]  Lisandro Zambenedetti Granville,et al.  Data Center Network Virtualization: A Survey , 2013, IEEE Communications Surveys & Tutorials.

[2]  Rolf Stadler,et al.  Gossip-based resource management for cloud environments , 2010, 2010 International Conference on Network and Service Management.

[3]  Minghui Zhou,et al.  Self-adaptive resource management for large-scale shared clusters , 2010 .

[4]  Jordi Torres,et al.  Utility-based placement of dynamic Web applications with fairness goals , 2008, NOMS 2008 - 2008 IEEE Network Operations and Management Symposium.

[5]  Filip De Turck,et al.  Hierarchical network-aware placement of service oriented applications in Clouds , 2014, 2014 IEEE Network Operations and Management Symposium (NOMS).

[6]  Arnold L. Rosenberg,et al.  Application Placement on a Cluster of Servers , 2007, Int. J. Found. Comput. Sci..

[7]  Malgorzata Steinder,et al.  A scalable application placement controller for enterprise data centers , 2007, WWW '07.

[8]  Lisandro Zambenedetti Granville,et al.  Paradigm-based adaptive provisioning in virtualized data centers , 2013, 2013 IFIP/IEEE International Symposium on Integrated Network Management (IM 2013).

[9]  Xin-She Yang,et al.  Introduction to Algorithms , 2021, Nature-Inspired Optimization Algorithms.

[10]  Judith Kelner,et al.  Resource allocation for distributed cloud: concepts and research challenges , 2011, IEEE Network.

[11]  Sanjay Chaudhary,et al.  Policy based resource allocation in IaaS cloud , 2012, Future Gener. Comput. Syst..

[12]  Gargi Dasgupta,et al.  Server Workload Analysis for Power Minimization using Consolidation , 2009, USENIX Annual Technical Conference.

[13]  Rolf Stadler,et al.  Resource Management in Clouds: Survey and Research Challenges , 2015, Journal of Network and Systems Management.

[14]  Asser N. Tantawi,et al.  Dynamic Application Placement Under Service and Memory Constraints , 2005, WEA.

[15]  Filip De Turck,et al.  Algorithms for efficient data management of component-based applications in cloud environments , 2014, 2014 IEEE Network Operations and Management Symposium (NOMS).