ATSSC:An Attack Tolerant System in Serverless Computing

Serverless computing is a promising paradigm in cloud computing that greatly simplifies cloud programming.With serverless computing,developers only provide function code to serverless platform,and these functions are invoked by its driven events.Nonetheless,security threats in serverless computing such as vulnerability-based security threats have become the pain point hindering its wide adoption.The ideas in proactive defense such as redundancy,diversity and dynamic provide promising approaches to protect against cyberattacks.However,these security technologies are mostly applied to serverless platform based on“stacked”mode,as they are designed independent with serverless computing.The lack of security consideration in the initial design makes it especially challenging to achieve the all life cycle protection for serverless application with limited cost.In this paper,we present ATSSC,a proactive defense enabled attack tolerant serverless platform.ATSSC integrates the characteristic of redundancy,diversity and dynamic into serverless seamless to achieve high-level security and efficiency.Specifically,ATSSC constructs multiple diverse function replicas to process the driven events and performs cross-validation to verify the results.In order to create diverse function replicas,both software diversity and environment diversity are adopted.Furthermore,a dynamic function refresh strategy is proposed to keep the clean state of serverless functions.We implement ATSSC based on Kubernetes and Knative.Analysis and experimental results demonstrate that ATSSC can effectively protect serverless computing against cyberattacks with acceptable costs.

A Secure and Cost-Effective Training Framework Atop Serverless Computing for Object Detection in Blasting

The data analysis of blasting sites has always been the research goal of relevant researchers.The rise of mobile blasting robots has aroused many researchers’interest in machine learning methods for target detection in the field of blasting.Serverless Computing can provide a variety of computing services for people without hardware foundations and rich software development experience,which has aroused people’s interest in how to use it in the field ofmachine learning.In this paper,we design a distributedmachine learning training application based on the AWS Lambda platform.Based on data parallelism,the data aggregation and training synchronization in Function as a Service(FaaS)are effectively realized.It also encrypts the data set,effectively reducing the risk of data leakage.We rent a cloud server and a Lambda,and then we conduct experiments to evaluate our applications.Our results indicate the effectiveness,rapidity,and economy of distributed training on FaaS.

A joint function warm-up and request routing scheme for performing confident serverless computing

Serverless computing has become increasingly popular recently due to its cost efficiency and flexibility.However,running serverless computing functions in the cloud can incur high end-to-end service latency and operational costs.Running them on edge servers may significantly reduce service latency but is limited by computing power and memory availability.Given the limitations of cloud and edge environments for performing serverless com-puting,this paper proposes a joint function warm-up and request routing scheme to perform serverless computing functions on edge and cloud collaboratively.The key idea of the new scheme is to maximize the hit ratio of server-less computing requests,thereby reducing the cold-start latency that dominates the overall serving latency.This scheme explicitly considers allocating server memory and operation budget for executing concurrent requests during the scheduling.The proposed scheme has been evaluated through extensive simulations.Its effectiveness has been proved by comparison with the upper-bound results.

Result-as-a-Service (RaaS): Persistent Helper Functions in a Serverless Offering

Serverless Computing or Functions-as-a-Service (FaaS) is an execution model for cloud computing environments where the cloud provider executes a piece of code (a function) by dynamically allocating resources. When a function has not been executed for a long time or is being executed for the first time, a new container has to be created, and the execution environment has to be initialized resulting in a cold start. Cold start can result in a higher latency. We propose a new computing and execution model for cloud environments called Result-as-a-Service (RaaS), which aims to reduce the computational cost and overhead while achieving high availability. In between successive calls to a function, a persistent function can help in successive calls by precomputing the functions for different possible arguments and then distributing the results when a matching function call is found.

Implementation of serverless cloud GIS platform for land valuation

Cloud computing enables performing computations and analysis tasks and sharing services in web-based computer centres instead of local desktop systems.One of the most used areas of cloud computing is geographic information systems(GIS)applications.Although Desktop GIS products are still used in the community frequently,Web GIS and Cloud GIS applications have drawn attention and have become more efficient for users.In this study,a serverless Cloud GIS framework is implemented for the land valuation platform.In order to store,analyse,and share geospatial data,the Aurora Serverless PostgreSQL database is created on Amazon Web Services(AWS).While adopting Aurora Serverless PostgreSQL as a database management system,a simple point in polygon analysis conducted to compare the performances with Amazon Relational Database Service(RDS)instance.Results showed that the serverless database responded to the query faster and scaled up during high workload to decrease latency.Hence,parcel vector data,which conveys ownership information and land values attributes,is shared directly from the PostGIS database as vector tiles.Besides S3 and AWS Lambda services are used for storing and disseminating raster-based land value map tiles.To visualize all shared data and maps through a web browser,open source web mapping library Mapbox GL JS is used.

Machine learning and price-based load scheduling for an optimal IoT control in the smart and frugal home

We pose and study a scheduling problem for an electric load to develop an Internet of Things(IoT)control system for power appliances,which takes advantage of real-time dynamic energy pricing.Using historical pricing data from a large U.S.power supplier,we study and compare several dynamic scheduling policies,which can be implemented in a smart home to activate a major appliance(dishwasher,washing machine,clothes dryer)at an optimal time of the day,to minimize electricity costs.We formulate our scheduling task as a supervised machine learning classification problem which activates the load during one of two preferred time bins.The features used in the machine learning problem are hourly market,spot and day-ahead prices along with delayed label of the prior day.We find that boosting tree-based algorithms outperform any other classification approach with measurable reduction of energy costs over certain types of naive and static policies.We observe that the delayed label has most predictive power across features,followed,on average,by spot,hourly market,and day-ahead energy prices.We further discuss implementation issues using a micro controller system coupled with cloud-based serverless computing and dynamic data storage.Our test system includes an interactive voice interface via an intelligent personal assistant.