Cost-Minimizing Dynamic Migration of Content Distribution Services into Hybrid Clouds

Abstract

With the recent advent of cloud computing technologies, a growing number of content distribution applications are contemplating a switch to cloud-based services, for better scalability and lower cost. Two key tasks are involved for such a move: to migrate the contents to cloud storage, and to distribute the web service load to cloud-based web services. The main issue is to best utilize the cloud as well as the application provider's existing private cloud, to serve volatile requests with service response time guarantee at all times, while incurring the minimum operational cost. While it may not be too difficult to design a simple heuristic, proposing one with guaranteed cost optimality over a long run of the system constitutes an intimidating challenge. Employing Lyapunov optimization techniques, we design a dynamic control algorithm to optimally place contents and dispatch requests in a hybrid cloud infrastructure spanning geo-distributed data centers, which minimizes overall operational cost over time, subject to service response time constraints. Rigorous analysis shows that the algorithm nicely bounds the response times within the preset QoS target, and guarantees that the overall cost is within a small constant gap from the optimum achieved by a T-slot lookahead mechanism with known future information. We verify the performance of our dynamic algorithm with prototype-based evaluation.

Interconnection Contracts between Service and Content Provider with Partial Cloud Migration

“…In order to minimize costs and improve Quality of Service (QoS) and hence Quality of Experience (QoE) of their customers, content providers need to consider using resources of cloud providers. Cloud providers maintain large-scale data centres to offer storage and computational resources at a relatively low cost [9]− [11]. These providers enable different pricing plans, such as reservation, on-demand and spot market [12].…”

Elastic service scaling optimization in cloud-based communication systems

“…However, some researchers argue that the two problems have close inter-relation and they consider the two problem jointly as a newer approach for load balancing in CDNs [10,42,43]. In addition, the recent work [44] considers this joint problem in the cloud CDNs. A more complicated scenario which takes content eviction into consideration, together with the two aforementioned factors, is studied in [45].…”

“…Instead we assume that the redirection destinations have been fixed, and costs like the routing cost and computing cost function at each of these destinations are known. The joint problem of content placement and request redirection has been considered in [44] under various constraints. However, the proposed algorithm is an integer linear program that has a high complexity and is sensitive to variations in request traffic, i.e., a new optimization is required at each time step.…”

“…This cost corresponds to the power used by the VM when its service capacity is µ [114], and is adopted by [53]. In addition to the cost of power consumption, we can also include the cost of processor and storage memory by letting s i (µ) = k i µ a i + r i µ, where r i > 0, and we have assumed that memory or storage cost scales linearly with service capacity [44].…”

“…We discussed the request redirection problem in CCMN in Chapter 3 and the replica placement problem in CDN and cloud CDN has been studied in [44][45][46]54] as well.…”