Large language models (LLMs) demonstrate exceptional instruct-following ability to complete various downstream tasks.Although this impressive ability makes LLMs flexible task solvers, their performance in solving tasks also heavily relies on instructions.In this paper, we reveal that LLMs are over-sensitive to lexical variations in task instructions, even when the variations are imperceptible to humans.By providing models with neighborhood instructions, which are closely situated in the latent representation space and differ by only one semantically similar word, the performance on downstream tasks can be vastly different.Following this property, we propose a blackbox Combinatorial Optimization framework for Prompt Lexical Enhancement (COPLE).COPLE performs iterative lexical optimization according to the feedback from a batch of proxy tasks, using a search strategy related to word influence.Experiments show that even widelyused human-crafted prompts for current benchmarks suffer from the lexical sensitivity of models, and COPLE recovers the declined model ability in both instruct-following and solving downstream tasks.

Microservice applications are gaining popularity as cloud-native embraced by the IT industry. However, they suffer from a latency problem because they intrinsically involve mass inter-microservice data exchange that introduces additional latency. The remarkable gap of data transmission speed between co-location and cross-server communication necessitates the need of utilizing microservice deployment strategies to accelerate data transmission and reduce end-to-end latency. Previous works characterize communication dependencies among microservices and put strongly inter-dependent ones on the same server to reduce communication overhead. Unfortunately, they overlook characteristics of microservice applications, and the resulting modeling is neither fine-grained enough nor comprehensive, which misleads microservice deployment and ultimately results in prolonged communication delay. To address the above problems, we propose a novel microser-vice deployment strategy based on fine-grained and comprehensive modeling of microservice dependencies, on the basis of an in-depth analysis of service characteristics. In this paper, dependencies are measured on the request level and combined with data sharing relationships to jointly decide microservice division, aiming to facilitate inter-microservice communication. After that, a selective scale-up strategy is designed to further reduce cross-server communication and shorten response latency. Extensive experiments on a real-world microservice application demonstrate that our method is effective in mitigating commu-nication overhead and can reduce end-to-end latency by 15.64% ~ 29.18 % compared with the state-of-the-art methods.

Microservice architecture is embraced by a growing number of enterprises due to the benefits of modularity and flexibility. However, being composed of numerous interdependent microservices, it is prone to cascading failures and afflicted by the arising problem of troubleshooting, which entails arduous efforts to identify the root cause node and ensure service availability. Previous works use call graph to characterize causality relation-ships of microservices but not completely or comprehensively, leading to an insufficient search of potential root cause nodes and consequently poor accuracy in culprit localization. In this paper, we propose ImpactTracer to address the above problems. ImpactTracer builds impact graph to provide a com-plete view of fault propagation in microservices and uses a novel backward tracing algorithm that exhaustively traverses the impact graph to identify the root cause node accurately. Extensive experiments on a real-world dataset demonstrate that ImpactTracer is effective in identifying the root cause node and outperforms the state-of-the-art methods by at least 72%, significantly facilitating troubleshooting in microservices.

Hardware-assisted virtualization is widely adopted in cloud computing and desktop platforms, significantly enhancing the security and efficiency of full virtualization systems by dividing multiple guest operating systems (OSes) and providing isolation between guest OSes and host OS. Thus, malware is usually analyzed in a guest OS to prevent the host OS from being compromised. However, an advanced malware will deliberately masquerade as a benign program when it detects that it is in a virtualized environment. The ideal goal of virtualization is to be utterly transparent to the guest processes, i.e., a guest process cannot be aware that it runs in a virtualized environment. Nowadays, although hardware-assisted virtualization detection is challenging, we present three novel approaches that can be leveraged to detect hardware-assisted virtualization. Our detection approaches are mainly based on the following features: i) The available CPU instruction sets are different in native and virtualized environments; ii) Modification to specific control register flag has different effects in native and virtualized environments; iii) The physical addresses in native and virtualized environments have different effects on the eviction of the level 2 CPU cache. We have conducted intensive experiments in both native and cloud environments. The experimental results show that our approaches can effectively detect the existence of hardware-assisted virtualization.

The selfish On-Board-Unit (OBU) attacks Vehicular Ad-Hoc Network (VANET) by various attacks for profit. However, many existing methods are based on the principle of direct reciprocity for communication, and when an attack occurs, it is easy to crash in the case of large-scale networks. In order to reduce the number of attackers in the vehicle ad-hoc network and restrain the attack motivation of the OBUs, we propose an indirect reciprocal incentive mechanism based on reputation to encourage the OBUs in the VANET to help each other. Since most OBUs are in great need of network services, including potential attackers, when the loss of network services is far greater than the illegal benefits of their attacks, selfish and rational OBU will give up attacks and take desirable behavior. In addition, to prevent some attacks from tampering with information, we also apply blockchain technology to record the behavior of OBU. The indirect reciprocity process of each OBU in VANET can be regarded as a Markov Decision Process (MDP). In order to restrain the attack motivation of selfish nodes and communicate normally without knowing the attack model, an algorithm based on Deep Reinforcement Learning (DRL) is proposed to suppress attack motivation, so as to activate OBU learning in dynamic environment and make wise decisions. Finally, through a large number of simulation experiments, the performance of our proposed algorithm is obviously better than that of the baseline strategy, and is verified by the simulation results.

Cloud computing provides a shared pool of configurable computing resources and significantly improves the utilization of computing resources, but it also introduces security threats. We focus on the virtual machine (VM) co-residency, which allows an attacker to launch cross- VM attacks against target VMs. To tackle this problem, we propose a secure and efficient VM allocation strategy to reduce the cross- VM attack threats while ensuring the efficiency of the cloud data center. First, we establish several metrics related to security and efficiency for the cloud data center. Then, we establish a constrained optimization model. Next, we allocate and migrate VMs based on typical suspicious or vulnerable VM features, and solve the optimization problem through our improved NSGA-II allocation. Finally, we implement our allocation strategy and conduct intensive experiments. The experimental results show that our allocation strategy performs better than the existing stratezies and orovides cloud vendors with tradeoff solutions.