Serverless platforms are widely adopted for deploying applications due to their autoscaling capabilities and pay-asyou- go billing models. These platforms execute an application's functions inside ephemeral containers and scale the number of containers based on incoming request rates. To meet service level objectives (SLOs), they often over-provision resources by maintaining warm containers or rapidly spawning new ones during traffic bursts. However, this strategy frequently leads to inefficient resource utilization, especially during periods of low activity. Prior research addresses this issue through intelligent scheduling, lightweight virtualization, and containersharing mechanisms. More recent work aims to improve resource utilization by remodeling the execution of a function within a container to better separate compute and I/O stages. Despite these improvements, existing approaches often introduce delays during execution and induce memory pressure under traffic bursts. In this paper, we present Styx, a novel workflow engine that enhances resource utilization by intelligently decoupling compute and I/O stages. Styx employs a fetch latency predictor that uses real-time system metrics from both the serverless node and the remote storage server to accurately estimate prefetch operations, ensuring input data is available exactly when needed. Furthermore, it offloads the output data upload operation from a container to a host-side data service, thereby efficiently managing provisioned memory. Our approach improves the overall memory allocation by 32.6% when running all the serverless workflows simultaneously when compared to Dataflower + Truffle. Additionally, this method improves the tail latency and the mean latency of a workflow by an average of 26.3% and 21%, respectively.

The cold start latency in serverless systems is a matter of great concern. It militates against its basic foundation, which is fast millisecond-level execution of mostly stateless functions. Over the last five years, a lot of work has been done in academia and industry to mitigate the overheads caused by long cold start times. In this paper, we focus on a specific line of work that proposes to modify the Docker container's architecture to address this problem. We observe that state-of-the-art work has either fused Docker layers or used smart on-demand fetching of data.

Serverless computing systems have become very popular because of their natural advantages with respect to auto-scaling, load balancing and fast distributed processing. As of today, almost all serverless systems define two QoS classes: best-effort ($BE$) and latency-sensitive ($LS$). Systems typically do not offer any latency or QoS guarantees for$BE$jobs and run them on a best-effort basis. In contrast, systems strive to minimize the processing time for$LS$jobs. This work proposes a precise definition for these job classes and argues that we need to consider a bouquet of performance metrics for serverless applications, not just a single one. We thus propose the comprehensive latency ($CL$) that comprises the mean, tail latency, median and standard deviation of a series of invocations for a given serverless function. Next, we design a systemFaaSCtrl, whose main objective is to ensure that every component of the$CL$is within a prespecified limit for an LS application, and for BE applications, these components are minimized on a best-effort basis. Given the sheer complexity of the scheduling problem in a large multi-application setup, we use the method of surrogate functions in optimization theory to design a simpler optimization problem that relies on performance and fairness. We rigorously establish the relevance of these metrics through characterization studies. Instead of using standard approaches based on optimization theory, we use a much faster reinforcement learning (RL) based approach to tune the knobs that govern process scheduling in Linux, namely the real-time priority and the assigned number of cores. RL works well in this scenario because the benefit of a given optimization is probabilistic in nature, owing to the inherent complexity of the system. We show using rigorous experiments on a set of real-world workloads thatFaaSCtrlachieves its objectives for both LS and BE applications and outperforms the state-of-the-art by 36.9% (for tail response latency) and 44.6% (for response latency's std. dev.) for LS applications.

Estimating the quality of a benchmark suite is a non-trivial task. A poorly selected or improperly configured bench-mark suite can present a distorted picture of the performance of the evaluated framework. With computing venturing into new domains, the total number of benchmark suites available is increasing by the day. Researchers must evaluate these suites quickly and decisively for their effectiveness. We present Perspector, a novel tool to quantify the performance of a benchmark suite. Perspector comprises novel metrics to characterize the quality of a benchmark suite. It provides a math-ematical framework for capturing some qualitative suggestions and observations made in prior work. The metrics are generic and domain-agnostic. Furthermore, our tool can be used to compare the efficacy of one suite vis-a-vis other benchmark suites, systematically and rigorously create a suite of workloads, and appropriately tune them for a target system.

Serverless computing is getting increasingly popular because of its fine-grained billing model and autoscaling features. To speed up the process of functions' sandbox creation, cloud providers typically utilize snapshot and restore-based mechanisms for pre-warmed snapshots. This effectively trades off the startup latency with the storage requirements and the overhead of creating/restoring these snapshots. Hence, there is a need to compress the snapshots by identifying identical data chunks across snapshots and then design methods to quickly deduplicate snapshots and retrieve them. We propose SnapStore -- a novel method of finding such duplicates. As opposed to conventional work that relies on better hashing methods, we use the natural structure of the program's memory map to reduce wasted work during deduplication. Furthermore, we sequentialize and minimize disk accesses as much as possible while retrieving a snapshot into a RAM-based cache. Both of these optimizations, yield a reasonably large speedup in the deduplication process as compared to the state-of-the-art (≈ 46% in the snapshot deduplication time and ≈ 82.6% in the retrieval time on HDDs). Upon integration with FaaSnap (a state-of-the-art serverless platform), SnapStore improves the end-to-end latency of serverless functions by 25.9% along with 2.4× storage space reduction over vanilla FaaSnap on HDDs. With SSDs, our deduplication time and retrieval time reduce by 36.2% and 75.8%, respectively, with almost no degradation in the end-to-end latency.

Trusted execution environments (TEEs) such as Intel SGX facilitate the secure execution of an application on untrusted machines. A plethora of work focuses on improving the performance of such environments necessitating the need for a standard, widely accepted benchmark suite. We present SGXGauge, a benchmark suite for SGX containing a diverse set of workloads from different domains. We also thoroughly characterize the behavior of the benchmark suite on a native platform and on a platform that uses a library OS-based shim layer (GrapheneSGX).

Modern software programs have dedicated license-check modules that restrict access to users, who possess valid credentials. They also have a large number of add-on pluggable modules that can be separately purchased and have their dedicated license managers. Sadly, recent work shows that regardless of their complexity, it is possible to break their security using a novel class of attacks known as control flow bending attacks (CFB attacks), where the program is run on a virtual CPU, unbeknownst to it.