Software applications and workloads, especially within the domains of Cloud computing and large-scale AI model training, exert considerable demand on computing resources, thus contributing significantly to the overall energy footprint of the IT industry. In this paper, we present an in-depth analysis of certain software coding practices that can play a substantial role in increasing the application's overall energy consumption, primarily stemming from the suboptimal utilization of computing resources. Our study encompasses a thorough investigation of 16 distinct code smells and other coding malpractices across 31 real-world open-source applications written in Java and Python. Through our research, we provide compelling evidence that vari-ous common refactoring techniques, typically employed to rectify specific code smells, can unintentionally escalate the application's energy consumption. We illustrate that a discerning and strategic approach to code smell refactoring can yield substantial energy savings. For selective refactorings, this yields a reduction of up to 13.1 % of energy consumption and 5.1 % of carbon emissions per workload on average. These findings underscore the potential of selective and intelligent refactoring to substantially increase energy efficiency of Cloud software systems.

Adaptive bitrate (ABR) algorithms play a critical role in video streaming by making optimal bitrate decisions in dynamically changing network conditions to provide a high quality of experience (QoE) for users. However, most existing ABRs suffer from limitations such as predefined rules and incorrect assumptions about streaming parameters. They often prioritize higher bitrates and ignore the corresponding energy footprint, resulting in increased energy consumption, especially for mobile device users. Additionally, most ABR algorithms do not consider perceived quality, leading to suboptimal user experience. This article proposes a novel ABR scheme called GreenABR+, which utilizes deep reinforcement learning to optimize energy consumption during video streaming while maintaining high user QoE. Unlike existing rule-based ABR algorithms, GreenABR+ makes no assumptions about video settings or the streaming environment. GreenABR+ model works on different video representation sets and can adapt to dynamically changing conditions in a wide range of network scenarios. Our experiments demonstrate that GreenABR+ outperforms state-of-the-art ABR algorithms by saving up to 57% in streaming energy consumption and 57% in data consumption while providing up to 25% more perceptual QoE due to up to 87% less rebuffering time and near-zero capacity violations. The generalization and dynamic adaptability make GreenABR+ a flexible solution for energy-efficient ABR optimization.

Network Function Virtualization (NFV) platforms consume significant energy, introducing high operational costs in edge and data centers. This paper presents a novel framework called GreenNFV that optimizes resource usage for network function chains using deep reinforcement learning. GreenNFV optimizes resource parameters such as CPU sharing ratio, CPU frequency scaling, last-level cache (LLC) allocation, DMA buffer size, and packet batch size. GreenNFV learns the resource scheduling model from the benchmark experiments and takes Service Level Agreements (SLAs) into account to optimize resource usage models based on the different throughput and energy consumption requirements. Our evaluation shows that GreenNFV models achieve high transfer throughput and low energy consumption while satisfying various SLA constraints. Specifically, GreenNFV with Throughput SLA can achieve 4.4× higher throughput and 1.5× better energy efficiency over the baseline settings, whereas GreenNFV with Energy SLA can achieve 3× higher throughput while reducing energy consumption by 50%.

Adaptive bitrate (ABR) algorithms aim to make optimal bitrate decisions in dynamically changing network conditions to ensure a high quality of experience (QoE) for the users during video streaming. However, most of the existing ABRs share the limitations of predefined rules and incorrect assumptions about streaming parameters. They also come short to consider the perceived quality in their QoE model, target higher bitrates regardless, and ignore the corresponding energy consumption. This joint approach results in additional energy consumption and becomes a burden, especially for mobile device users. This paper proposes GreenABR, a new deep reinforcement learning-based ABR scheme that optimizes the energy consumption during video streaming without sacrificing the user QoE. GreenABR employs a standard perceived quality metric, VMAF, and real power measurements collected through a streaming application. GreenABR's deep reinforcement learning model makes no assumptions about the streaming environment and learns how to adapt to the dynamically changing conditions in a wide range of real network scenarios. GreenABR outperforms the existing state-of-the-art ABR algorithms by saving up to 57% in streaming energy consumption and 60% in data consumption while achieving up to 22% more perceptual QoE due to up to 84% less rebuffering time and near-zero capacity violations.

Cloud Security Posture Management (CSPM) tools have been gaining popularity to automate, monitor and visualize the security posture of multi-cloud environments. The foundation to assess the risk lies on being able to analyze each vulnerability and quantify its risk. However, the number of vulnerabilities in National Vulnerability Database (NVD) has skyrocketed in recent years and surpassed 144K as of late 2020. The current standard vulnerability tracking system relies mostly on human-driven efforts. Besides, open-source libraries do not necessarily follow the standards of vulnerability reporting set by CVE and NIST, but rather use Github issues for reporting. In this paper, we propose a framework, NL2Vul, to measure score of vulnerabilities with minimal human efforts. NL2Vul makes use of deep neural networks to train on descriptions of software vulnerabilities from NVD and predicts vulnerability scores. To flexibly expand the trained NVD model for different data sources that are being used to evaluate the risk posture in CSPM, NL2Vul uses transfer learning for quick re-training. We have evaluated NL2Vul with vanilla NVD, public Github issues of open source projects, and compliance technology specification documents.

IT compliance is an area of increasing attention and capital spend in enterprise IT environments. Enforcing compliance is a complex process, which involves following regulatory requirements coming from many, often overlapping sources, and mapping those requirements against a controls framework that implements them on the ground. In this paper, we propose a solution for streamlining the process of analyzing, monitoring and enforcing compliance in the cloud. We rely on text classification methodologies to match both regulatory requirements and controls against a common hierarchy. Finally, we explain how to use the text classification techniques to analyze the regulatory requirements, and match them to executable code that enforces these requirements in the cloud infrastructure components, such as in virtual machines and containers.

DevOps (software DEVelopment and information technology OPerationS) has established a culture and environment, in which building, testing, and releasing software happen more rapidly, frequently, and reliably through automated pipelines. To support this high turnover application cycle, the cluster orchestration frameworks such as Kubernetes or Docker Swarm have evolved to provide high flexibility and reliability. However, while cluster orchestrators run on the cloud infrastructure, any failure incurred from the infrastructure can directly impact the nodes of a cluster, so infrastructure failures can disrupt applications running on the cluster. In this paper, we propose proactive application placement algorithms with prediction of infrastructure failures. The proposed algorithms utilize failure-risk measurements, Failure-Index, determined from turnover rate of applications and prediction of infrastructure failures. We build stochastic models for application turnover and infrastructure failure processes, and provide various types of Failure-Index. Our placement algorithms are implemented in an orchestration framework, Kubernetes, and in the simulation model. Experimental results show that our methods reduce the amount of application disruption by 20% than the state-of-the-art algorithms in Kubernetes.

With the emergence of data deluge, the energy footprint of global data movement has surpassed 100 terawatt hours, costing more than 20 billion US dollars to the world economy. During an active data transfer, depending on the number of hops between the source and destination, the networking infrastructure consumes between 10% - 75% of the total energy, and the rest is consumed by the end systems. Even though there has been extensive research on reducing the power consumption at the networking infrastructure, the work focusing on saving energy at the end systems has been limited to the tuning of a few application-level parameters. In this paper, we introduce a novel cross-layer optimization framework which jointly considers application-level and kernel-level parameters to minimize the energy consumption without sacrificing from the transfer throughput. We present three different algorithms which can dynamically tune the CPU frequency level, number of active CPU cores, number of active transfer threads, number of parallel TCP streams, and the level of transfer command pipelining to achieve different user-set goals. Experimental results show that our proposed algorithms outperform the state-of-the-art solutions, achieving up to 80% higher throughput while consuming 48% less energy.

The global data movement over Internet has an estimated energy footprint of 100 terawatt hours per year, costing the world economy billions of dollars. The networking infrastructure together with source and destination nodes involved in the data transfer contribute to overall energy consumption. Although considerable amount of research has rendered power management techniques for the networking infrastructure, there has not been much prior work focusing on energy-aware data transfer solutions for minimizing the power consumed at the end-systems. In this paper, we introduce a novel application-layer solution based on historical analysis and real-time tuning called GreenDataFlow, which aims to achieve high data transfer throughput while keeping the energy consumption at the minimal levels. GreenDataFlow supports service level agreements (SLAs) which give the service providers and the consumers the ability to fine tune their goals and priorities in this optimization process. Our experimental results show that GreenDataFlow outperforms the closest competing state-of-the art solution in this area 50% for energy saving and 2.5× for the achieved end-to-end performance.

This article describes LineKing, a crowdsensing system for monitoring and forecasting coffee shop line wait times. LineKing consists of a smartphone component that provides automatic and accurate wait-time detection, and a cloud backend that uses the collected data to provide accurate wait-time estimation. LineKing is used on a daily basis by hundreds of users to monitor the wait-times of a coffee shop in the University at Buffalo, SUNY. The novel wait-time estimation algorithms of LineKing deployed at the cloud backend provide median absolute errors of less than 3 minutes.

The proximity alert service on Android is important as an enabler of smart cities, however, it is also limited in this role due to its excessive energy expenditure. In this paper, we present the design and implementation of an energy-efficient proximity alert service for both high-precision and low-precision applications. Our methods utilize the distance to the point of interest and the user's transportation mode in order to dynamically determine the location-sensing interval and the location providers (GPS or Network) to be used. We implement our methods as a middleware service in the Android open source project. Our service for realistic scenarios, on average, increases the battery life time of baseline proximity alert in Android by 62% for high-precision and 71% for low-precision applications.