Cloud Service Providers deliver their products in form of ”as-a-Service”, which are typically categorized by the level of abstraction. This approach hides the implementation details and shows only functionality to the user. However, the problem is that it is hard to measure the performance of Cloud services, because they behave like black boxes. Especially with Function-as-a-Service it is even more difficult because it completely hides server and infrastructure management from users by design. Cloud Service Prodivers usually restrict the maximum size of code, memory and runtime of Cloud Functions. Nevertheless, users need clarification if more ressources are needed to deliver services in high quality. In this regard, we present the architectural design of a new Function-as-a-Service benchmarking tool, which allows users to evaluate the performance of Cloud Functions. Furthermore, the capabilities of the framework are tested on an isolated platform with a specific workload. The results show that users are able to get insights into Function-as-a-Service environments. This, in turn, allows users to identify factors which may slow down or speed up the performance of Cloud Functions.
@cite_9 analyze several resource-intensive tasks in terms of comparing FaaS models with conventional monolithic algorithms. The authors conduct several experiments and compare the performance and other resource-related characteristics. The results demonstrate that solutions for scientific and high-performance computing can be realized by Cloud Functions. The authors mainly focus on computing intensive tasks (e.g. face detection, calculation of @math , etc.) in the domain of scientific and high-performance computing but they do not take other FaaS qualities of interest into account. For example, FaaS environments offer a timeout parameter, which specifies the maximum time for code execution. After this time, any code execution stops and the FaaS platform returns an error status. A timeout might become crucial when it comes to Cloud Function pricing since CSC is charged for the entire time that the Cloud Function executes. Therefore, a lack of precision can lead to early and misleading timeouts during code execution, which, in turn, can end in uncompleted tasks and high costs.
@cite_9
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The adoption of cloud computing facilities and programming models differs vastly between different application domains. Scalable web applications, low-latency mobile backends and on-demand provisioned databases are typical cases for which cloud services on the platform or infrastructure level exist and are convincing when considering technical and economical arguments. Applications with specific processing demands, including high-performance computing, high-throughput computing and certain flavours of scientific computing, have historically required special configurations such as compute- or memory-optimised virtual machine instances. With the rise of function-level compute instances through Function-as-a-Service (FaaS) models, the fitness of generic configurations needs to be re-evaluated for these applications. We analyse several demanding computing tasks with regards to how FaaS models compare against conventional monolithic algorithm execution. Beside the comparison, we contribute a refined FaaSification process for legacy software and provide a roadmap for future work. We consider a market-based resource allocation model for batch jobs in cloud computing clusters. In our model, we incorporate the importance of the due date of a job rather than the number of servers allocated to it at any given time. Each batch job is characterized by the work volume of total computing units (e.g., CPU hours) along with a bound on maximum degree of parallelism. Users specify, along with these job characteristics, their desired due date and a value for finishing the job by its deadline. Given this specification, the primary goal is to determine the scheduling of cloud computing instances under capacity constraints in order to maximize the social welfare (i.e., sum of values gained by allocated users). Our main result is a new ( C (C-k) ⋅ s (s-1))-approximation algorithm for this objective, where C denotes cloud capacity, k is the maximal bound on parallelized execution (in practical settings, k l C) and s is the slackness on the job completion time i.e., the minimal ratio between a specified deadline and the earliest finish time of a job. Our algorithm is based on utilizing dual fitting arguments over a strengthened linear program to the problem. Based on the new approximation algorithm, we construct truthful allocation and pricing mechanisms, in which reporting the job true value and properties (deadline, work volume and the parallelism bound) is a dominant strategy for all users. To that end, we provide a general framework for transforming allocation algorithms into truthful mechanisms in domains of single-value and multi-properties. We then show that the basic mechanism can be extended under proper Bayesian assumptions to the objective of maximizing revenues, which is important for public clouds. We empirically evaluate the benefits of our approach through simulations on data-center job traces, and show that the revenues obtained under our mechanism are comparable with an ideal fixed-price mechanism, which sets an on-demand price using oracle knowledge of users' valuations. Finally, we discuss how our model can be extended to accommodate uncertainties in job work volumes, which is a practical challenge in cloud settings. The Function as a Service (FaaS) subtype of serverless computing provides the means for abstracting away from servers on which developed software is meant to be executed. It essentially offers an event-driven and scalable environment in which billing is based on the invocation of functions and not on the provisioning of resources. This makes it very attractive for many classes of applications with bursty workload. However, the terms under which FaaS services are structured and offered to consumers uses mechanisms like GB–seconds (that is, X GigaBytes of memory used for Y seconds of execution) that differ from the usual models for compute resources in cloud computing. Aiming to clarify these terms, in this work we develop a microbenchmark that we use to evaluate the performance and cost model of popular FaaS solutions using well known algorithmic tasks. The results of this process show a field still very much under development, and justify the need for further extensive benchmarking of these services. Testing and executing large-scale computational applications in public clouds is becoming prevalent due to cost saving, elasticity, and scalability. However, how to increase the reliability and reduce the cost to run large-scale applications in public clouds is still a big challenge. In this paper, we analyzed the pricing schemes of Amazon Elastic Compute Cloud (EC2) and found the disturbance effect that the price of the spot instances can be heavily affected due to the large number of spot instances required. We proposed a dynamic approach which schedules and runs large-scale computational applications on a dynamic pool of cloud computational instances. We use hybrid instances, including both on-demand instances for high priority tasks and backup, and spot instances for normal computational tasks so as to further reduce the cost without significantly increasing the completion time. Our proposed method takes the dynamic pricing of cloud instances into consideration, and it reduces the cost and tolerates the failures for running large-scale applications in public clouds. We conducted experimental tests and an agent based Scalable complex System modeling for Sustainable city (S3) application is used to evaluate the scalability, reliability and cost saving. The results show that our proposed method is robust and highly flexible for researchers and users to further reduce cost in real practice.