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Cloud computing has become crucial for the commercial world due to its computational capacity, storage capabilities, scalability, software integration, and billing convenience. Initially, clouds were relatively homogeneous, but now diverse machine configurations in heterogeneous clouds are recognized for their improved application performance and energy efficiency. This shift is driven by the integration of various hardware to accommodate diverse user applications. However, alongside these advancements, security threats like micro-architectural attacks are increasing concerns for cloud providers and users. Studies like Repttack and Cloak & Co-locate highlight the vulnerability of heterogeneous clouds to co-location attacks, where attacker and victim instances are placed together. The ease of these attacks isn’t solely linked to heterogeneity but also correlates with how heterogeneous the target systems are. Despite this, no numerical metrics exist to quantify cloud heterogeneity. This article introduces the Heterogeneity Score (HeteroScore) to evaluate server setups and instances. HeteroScore significantly correlates with co-location attack security. The article also proposes strategies to balance diversity and security. This study pioneers the quantitative analysis connecting cloud heterogeneity and infrastructure security. 

In recent decades, due to the emerging requirements of computation acceleration, cloud FPGAs have become popular in public clouds. Major cloud service providers, e.g. AWS and Microsoft Azure have provided FPGA computing resources in their infrastructure and have enabled users to design and deploy their own accelerators on these FPGAs. Multi-tenancy FPGAs, where multiple users can share the same FPGA fabric with certain types of isolation to improve resource efficiency, have already been proved feasible. However, this also raises security concerns. Various types of side-channel attacks targeting multi-tenancy FPGAs have been proposed and validated. The awareness of security vulnerabilities in the cloud has motivated cloud providers to take action to enhance the security of their cloud environments. In FPGA security research papers, researchers always perform attacks under the assumption that attackers successfully co-locate with victims and are aware of the existence of victims on the same FPGA board. However, the way to reach this point, i.e., how attack- ers secretly obtain information regarding accelerators on the same fabric, is constantly ignored despite the fact that it is non-trivial and important for attackers. In this paper, we present a novel finger- printing attack to gain the types of co-located FPGA accelerators. We utilize a seemingly non-malicious benchmark accelerator to sniff the communication link and collect performance traces of the FPGA-host communication link. By analyzing these traces, we are able to achieve high classification accuracy for fingerprinting co-located accelerators, which proves that attackers can use our method to perform cloud FPGA accelerator fingerprinting with a high success rate. As far as we know, this is the first paper targeting multi-tenant FPGA accelerator fingerprinting with the communica- tion side-channel. 

Cloud computing has emerged as a critical part of commercial computing infrastructure due to its computing power, data storage capabilities, scalability, software/API integration, and convenient billing features. At the early stage of cloud computing, the majority of clouds are homogeneous, i.e., most machines are identical. It has been proven that heterogeneity in the cloud, where a variety of machine configurations exist, provides higher performance and power efficiency for applications. This is because heterogeneity enables applications to run in more suitable hardware/software environments. In recent years, the adoption of heterogeneous cloud has increased with the integration of a variety of hardware into cloud systems to serve the requirements of increasingly diversified user applications. At the same time, the emergence of security threats, such as micro-architectural attacks, is becoming a more critical problem for cloud users and providers. It has been demonstrated (e.g., Repttack and Cloak & Co-locate) that the prerequisite of micro-architectural attacks, the co-location of attack and victim instances, is easier to achieve in the heterogeneous cloud. This also means that the ease of attack is not just related to the heterogeneity of the cloud but increases with the degree of heterogeneity. However, there is a lack of numerical metrics to define, quantify or compare the heterogeneity of one cloud environment with another. In this paper, we propose a novel metric called Heterogeneity Score (HeteroScore), which quantitatively evaluates the heterogeneity of a cluster. We demonstrate that HeteroScore is closely connected to security against co-location attacks. Furthermore, we propose mitigation techniques to tradeoff heterogeneity offered with security. We believe this is the first quantitative study that evaluates cloud heterogeneity and links heterogeneity to infrastructure security