Summary
Cloud providers are constantly seeking to become more cost effective, where a common strategy is to consolidate multiple applications in physical machines using virtualization techniques. This consolidation, however, may result in performance related problems such as resource interference. Moreover, if the workload is composed of multi‐tier applications, an increasingly popular method of application development, especially for web and mobile, in which tiers need to communicate through the network, we have another possible source of performance degradation, which we refer as network affinity. In order to reduce the effects of such problems, placement techniques are used to better distribute the applications in the physical machines. Several of these placement techniques consider resource interference or network affinity in order to decide the best placement, however, none of them apply both criteria at the same time. In our previous work, we identified that a combined approach could result in better solutions for this problem and proposed a set of placement policies that explore this tradeoff. In this paper, we propose placement algorithms based on these policies and evaluate the proposed solutions for different workload scenarios using a visual simulation tool we developed called CIAPA. CIAPA introduces a performance degradation model, a cost function, and heuristics to find a placement with the minimum cost for a specific workload of multi‐tier applications. In our preliminary experiments, we compared the solution generated by CIAPA with other placement strategies from related work, and have verified that, for the tested scenarios, it delivers placement decisions with better cost and, consequently, improved performance. We observed a reduction in response time of 10% when compared to interference strategies and up to 18% when considering only affinity strategies.
Cloud computing has transformed the means of computing in recent years with several benefits over traditional systems, like scalability and high availability. However, there are still some opportunities, especially in the area of resource provisioning and scaling [13]. Since workload may fluctuate a lot in certain environments, over-provisioning is a common practice to avoid abrupt Quality of Service (QoS) drops that may result in Service Level Agreement (SLA) violations, but at the price of an increase in provisioning costs and energy consumption. Workload prediction is one of the strategies by which efficiency and operational cost of a cloud can be improved [13]. Knowing demand in advance allows the previous allocation of sufficient resources to maintain QoS and avoid SLA violations [1]. This paper presents the advantages and disadvantages of three workload prediction techniques when applied in the context of cloud computing. Our preliminary results compare ARIMA, MLP, and GRU under different cloud configurations to help administrators choose the more appropriate and efficient predictive model for their specific problem.
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