In Function as a Service (FaaS), a serverless computing variant, customers deploy functions instead of complete virtual machines or Linux containers. It is the cloud provider who maintains the runtime environment for these functions. FaaS products are offered by all major cloud providers (e.g. Amazon Lambda, Google Cloud Functions, Azure Functions); as well as standalone open-source software (e.g. Apache OpenWhisk) with their commercial variants (e.g. Adobe I/O Runtime or IBM Cloud Functions). We take the bottom-up perspective of a single node in a FaaS cluster. We assume that all the execution environments for a set of functions assigned to this node have been already installed. Our goal is to schedule individual invocations of functions, passed by a load balancer, to minimize performance metrics related to response time. Deployed functions are usually executed repeatedly in response to multiple invocations made by end-users. Thus, our scheduling decisions are based on the information gathered locally: the recorded call frequencies and execution times. We propose a number of heuristics, and we also adapt some theoretically-grounded ones like SEPT or SERPT. Our simulations use a recently-published Azure Functions Trace. We show that, compared to the baseline FIFO or round-robin, our data-driven scheduling decisions significantly improve the performance.
The serverless scheduling problem poses a new challenge to Cloud service platform providers because it is rather a job scheduling problem than a traditional resource allocation or request load balancing problem. Traditionally, elastic cloud applications use managed virtual resource allocation and employ request load balancers to orchestrate the deployment. With serverless, the provider needs to solve both the load balancing and the allocation. This work reviews the current Apache OpenWhisk serverless event load balancing and a noncooperative game-theoretic load balancing approach for response …
Serverless computing has seen rapid growth due to the ease-of-use and cost-efficiency it provides. However, function scheduling, a critical component of serverless systems, has been overlooked. In this paper, we take a first-principles approach toward designing a scheduler that caters to the unique characteristics of serverless functions as seen in real-world deployments. We first create a taxonomy of scheduling policies along three dimensions. Next, we use simulation to explore the scheduling policy space for the function characteristics in a 14-day trace of Azure functions and conclude that …
Python-written data analytics applications can be modeled as and compiled into a directed acyclic graph (DAG) based workflow, where the nodes are fine-grained tasks and the edges are task dependencies. Such analytics workflow jobs are increasingly characterized by short, fine-grained tasks with large fan-outs. These characteristics make them well-suited for a new cloud computing model called serverless computing or Function-as-a-Service (FaaS), which has become prevalent in recent years. The auto-scaling property of serverless computing platforms accommodates short tasks and bursty workloads, …
Serverless computing is an emerging Cloud service model. It is currently gaining momentum as the next step in the evolution of hosted computing from capacitated machine virtualisation and microservices towards utility computing. The term "serverless" has become a synonym for the entirely resource-transparent deployment model of cloud-based event-driven distributed applications. This work investigates how adaptive event dispatching can improve serverless platform resource efficiency and contributes a novel approach that allows for better scaling and fitting of the platform's resource consumptio…
Serverless computing is increasingly being used for parallel computing, which have traditionally been implemented as stateful applications. Executing complex, burst-parallel, directed acyclic graph (DAG) jobs poses a major challenge for serverless execution frameworks, which will need to rapidly scale and schedule tasks at high throughput, while minimizing data movement across tasks. We demonstrate that, for serverless parallel computations, decentralized scheduling enables scheduling to be distributed across Lambda executors that can schedule tasks in parallel, and brings multiple benefits, i…
Serverless computing is an emerging service model in distributed computing systems. The term captures cloud-based event-driven distributed application design and stems from its completely resource-transparent deployment model, i.e. serverless. This work thesisizes that adaptive event dispatching can improve current serverless platform resource efficiency by considering locality and dependencies. These design contemplations have also been formulated by Hendrickson et al., which identifies the requirement that "Serverless load balancers must make low-latency decisions while considering session, …
We present a framework for scheduling multifunction serverless applications over a hybrid public-private cloud. A set of serverless jobs is input as a batch, and the objective is to schedule function executions over the hybrid platform to minimize the cost of public cloud use, while completing all jobs by a specified deadline. As this scheduling problem is NP-Hard, we propose a greedy algorithm that dynamically determines both the order and placement of each function execution using predictive models of function execution time and network latencies. We present a prototype implementation of our…
The Internet is responsible for accelerating growth in several fields such as digital media, healthcare, the military. Furthermore, the Internet was founded on the principle of allowing clients to communicating with servers. However, serverless computing is one such field that tries to break free from this paradigm. Event-driven compute services allow users to build more agile applications using capacity provisioning and a pay-for-value billing model. This paper provides a formal account of the research contributions in the field of Serverless computing.
The development of cloud infrastructures inspires the emergence of cloud-native computing. As the most promising architecture for deploying microservices, serverless computing has recently attracted more and more attention in both industry and academia. Due to its inherent scalability and flexibility, serverless computing becomes attractive and more pervasive for ever-growing Internet services. Despite the momentum in the cloud-native community, the existing challenges and compromises still wait for more advanced research and solutions to further explore the potentials of the serverless comput…
Serverless computing has emerged as an attractive deployment option for cloud applications in recent times. The unique features of this computing model include, rapid auto-scaling, strong isolation, fine-grained billing options and access to a massive service ecosystem which autonomously handles resource management decisions. This model is increasingly being explored for deployments in geographically distributed edge and fog computing networks as well, due to these characteristics. Effective management of computing resources has always gained a lot of attention among researchers. The need to a…
Serverless computing has become a major trend among cloud providers. With serverless computing, developers fully delegate the task of managing the servers, dynamically allocating the required resources, as well as handling availability and fault-tolerance matters to the cloud provider. In doing so, developers can solely focus on the application logic of their software, which is then deployed and completely managed in the cloud. Despite its increasing popularity, not much is known regarding the actual system performance achievable on the currently available serverless platforms. Specifically, i…
Serverless computing is a widely adopted cloud execution model composed of Function-as-a-Service (FaaS) and Backend-as-a-Service (BaaS) offerings. The increased level of abstraction makes vendor lock-in inherent to serverless computing, raising more concerns than previous cloud paradigms. Multi-cloud serverless is a promising emerging approach against vendor lock-in, yet multiple challenges must be overcome to tap its potential. First, we need to be aware of both the performance and cost of each FaaS provider. Second, a multi-cloud architecture must be proposed before deploying a multi-cloud w…
This report describes 1) how we use Intel's Optane DCPMM in the memory Mode. We investigate the the scalability of applications on a single Optane machine, using Subgraph counting as memory-intensive graph problem. We test with various input graph and subtemplate sizes to determine its performance for different memory and CPU loads, as well as a comparison of performance on a single node Optane with a distributed set of nodes in a cluster using MPI. 2) We investigate the end-to-end execution delays in serverless computing and study concurrent function executions with cold starts. In future wor…
Serverless computing offers an event driven pay-as-you-go framework for application development. A key selling point is the concept of no back-end server management, allowing developers to focus on application functionality. This is achieved through severe abstraction of the underlying architecture the functions run on. We examine the underlying architecture and report on the performance of serverless functions and how they are effected by certain factors such as memory allocation and interference caused by load induced by other users on the platform. Specifically, we focus on the serverless o…
Serverless computing has recently experienced significant adoption by several applications, especially Internet of Things (IoT) applications. In serverless computing, rather than deploying and managing dedicated virtual machines, users are able to deploy individual functions, and pay only for the time that their code is actually executing. However, since serverless platforms are relatively new, they have a completely different pricing model that depends on the memory, duration, and the number of executions of a sequence/workflow of functions. In this paper we present an algorithm that optimize…
Wireless sensor network (WSN) has been developed for decades and have performed well in the performance, power consumption, and congestion control. However, the following problems have not been addressed, such as inaccurate cost estimation of device's lifecycle, highly-coupled engineering development, and low utilization of hardware and software resources during the life cycle of WSN. Therefore, we first propose the conceptual view of maintainability implementation for WSN based on Serverless Computing. The maintainability implementation refers to the ability to meet the WSN product to consume…
Function-as-a-Service (FaaS) is one of the most promising directions for the future of cloud services, and serverless functions have immediately become a new middleware for building scalable and cost-efficient microservices and applications. However, the quickly moving technology hinders reproducibility, and the lack of a standardized benchmarking suite leads to ad-hoc solutions and microbenchmarks being used in serverless research, further complicating metaanalysis and comparison of research solutions. To address this challenge, we propose the Serverless Benchmark Suite: the first benchmark f…
Cloud-based serverless computing is an increasingly popular computing paradigm. In this paradigm, different services have diverse computing requirements that justify deploying an inconsistently Heterogeneous Computing (HC) system to efficiently process them. In an inconsistently HC system, each task needed for a given service, potentially exhibits different execution times on each type of machine. An ideal resource allocation system must be aware of such uncertainties in execution times and be robust against them, so that Quality of Service (QoS) requirements of users are met. This research ai…
The exponential growth of Internet of Things (IoT) has given rise to a new wave of edge computing due to the need to process data on the edge, closer to where it is being produced and attempting to move away from a cloud-centric architecture. This provides its own opportunity to decrease latency and address data privacy concerns along with the ability to reduce public cloud costs. The serverless computing model provides a potential solution with its event-driven architecture to reduce the need for ever-running servers and convert the backend services to an as-used model. This model is an attra…
Serverless computing has become very popular today since it largely simplifies cloud programming. Developers do not need to longer worry about provisioning or operating servers, and they pay only for the compute resources used when their code is run. This new cloud paradigm suits well for many applications, and researchers have already begun investigating the feasibility of serverless computing for data analytics. Unfortunately, today's serverless computing presents important limitations that make it really difficult to support all sorts of analytics workloads. This paper first starts by analy…
Serverless computing is an excellent fit for big data processing because it can scale quickly and cheaply to thousands of parallel functions. Existing serverless platforms isolate functions in ephemeral, stateless containers, preventing them from directly sharing memory. This forces users to duplicate and serialise data repeatedly, adding unnecessary performance and resource costs. We believe that a new lightweight isolation approach is needed, which supports sharing memory directly between functions and reduces resource overheads. We introduce Faaslets, a new isolation abstraction for high-pe…
Developing accurate and extendable performance models for serverless platforms, aka Function-as-a-Service (FaaS) platforms, is a very challenging task. Also, implementation and experimentation on real serverless platforms is both costly and time-consuming. However, at the moment, there is no comprehensive simulation tool or framework to be used instead of the real platform. As a result, in this paper, we fill this gap by proposing a simulation platform, called SimFaaS, which assists serverless application developers to develop optimized Function-as-a-Service applications in terms of cost and p…
Serverless computing provides fine-grain resource sharing between Cloud tenants through containers. Each function invocation (action) runs in an individual container. When there is not an already started container for a user function, a new container has to be created for it. However, the long cold startup time of a container results in the long response latency of the action. Our investigation shows that the containers for some user actions share most of the software packages. If an action that requires a new container can ``borrow'' a similar warm container from other actions, the long cold …
