Cloud providers commonly employ oversubscription strategies to maximize profitability, leveraging the significant gap between the resources purchased by tenants and those actually consumed by their workloads. However, the temporal volatility of workloads may lead to overload on oversubscribed nodes. To address this issue, existing works typically focus on designing reactive rescheduling mechanisms triggered by overload events or adopt conservative oversubscription strategies to mitigate overload risks. Nonetheless, these solutions compromise either tenant experience or provider profitability. In fact, reducing the temporal volatility of workloads is key to addressing the above challenges. We observe that many workloads exhibit temporal complementarity. Aggregating such workloads can effectively mitigate temporal volatility, thereby improving overall resource utilization. Motivated by this insight, we first design a new metric, called Maximum-based Coefficient of Variation (MCV), to quantify the temporal volatility of workloads. We then propose Hestia, a framework that achieves long-term stable oversubscription through workload aggregation. Specifically, we propose a smoothing-based method to classify workloads suitable for aggregation according to their periodicity. Subsequently, we design an aggregation algorithm to minimize the overall MCV, and treat the aggregated workloads as the units for oversubscription. Experimental results show that, using CPU as a representative example, Hestia reduces MCV by 43.3% and increases oversubscription profit by 66.74%.

Virtual private clouds (VPCs) play a critical role in providing secure and isolated network environments for web services. However, with the growing number and size of VPCs, efficiently delivering control messages from the control plane to the data plane has become a major concern for cloud vendors. Existing end-to-end transmission solutions (e.g., RPC) will result in substantial overhead in the control plane, while message-oriented middleware-based solutions (e.g., message queue) will lead to high data plane overhead. To address this issue, we design Meteor, a high-performance control message delivery system for large-scale clouds. Specifically, Meteor combines an RPC path with a message queue (MQ) path and employs an auto dual-path switching mechanism to minimize the message delivery latency. Additionally, we propose a VPC-based message delivery and filtering scheme for the MQ path to reduce data plane overhead. We also design a delivery robustness guarantee mechanism to ensure the reachability and consistency of control messages. Meteor has been thoroughly tested with up to 100k container instances. Evaluation results show that Meteor decreases the message delivery latency by 48.8% and reduces the overhead by about 50% in real-world scenarios, compared with state-of-the-art solutions.

Ensuring reliable operation of cloud networks is critical for cloud service providers to guarantee quality of service for tenants. A promising solution is to design a high-fidelity cloud network validation platform that proactively validates the correctness of all operations before implementing changes to the production network. However, the tight coupling between physical and virtual networks in the cloud poses challenges to achieving high-fidelity cloud network validation. Existing network validation platforms focus primarily on traditional physical networks, while ignoring virtual network validation. Regrettably, neglecting the combined validation of physical and virtual networks will result in inaccurate evaluations. To bridge this gap, we present HifiCNet, a high-fidelity platform that concurrently validates both physical and virtual networks. HifiCNet designs an orchestrator to elegantly coordinate the interaction between physical and virtual networks in the cloud and innovatively adopts an emulator-simulator hybrid architecture to ensure high fidelity and scalability for cloud network validation. Through extensive evaluation based on real topologies and traffic traces, we show that HifiCNet enables high-fidelity validation of cloud network configurations, services, and exceptions. Notably, HifiCNet can leverage 38 servers to establish a physical network comprising 10k hosts, as well as a virtual network consisting of 200k virtual machines.

The increasing demands of tenants are driving the growth of single virtual private cloud (VPC), leading to a trend towards cross-cluster VPC deployments, which fuels an increasing demand for cross-cluster VPC communication. However, the rapid growth of cross-cluster traffic and its inherently dynamic nature have exposed critical limitations in existing network solutions, which now struggle to maintain required throughput levels and ensure reliable communication. This growing inadequacy has consequently created persistent network performance bottlenecks in cross-cluster communication systems. To address this issue, we present HiReC, a system designed to achieve high-throughput and reliable cross-cluster VPC communication. To optimize throughput performance, HiReC leverages multiple gateways with a rounding-based mapping algorithm that ensures effective load balancing to forward cross-cluster traffic. Furthermore, HiReC augments gateway forwarding efficiency through implementation of the eXpress Data Path (XDP) framework, leveraging kernel-bypass techniques to accelerate packet processing. For reliability enhancement, HiReC employs a low-overhead, eBPF-based monitoring module and adaptive load adjustment mechanism to dynamically adjust traffic distribution among gateways, effectively handling gateway node or link failures. We implement our system and evaluate its performance through testbed experiments and simulation experiments. The results show that HiReC can effectively improve the throughput of cross-cluster communication and deal with abnormal events. For example, HiReC improves the throughput by$3.8\times $and reduces the failure recovery latency by$19\times $compared with state-of-the-art solutions.

The increasing demands of tenants are driving the growth of single virtual private cloud (VPC), leading to a trend towards cross-cluster VPC deployments, which fuels an increasing demand for cross-cluster VPC communication. However, with the rapid increase in cross-cluster traffic and its inherent dynamism, existing solutions fail to meet tenants' demands for throughput and reliability, thereby leading to network performance bottlenecks in cross-cluster communication. To address this issue, we present HiReC, a system designed to achieve high-throughput and reliable cross-cluster VPC communication. To improve throughput, HiReC leverages multiple gateways with a rounding-based mapping algorithm for load balancing to forward cross-cluster traffic. Moreover, we further enhance the forwarding capabilities of gateways with the eXpress Data Path (XDP) technology. To enhance reliability, HiReC employs a low-overhead, eBPF-based monitoring module and adaptive load adjustment mechanism to dynamically adjust traffic distribution among gateways, effectively handling gateway node or link failures. We implement our system and evaluate its performance through testbed experiments. The results show that HiReC can effectively improve the throughput of cross-cluster communication and deal with abnormal events. For example, HiReC improves the throughput by$3.88 \times$and reduces the failure recovery latency by$19 \times$compared with state-of-the-art solutions.

Cloud vendors provide network services to tenants through shared service nodes, which may cause the abnormal traffic of one tenant to affect others. Deploying auxiliary systems such as firewalls will reduce the frequency of abnormal traffic occurrences but cannot eliminate them entirely. In practice, proper tenant deployment is a promising method to pursue anomaly isolation. Previous works have explored solutions along this line, such as controlling the impact scope of abnormal traffic to mitigate the influence of anomalies among tenants. However, these solutions cannot ensure full anomaly isolation among all tenants. That is, an anomaly in one tenant may cause complete service disruption for another tenant. To bridge the gap, we study the problem of multi-tenant Deployment with Anomaly Isolation (DAI), which is NP-hard. To address this problem, this paper introduces R-DAI, a rounding-based algorithm that can provide a tenant deployment solution in polynomial time, ensuring anomaly isolation among all tenants and load balancing. We implement our proposed algorithm on a large-scale simulation, and the results demonstrate its superior performance. For example, our algorithm eliminates tenant service disruptions caused by abnormal traffic and reduces the impact scope of a service node failure by 53% compared with other alternatives.

Ensuring reliable operation of cloud networks is critical for cloud service providers to guarantee quality of service for tenants. A promising solution is to design a high-fidelity cloud network validation platform that proactively validates the correctness of all operations before implementing changes to the production network. However, the tight coupling between physical and virtual networks in the cloud poses challenges to achieving high-fidelity cloud network validation. Existing network validation platforms focus primarily on traditional physical networks, while ignoring virtual network validation. Regrettably, neglecting the combined validation of physical and virtual networks will result in inaccurate evaluations. To bridge this gap, we present HifiCNet, a high-fidelity platform that concurrently validates both physical and virtual networks. HifiCNet designs an orchestrator to elegantly coordinate the interaction between physical and virtual networks in the cloud and innovatively adopts an emulator-simulator hybrid architecture to ensure high fidelity and scalability for cloud network validation. Through extensive evaluation based on real topologies and traffic traces, we show that HifiCNet enables high-fidelity validation of cloud network configurations, services, and exceptions. Notably, HifiCNet can use 38 servers to establish a physical network comprising 10k hosts, and a virtual network consisting of 200 k virtual machines.

In a multi-tenant cloud, the cloud service provider (CSP) leases physical resources to tenants in the form of virtual machines (VMs) with an agreed service level agreement (SLA). As the most important indicator of SLA, we should guarantee the service availability of tenants when placing the VMs. However, previous works about VM placement mainly concentrate on optimizing the cloud resource utilization, but only a few works consider the service availability by measuring the hardware availability. In fact, abnormal tenants can make the corresponding service unavailable by launching network attacks. That is, both the hardware availability and the tenant uncertainty will affect the service availability of VMs on physical machines (PMs). Without considering this factor, the CSP may fail to meet the tenant’s SLA requirements, leading to a reduction in revenue. To solve such a problem, this paper considers the service availability in terms of both the hardware availability and the tenant uncertainty, and studies the service availability-guaranteed VM placement in multi-tenant clouds (SAG-VMP) problem. This problem is very challenging since the service availability actually changes with the tenants served on the PM. To address this issue, we propose a two-phase approach: PM assignment and VM placement. The first phase determines the availability of each PM through a long-term tenant-PM mapping algorithm and the second phase places each VM on a PM that meets the service availability requirement based on a primal-dual online algorithm. Two algorithms with bounded approximation factors are proposed for these two phases, respectively. Both small-scale experiment results and large-scale simulation results show the superior performance of our proposed algorithms compared with other alternatives.

Autopsy reports play a pivotal role in forensic science. Medical examiners (MEs) and diagnostic radiologists (DRs) cross-reference autopsy results in the form of autopsy reports, while judicial personnel derive legal documents from final autopsy reports. In our prior study, we presented a visual analysis system called the forensic autopsy system for e-court instruments (FORSETI) with an extended legal medicine markup language (x-LMML) that enables MEs and DRs to author and review e-autopsy reports. In this paper, we present our extended work to incorporate provenance infrastructure with authority management into FORSETI for forensic data accountability, which contains two features. The first is a novel provenance management mechanism that combines the forensic autopsy workflow management system (FAWfMS) and a version control system called lmmlgit for x-LMML files. This management mechanism allows much provenance data on e-autopsy reports and their documented autopsy processes to be individually parsed. The second is provenance-supported immersive analytics, which is intended to ensure that the DRs’ and MEs’ autopsy provenances can be viewed, listed, and analyzed so that a principal ME can author their own report through accountable autopsy referencing in an augmented reality setting. A fictitious case with a synthetic wounded body is used to demonstrate the effectiveness of the provenance-aware FORSETI system in terms of data accountability through the experience of experts in legal medicine.

Even when generated with the same weapon, a wound's appearance would be affected by its use and the assaulter's physique. In contrast, even if wounds look similar in shape and/or color, they could have a different wounding history. In this work, we strive to extract the features of shape and color from a given wound image and build on the mathematical model of meaning to create the associated semantics of the criminal act. We demonstrate that a system called THEMIS (theoretical estimation of the meaning of insults) provides a context-sensitive visual similarity analysis for wound imagery in computational forensics. The THEMIS system can help forensic doctors and e-court stakeholders potentially determine important aspects of a case through a comparison with wounds of corpses in past cases, in a way that is not currently possible.