Dynamic Scaling of VoD Services into Hybrid Clouds with Cost Minimization and QoS Guarantee

A large-scale video-on-demand (VoD) service demands huge server costs, to provision thousands of videos to millions of users with high streaming quality. As compared to the traditional practice of relying on large on-premise server clusters, the emerging platforms of geo-distributed public clouds promise a more economic solution: their on-demand resource provisioning can constitute ideal supplements of resources from on-premise servers, and effectively support dynamic scaling of the VoD service at different times. Promising though it is, significant technical challenges persist before it turns into reality: how shall the service provider dynamically replicate videos and dispatch user requests over the hybrid platform, such that the service quality and the minimization of overall cost can be guaranteed over the long run of the system? In this paper, we present a dynamic algorithm that optimally makes decisions on video replication and user request dispatching in a hybrid cloud of on-premise servers and geo-distributed cloud data centers, based on the Lyapunov optimization framework. We rigorously prove that this algorithm can nicely bound the streaming delays within the preset QoS target in cases of arbitrary request arrival patterns, and guarantee that the overall cost is within a small constant gap from the optimum achieved by a T-slot lookahead mechanism with known information into the future. We evaluate our algorithm with extensive simulations under realistic settings, and demonstrate that cost minimization and smooth playback can be achieved in cases of volatile user demands.published_or_final_versio

A Latency-driven Availability Assessment for Multi-Tenant Service Chains

Nowadays, most telecommunication services adhere to the Service Function Chain (SFC) paradigm, where network functions are implemented via software. In particular, container virtualization is becoming a popular approach to deploy network functions and to enable resource slicing among several tenants. The resulting infrastructure is a complex system composed by a huge amount of containers implementing different SFC functionalities, along with different tenants sharing the same chain. The complexity of such a scenario lead us to evaluate two critical metrics: the steady-state availability (the probability that a system is functioning in long runs) and the latency (the time between a service request and the pertinent response). Consequently, we propose a latency-driven availability assessment for multi-tenant service chains implemented via Containerized Network Functions (CNFs). We adopt a multi-state system to model single CNFs and the queueing formalism to characterize the service latency. To efficiently compute the availability, we develop a modified version of the Multidimensional Universal Generating Function (MUGF) technique. Finally, we solve an optimization problem to minimize the SFC cost under an availability constraint. As a relevant example of SFC, we consider a containerized version of IP Multimedia Subsystem, whose parameters have been estimated through fault injection techniques and load tests