Modern web services rely on Content Delivery Networks (CDNs) to efficiently deliver contents to end users. In order to minimize the experienced communication cost, it is necessary to send the end user's requests to the nearest servers. However, it is shown that this naive method causes some servers to get overloaded. Similarly, when distributing the requests to avoid overloading, the communication cost increases. This is a well-known trade-off between communication cost and load balancing in CDNs. In this work, by introducing a new meta-heuristic algorithm, we try to optimize this trade-off, that is, to have less-loaded servers at lower experienced communication cost. This trade-off is even better managed when we optimize the way servers update their information of each others' load. The proposed scheme, which is based on Honeybee algorithm, is an implementation of bees algorithm which is known for solving continuous optimization problems. Our proposed version for CDNs is a combination of a request redirecting method and a server information update algorithm. To evaluate the suggested method in a large-scale network, we leveraged our newly developed CDN simulator which takes into account all the important network parameters in the scope of our problem. The simulation results show that our proposed scheme achieves a better trade-off between the communication cost and load balancing in CDNs, compared to previously proposed schemes.
In @cite_6 , authors improved the CLB algorithm by adding a new parameter @math to control communication cost. Although the existing trade-off is not mentioned in this paper, the simulation results somehow admit the existence of such trade-off. Its results show that more constraints on communication cost lead to worse response time. Despite @cite_8 's assumption about limited cache size, the authors of @cite_6 consider every server is capable of responding to every request which is a simplified model of real CDNs.
@cite_6 @cite_8
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Load balancing plays an important role in improving scalability and stability in Content Delivery Networks (CDNs) to meet the increasing demand on bandwidth. This paper proposed a modified algorithm that takes into account the equilibrium between load balancing and redirection proximity. We extended a fluid queue model which is adopted in the existing literatures to the overall CDN system. In the system, scheduler selects proper replica server for each redistributed request by exploiting load differences between them. Furthermore, through limiting the migration distance for each request, the total costs mainly associated with delay are also effectively optimized. The simulation result indicates that the proposed algorithm can efficiently reduce redirection cost compared to Control-Law Balancing (CLB) algorithm at the expense of a bit of performance sacrifice of queue balancing. Besides, we found that the proposed mechanism has more benefit on queue balancing than CLB algorithm as well, when selecting an appropriate distance threshold. It is well known that load balancing and low delivery communication cost are two critical issues in mapping requests to servers in Content Delivery Networks (CDNs). However, the trade-off between these two performance metrics has not been yet quantitatively investigated in designing efficient request mapping schemes. In this work, we formalize this trade-off through a stochastic optimization problem. While the solutions to the problem in the extreme cases of minimum communication cost and optimum load balancing can be derived in closed form, finding the general solution is hard to derive. Thus we propose three heuristic mapping schemes and compare the trade-off performance of them through extensive simulations. Our simulation results show that at the expense of high query cost, we can achieve a good trade-off curve. Moreover, by benefiting from the power of multiple choices phenomenon, we can achieve almost the same performance with much less query cost. Finally, we can handle requests with different delay requirements at the cost of degrading network performance. It is becoming increasingly common to construct network services using redundant resources geographically distributed across the Internet. Content Distribution Networks are a prime example. Such systems distribute client requests to an appropriate server based on a variety of factors---e.g., server load, network proximity, cache locality---in an effort to reduce response time and increase the system capacity under load. This paper explores the design space of strategies employed to redirect requests, and defines a class of new algorithms that carefully balance load, locality, and proximity. We use large-scale detailed simulations to evaluate the various strategies. These simulations clearly demonstrate the effectiveness of our new algorithms, which yield a 60--91 improvement in system capacity when compared with the best published CDN technology, yet user-perceived response latency remains low and the system scales well with the number of servers. We consider load balancing in a network of caching servers delivering contents to end users. Randomized load balancing via the so-called power of two choices is a well-known approach in parallel and distributed systems. In this framework, we investigate the tension between storage resources, communication cost, and load balancing performance. To this end, we propose a randomized load balancing scheme which simultaneously considers cache size limitation and proximity in the server redirection process. In contrast to the classical power of two choices setup, since the memory limitation and the proximity constraint cause correlation in the server selection process, we may not benefit from the power of two choices. However, we prove that in certain regimes of problem parameters, our scheme results in the maximum load of order @math (here @math is the network size). This is an exponential improvement compared to the scheme which assigns each request to the nearest available replica. Interestingly, the extra communication cost incurred by our proposed scheme, compared to the nearest replica strategy, is small. Furthermore, our extensive simulations show that the trade-off trend does not depend on the network topology and library popularity profile details.