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Multi-tenant service-based systems (SBSs) have gained unprecedented prominence in recent years. Network-accessible Web services are composed in the form of business process to simultaneously fulfill multiple tenants' functional and multi-dimensional quality-of-service (QoS) requirements. Those services often operate in a distributed and volatile environment. It is of tremendous importance to monitor...
Massive parallel business workflows running in the cloud are prone to temporal violations (namely intermediate runtime delays) due to various reasons such as service performance fluctuation and resource conflicts. To deliver satisfactory on-time completion, cloud workflow temporal verification is employed to accurately detect time delays of workflow activities and timely handle temporal violations...
Detecting runtime anomalies is very important to monitoring and maintenance of distributed services. People often use execution logs for troubleshooting and problem diagnosis manually, which is time consuming and error-prone. In this paper, we propose an approach for automatic anomaly detection based on logs. We first mine a hybrid graph model that captures normal execution flows inter and intra services,...
In this paper, we introduce a runtime monitoring method for Actor-based programs and present a Scala module that realizes the proposed method. The primary characteristic of our method is that it supports asynchronous message-passing based on the Actor model. Besides, the module does not require specialized languages for describing application properties to be monitored. Once a developer incorporates...
Cloud federation is an emergent cloud-computing paradigm where partner organisations share data and services hosted on their own cloud platforms. In this context, it is crucial to enforce access control policies that satisfy data protection and privacy requirements of partner organisations. However, due to the distributed nature of cloud federations, the access control system alone does not guarantee...
With the raise of computer systems' complexity, greater number of modules, technologies involved and functionalities, monitoring and keeping up with such systems in real time has become an indispensable task, given the fact that the quality of production software impacts directly on decision making and risk management by organizations. Knowing what to observe, which metrics are being more used for...
Monitoring the traffic volumes of elephant flows, including the total byte count per flow, is a fundamental capability for online network measurements. We present an asymptotically optimal algorithm for solving this problem in terms of both space and time complexity. This improves on previous approaches, which can only count the number of packets in constant time. We evaluate our work on real packet...
The workload of many-core systems includes real-time (RT) applications. Obtain energy savings while executing RT applications is a challenge due to the RT timing constraints. The techniques used to reduce the consumption, as dynamic voltage and frequency scaling (DVFS), usually delays the applications, leading to constraint violations. Most works in the literature ensure RT constraints based on design-time...
The combination of fast online anomaly detection and offline learning is a vital element of operations in large-scale datacenters and utility clouds. Given ever-increasing datacenter sizes coupled with the complexities of systems software, applications, and workload patterns, such anomaly detection must operate continuous and real-time at runtime. Further, detection should function for both hardware...
MapReduce is a framework for processing large data sets much used in the context of cloud computing. MapReduce implementations like Hadoop can tolerate crashes and file corruptions, but not arbitrary faults. Unfortunately, there is evidence that arbitrary faults do occur and can affect the correctness of MapReduce job executions. Furthermore, many outages of major cloud offerings have been reported,...
Software systems are deployed in environments that keep changing over time. They should therefore adapt to changing conditions in order to meet their requirements. The satisfaction rate of these requirements depends on the rate at which adverse conditions prevent their satisfaction. Obstacle analysis is a goal-oriented form of risk analysis for requirements engineering (RE) whereby obstacles to system...
This paper presents Lotus@Runtime, an extensible tool that uses models@runtime to monitor and verify self-adaptive systems. The tool monitors the execution traces generated by a self-adaptive system and annotates the probabilities of occurrence of each system action on their respective transition on the system model, which is created at design time in the tool as a Labelled Transition System (LTS)...
Dynamic Software Product Line (DSPLs) based Systems are capable of adapting inresponse to changes concerning their observations at runtime in order toexhibit appropriate behavior. The observation space and the variability in theconfiguration space is usually known at design time. However, running a set oftests with all combinations of configuration and observation from these spacesis likely to be...
Top-k reports are compound metrics that provide useful information when diagnosing problems in a system, e.g., to identify persistent CPU usage by a process. In large systems, these reports are collected at regular intervals and need to be resampled to a coarser granularity to answer user queries for different sampling periods, or to save space and make it possible to keep historical data for long...
The ongoing integration of various devices and services into nowadays buildings as part of networked building automation systems (BAS) demands for flexible and dynamically adaptable system architectures as they are supported by service orientation, which, moreover, can provide suitable abstractions when integrating new technologies and systems arising for example from the Internet of Things. The increasing...
In order to boost the performance of data-intensive computing on HPC systems, in-memory computing frameworks, such as Apache Spark and Flink, use local DRAM for data storage. Optimizing the memory allocation to data storage is critical to delivering performance to traditional HPC compute jobs and throughput to data-intensive applications sharing the HPC resources. Current practices that statically...
The vast attack surface of clouds presents a challenge in deploying scalable and effective defenses. Traditional security mechanisms, which work from inside the VM fail to provide strong protection as attackers can bypass them easily. The only available option is to provide security from the layer below the VM i.e., the hypervisor. Previous works that attempt to secure VMs from "outside"...
In this paper, we introduce the idea of exploiting the emotional information as a key element in providing personalized context-aware software services and consequently enhancing quality of User Experience(UX). We argue that emotional measurements can be integrated in Quality of Service (QoS) assurance frameworks. The idea builds on the strength of technological advances in emotion measurement tools,...
Decision guidance models are a means for design space exploration and documentation. In this paper, we present decision guidance models for microservice monitoring. The selection of a monitoring system is an essential part of each microservice architecture due to the high level of dynamic structure and behavior of such a system. We present decision guidance models for generation of monitoring data,...
Concurrency is a requirement for much modern software, but the implementation of multithreaded algorithms comes at the risk of errors such as data races.Programmers can prevent data races by documenting and obeying a locking discipline, which indicates which locks must be held in order to access which data.This paper introduces a formal semantics for locking specifications that gives a guarantee of...
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