Prof. Hanoch Levy

Today’s software development landscape has witnessed a shift towards microservices based architectures. Using this approach, large software systems are implemented by combining loosely-coupled services, each responsible for specific task and defined with separate scaling properties.
Auto-scaling is a primary capability of cloud computing which allows systems to adapt to fluctuating traffic loads by dynamically increasing (scale-up) and decreasing (scale-down) the number of resources used.

We observe that when microservices which utilize separate auto-scaling mechanisms operate in tandem to process traffic, they may perform ineffectively, especially under overload conditions, due to DDoS attacks. This can result in throttling (Denial of service — DoS) and over-provisioning of resources (Economic Denial of Sustainability — EDoS).

This paper demonstrates how an attacker can exploit the tandem behavior of microservices with different auto-scaling mechanisms to create an attack we denote as the \emph{Tandem Attack}. We demonstrate the attack on a typical \emph{Serverless} architecture and analyze its economical and performance damages. One intriguing finding is that some attacks may make a cloud customer paying for service denied requests.

We conclude that independent scaling of loosely coupled components might form an inherent difficulty and end-to-end controls might be needed.

Retry mechanisms are commonly used in microservices architectures as a mechanism for recovering from transit errors, including network failures and service overloading. This research aims at studying the operation of cloud retry mechanisms under deliberated DDoS attacks, and their effect on the application performance and operational costs. In this poster we focus on the economic aspect, and demonstrate that enabling such mechanisms improperly might be counter-productive and expose the system to substantial
and quadratic economical damage in the presence of attacks.

Performance analysis has been used for over half a century for the design and operations of computer systems and communications networks. This methodology, mostly stochastic in nature, has been based on the underlying paradigm that users are innocent and “well behaving”. Following the flourish of the internet and the subsequent rise of malicious incidents, research started investigating the effect of malicious attacks aimed at degrading system performance.

The objective of this work is to understand how system performance is affected by malicious behavior and how performance evaluation should account for it. We do so by considering an array of “classical” systems taken from the literature and examining their degree of vulnerability. These can also serve in providing some initial insights into what makes a system design vulnerable or resilient.

Performance analysis has been used for over half a century for the design and operations of computer systems and communications networks. This methodology, mostly stochastic in nature, has been based on the underlying paradigm that users are innocent and “well behaving”. Following the flourish of the internet and the subsequent rise of malicious incidents, research started investigating the effect of malicious attacks aimed at degrading system performance. The objective of this work is to understand how system performance is affected by malicious behavior and how performance evaluation should account for it. We do so by considering an array of “classical” systems taken from the literature and examining their degree of vulnerability. These can also serve in providing some initial insights into what makes a system design vulnerable or resilient.