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.
Some other published algorithms like Joint Shortest Queue (JSQ) consider crowdedness of servers as the most critical decision-making parameter of redirecting. In a later work, Control-Law Load Balancing (CLB) algorithm @cite_7 improves JSQ's results. However, it still neglects the communication cost and assumes replica sets are close together.
@cite_7
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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. 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. We consider a multiclass multiplexer with support for multiple service classes and dedicated buffers for each service class. Under specific scheduling policies for sharing bandwidth among these classes, we seek the asymptotic (as the buffer size goes to infinity) tail of the buffer overflow probability for each dedicated buffer. We assume dependent arrival and service processes as is usually the case in models of bursty traffic. In the standard large deviations methodology, we provide a lower and a matching (up to first degree in the exponent) upper bound on the buffer overflow probabilities. We introduce a novel optimal control approach to address these problems. In particular, we relate the lower bound derivation to a deterministic optimal control problem, which we explicitly solve. Optimal state trajectories of the control problem correspond to typical congestion scenarios. We explicitly and in detail characterize the most likely modes of overflow. We specialize our results to the generalized processor sharing policy (GPS) and the generalized longest queue first policy (GLQF). The performance of strict priority policies is obtained as a corollary. We compare the GPS and GLQF policies and conclude that GLQF achieves smaller overflow probabilities than GPS for all arrival and service processes for which our analysis holds. Our results have important implications for traffic management of high-speed networks and can be used as a basis for an admission control mechanism which guarantees a different loss probability for each class. Efficient server selection algorithms reduce retrieval time for objects replicated on different servers and are an important component of Internet cache architectures. This paper empirically evaluates six client-side server selection algorithms. The study compares two statistical algorithms, one using median bandwidth and the other median latency, a dynamic probe algorithm, two hybrid algorithms, and random selection. The server pool includes a topologically dispersed set of United States state government Web servers. Experiments were run on three clients in different cities and on different regional networks. The study examines the effects of time-of day, client resources, and server proximity. Differences in performance highlight the degree of algorithm adaptability and the effect that network upgrades can have on statistical estimators. Dynamic network probing performs as well or better than the statistical bandwidth algorithm and the two probe bandwidth hybrid algorithms. The statistical latency algorithm is clearly worse, but does outperform random selection.