Video streaming, whether on demand or live, has become one of the most popular internet applications. However, financial investments required for it is a severe problem since it needs more real time storage, higher data transfer and a significant amount of computation than other kinds of multimedia data. To tackle this problem, cloud computing, offering services without investing in hardware or software, emerges as a preferred technology. However, there are a large number of cloud service providers and they offer different pricing strategies for various applications in various regions. Therefore, it is of great importance for them that incoming service requests are assigned to the appropriate cloud services at minimum cost and provide maximum user satisfaction [quality of service (QoS) attributes]. Due to the issues, such as multiple cloud providers, different QoS requirements, different service level agreements and uncertainties in demand, price and availability, the optimization of resource allocation present further challenges. The objective of our study is to optimize the cost and performance of video on demand applications using cloud content delivery networks, storage and transcoders based on the QoS requirements of users. To solve the NP hard problem, Particle Swarm Optimization (PSO) technique is used due to the easiness in its concept and coding, less sensitive to the nature of the objective function, limited number of parameters and generating high quality solution within a short time. We propose a new method in which the optimum solution is affected not only by the best solution of the particle and global best solution but also by the best solution of the neighborhood particles in that iteration. This ternary approach is implemented into the well-known discrete and constrained PSO, achieving the minimum cost with user satisfaction for allocation of video requests to cloud resources. Although the proposed method yields better results in terms of accuracy, execution time of the algorithm is not reasonable. To overcome this inefficiency; ternary approach is embedded into multi-swarm PSO and it is parallelized and combined with greedy heuristic algorithms. The results of the comparison with the benchmarking algorithms show that our proposed method yields better results from the standpoint of both accuracy and execution time.

无论是点播还是直播视频流，都已成为最受欢迎的互联网应用程序之一。但是，由于与其他类型的多媒体数据相比，它需要更多的实时存储，更高的数据传输和大量的计算，因此它所需要的财务投资是一个严重的问题。为了解决这个问题，无需提供硬件或软件投资即可提供服务的云计算成为一种首选技术。但是，有大量的云服务提供商，它们为各个地区的各种应用提供不同的定价策略。因此，对于他们而言，至关重要的是将传入的服务请求以最小的成本分配给适当的云服务，并提供最大的用户满意度[服务质量（QoS）属性]。由于这些问题，例如多个云提供商，不同的QoS要求，不同的服务水平协议以及需求，价格和可用性的不确定性，资源分配的优化面临着进一步的挑战。我们研究的目的是基于用户的QoS要求，使用云内容交付网络，存储和代码转换器来优化视频点播应用程序的成本和性能。为了解决NP难题，由于其概念和编码容易，对目标函数的性质较不敏感，参数数量有限并在短时间内生成了高质量的解决方案，因此使用了粒子群优化（PSO）技术。我们提出了一种新方法，其中最优解不仅受粒子的最佳解和全局最佳解的影响，而且还受到该迭代中邻域粒子的最佳解的影响。这种三元方法被实施到众所周知的离散和受约束的PSO中，从而实现了将视频请求分配给云资源时用户满意的最低成本。尽管所提出的方法在准确性方面产生了更好的结果，但是该算法的执行时间并不合理。克服这种低效率；将三元方法嵌入到多群PSO中，并将其并行化并与贪婪启发式算法结合。与基准算法的比较结果表明，从准确性和执行时间的角度来看，我们提出的方法产生了更好的结果。