Model-based resource management for fine-grained services

The emergence of DevOps has changed the way modern distributed software systems are developed. Architectures decomposed in fine-grained services, such as microservices or function-as-a-service (FaaS), are now widespread across many organizations. From a resource management perspective, although the systems built with such architectures have many benefits, there are still research challenges that need further attention. In this study, we have focused on three such challenges, each concerning a specific system resource: compute, memory, or storage. Firstly, we focus on scaling the capacity of microservices at runtime. Here, the challenge is to design an autoscaler that can decide between vertical and horizontal scaling options to distribute the CPU capacity. Secondly, we focus on estimating the required capacity of an on-premises FaaS platform such that the service level agreements (SLAs) for function response times are satisfied. The challenge here is to address the cold start dilemma, i.e., that a cold start delays a function response but reduces the memory consumption. Thus, we must find a limit of cold starts such that the memory-consumption remains in-check while satisfying the SLAs. Finally, we focus on the storage management for distributed tracing targeted at microservices. The volume of such traces generated in a data center can be in the scale of tens of terabytes per day, but only a small fraction of these traces is useful for troubleshooting. The objective then is to sample only the useful traces. The key to addressing all these challenges is first, modeling the dynamics concerning the resources and subsequently, leveraging the model in a resource controller. To address the first challenge, we have developed an autoscaler ATOM that leverages layered queueing network (LQN) models to take its scaling decisions. Our experiment, with a real-life application, shows that ATOM produces 30-37% better results than the baseline autoscalers. For the second challenge, we have developed COCOA, a cold start aware capacity planner. COCOA utilizes M/M/k setup and LQN models to assess the cold start scenario and estimate the required capacity. We show with simulation that COCOA can reduce over-provisioning by over 70% compared to the availability aware approaches. Finally, addressing the third challenge, we propose SampleHST, a trace sampler that works under a storage budget constraint. SampleHST relies on either bag of words or graph-based models to represent a trace and groups similar traces using online clustering to perform sampling. We have evaluated the performance of SampleHST using data from both literature and production, which shows it produces 1.2x to 19x better results than the state-of-the-art.Open Acces

An Optimization of CDN Using Efficient Load Distribution and RADS Caching Algorithm

Nowadays, while large-sized multimedia objects are becoming very popular throughout the Internet, one of the important issues appears to be the acceleration of content delivery network (CDN) performance. CDN is a web system that delivers the web cached objects to the client and accelerates the web performance. Therefore the performance factor for any CDN is vital factor in determining the quality of services. The performance improvement can be achieved through load balancing technique, so the server load could be distributed to several clustered groups of machines and processed in parallel. Also the performance of CDN heavily depends on caching algorithm which is used to cache the web objects. This study investigates a method that improves the performance of delivering multimedia content through CDN while using RADS algorithm for caching large-sized objects separately from small-sized ones. We will also consider the efficient distribution of requests outgoing from local servers in order to balance the CDN load. This method uses various types of factors such as CPU processing time, I/O access time and Task Queue between nearby servers. At the end of the paper, we will see the experimental results derived from implementing the proposed optimization technique and observe how it could contribute to the effectiveness of CDN