Singular valued differential link count linear estimator for traffic matrix of large cloud computing networks

Today cloud computing dominates the range of services one can obtain from the Internet including Software as a service (SAAS), Platform as a service (PAAS) and Infrastructure as a Service (IAAS). These services increasingly provide ease to end user by providing a large number of applications. However, this versatility in services and applications make network planning, administration, management, security and assurances of service quality even more difficult. Hence, the significance of network traffic estimation from perspective of network planning and analysis has now become undeniable. The traffic estimation problem is often modeled as a system of linear equations Y=AX where the unobservable Origin Destination (OD) Traffic Matrix X has to be estimated from the observable Link Counts Matrix Y and Routing matrix A. However, this system of equations is under-constrained leading to difficulty in estimation of traffic matrix without knowledge of a prior estimate as a starting point. Recent research literature discusses several traffic matrix estimation techniques involving adoption of optimization approaches to satisfy hard space based constraints of the problem Y=AX which is computationally intensive, considerably expensive for real time traffic estimation. This paper proposes a novel light weight linear estimator for OD Traffic Matrix of large cloud computing platform suitable for real time prediction. Our contribution in this work is a novel algorithm which tries to incorporate these hard spaced based constraints into softer form which can be embedded into a fast, light weight linear estimator for dynamic estimation of OD Traffic Matrix.