360° video streaming is central to immersive applications such as virtual reality, education, and telepresence, yet delivering stall-free playback with high viewport quality remains difficult under fluctuating bandwidth and inaccurate viewport prediction. Prior solutions either fail to guarantee stall-free playback, introduce prohibitive overhead, or neglect prediction inaccuracies. We propose Opt360, a DASH-compliant optimization framework that generalizes tile assignment into multi-tier priority zones, incorporates prediction accuracy and window length directly into the optimization, and enforces hard constraints on stalls and quality switches. The resulting mixedinteger formulation, coupled with a segment-internal tile scheduler, adapts to diverse viewport models while remaining real-time feasible. Extensive evaluations demonstrate that Opt360 ensures smooth playback, remains resilient to viewport variations, and effectively utilizes bandwidth for improved video quality, even under challenging network conditions.

Official statistics indicate that internet users all around the world watch more videos and play more games during the COVID-19 pandemic than at any time [18]. This unprecedented, challenging situation demands solutions to accommodate rapid growth while maintaining and/or enhancing the video quality. This paper proposes SODA-Stream, an SDN-based optimization framework for enhancing Quality-of-Experience (QoE) in DASH streaming. The optimization framework max-imizes the number of concurrent streaming sessions that can be accommodated in a network and maximize streaming quality. The practical implementation of the framework utilizes the dynamic routing and bandwidth allocation enabled by Software Defined Networking (SDN). The evaluation results show that SODA-Stream significantly outperforms the conventional network routing and resource allocation algorithms, accepting 52% more sessions, 45% improvement in bandwidth allocation, and 70% reduction in bandwidth wastage, smoother playback, and better viewing experience.

Cloud computing provisions scalable resources for high performance industrial applications. Cloud providers usually offer two types of usage plans: reserved and on-demand. Reserved plans offer cheaper resources for long-term contracts while on-demand plans are available for short or long periods but are more expensive. To satisfy incoming user demands with reasonable costs, cloud resources should be allocated efficiently. Most existing works focus on either cheaper solutions with reserved resources that may lead to under-provisioning or over-provisioning, or costly solutions with on-demand resources. Since inefficiency of allocating cloud resources can cause huge provisioning costs and fluctuation in cloud demand, resource allocation becomes a highly challenging problem. In this paper, we propose a hybrid method to allocate cloud resources according to the dynamic user demands. This method is developed as a two-phase algorithm that consists of reservation and dynamic provision phases. In this way, we minimize the total deployment cost by formulating each phase as an optimization problem while satisfying quality of service. Due to the uncertain nature of cloud demands, we develop a stochastic optimization approach by modeling user demands as random variables. Our algorithm is evaluated using different experiments and the results show its efficiency in dynamically allocating cloud resources.

Detailed routing is one of the most challenging aspects of the physical design process. Many of the violations that occur during the detailed routing stage stem from the placement of the cells. In this paper, we propose a deep learning framework to identify short violations that can occur during detailed routing from a placed netlist. One of the advantages of our technique is that by using the proposed deep learning-based predictor, global routing is no longer required as frequently and hence the total runtime for place and route can be significantly reduced. In this paper, we discuss the proposed framework and the methodology for analyzing the extracted features. The experimental results show that the average sensitivity, specificity, and accuracy of Eh?Predictor is above 90%. In addition, we show that Eh?Predictor is up to 14 times faster than NCTUgr for smaller designs and up to 96 times faster for larger designs.

Detecting and preventing routing violations has become a critical issue in physical design, especially in the early stages. Lack of correlation between global and detailed routing congestion estimations and the long runtime required to frequently consult a global router adds to the problem. In this paper, we propose a machine learning framework to predict detailed routing short violations from a placed netlist. Factors contributing to routing violations are determined and a supervised neural network model is implemented to detect these violations. Experimental results show that the proposed method is able to predict on average 90% of the shorts with only 7% false alarms and considerably reduced computational time.

This paper introduces a new method for computing matrix exponential based on truncated Neumann series. The efficiency of the method is based on smart factorizations for evaluation of several Neumann series that can be done in parallel and divided across different processors with low communication overhead. A physical realization on FPGA is provided for proof-of-concept. The method is verified to be advantageous over the usual Horner's rule approach for polynomial evaluation. The hardware verification shows a reduction of 62% in time required for processing for series approximations with 9 terms. Software verification demonstrates a 30% reduction in time compared to Horner's rule and the trade-offs between using a higher precision approach is illustrated.

Cloud computing is a new era of computing that offers resources and services for Web applications. Selection of optimal cloud resources is the main goal in cloud resource allocation. Sometimes, customers pay more than required since cloud providers' pricing strategy is designed for the interest of the providers. Nonetheless, cloud customers are interested in selecting cloud resources to meet their quality of service (QoS) requirements. Thus, for the interest of both providers and customers, it is vital to balance the two conflicting objectives of deployment cost and QoS performance. In this paper, we present a cost-effective and runtime friendly algorithm that minimizes the deployment cost while meeting the QoS performance requirements. In other words, the algorithm offers an optimal choice, from customers' point of view, for deploying a Web application in cloud environment. The multi-objective optimization algorithm minimizes cost and maximizes QoS performance simultaneously. The proposed algorithm is verified by a series of experiments on different workload scenarios deployed in two distinct cloud providers. The results show that the proposed algorithm finds the optimal combination of cloud resources that provides a balanced trade-off between deployment cost and QoS performance in relatively low runtime.

Cloud computing provides a reliable and cost- effective setting for deploying large-scale web applications. However, choosing and configuring an appropriate cloud Infrastructure-as-a-Service (IaaS), e.g., the appropriate database and computing instances and acceptable service rates, is a daunting task. The task is also challenging when trying to optimize the IaaS for conflicting objectives such as performance and cost. Furthermore, due to lack of understanding of the pricing model and the cloud IaaS, a cloud consumer may pay more than necessary or may not fully utilize the purchased resources. For this reason, we propose an algorithm that suggests the most cost-effective configuration meeting the QoS requirements and budget constraint. In contrast to existing cost optimization proposals, our proposed algorithm maps the minimum requirements of the to- be-deployed web application to deployment costs according to the price model set by cloud providers. The algorithm also considers QoS requirements for different resource types in the cloud, namely, database servers, computing servers, storage, and service rate. The proposed algorithm is evaluated by a series of experiments on a web application with seven different workload scenarios. The experimental results show the effectiveness of the proposed algorithm in achieving a solution with the minimum deployment cost for each scenario while satisfying all customer's requirements.

Participants in an electricity market expect to have a fair, transparent, and open competition. Market Surveillance Administrators (MSA) are responsible for monitoring the market outcomes to investigate if they are consistent with the fundamentals of the electricity markets. It can be an immensely time-consuming process with high amounts of computations in an electricity market with huge numbers of participants. Besides, a manual review of market operations may be biased by involving humans in the decision making process. If this anomaly detection procedure can be done automatically then it can be a great aid to the market surveillance process for having an unbiased and prompt tool to monitor the market. In this paper, an anomaly detection algorithm is proposed to identify the events of interest in an electricity market. This algorithm provides the MSA with a tool to detect the instances in the electricity market when electricity price behavior deviates from the normal expected regime. These anomalous hours can then be analyzed further in order to diagnose the reason.

In this paper, we present and analyze four efficient models that produce significantly improved results by optimizing conflicting power and skew objectives in the clock network buffer sizing problem. Each model is in geometric programming format and has certain advantages, such as maximum reduction in power, robustness to process variation, and striking a balance between skew and power optimization. The buffer sizing problem is formulated as a geometric programming problem to provide globally optimal solutions to the four models. We also show that a geometric programming multiobjective model can be used to optimize both power and skew without requiring any tuning from a designer. The presented self-tuning multiobjective formulation not only provides optimal solutions for buffer sizes, but also finds the tuning parameters that result in overall combined reduction in power and skew without loss of convexity. The effectiveness of the models are illustrated on several publicly available benchmarks. The models provide on average 40% to 60% improvement in power while reducing skew in several cases. We have also proposed a smart heuristic for discretization of the continuous geometric programming solution that preserves skew and power. Finally, we provide a guideline for designers to decide which one of the proposed models is the most appropriate for their needs.

Most engineering problems involve optimizing different and competing objectives. To solve multi-objective problems, normally a weighted sum of the objectives is optimized. However, how the weights are assigned can greatly affect the outcome. Therefore, many designers have to resort to producing the Pareto surface - a time-consuming procedure. In this paper, we propose a framework for solving multi-objective geometric programming problems where weights in the objective are optimally calculated during the optimization problem without having to produce the Pareto surface. It is shown that the proposed self-tuning multi-objective framework can be applied to geometric programming gate sizing problems. Then, the efficacy of the proposed framework is proven using the clock network buffer sizing problem as an application. The problem is first formulated as a geometric programming (GP) problem with the objectives of reducing power, skew, and slew. The problem is solved using ISPD09 circuits. The power, skew and slew of the optimized networks are calculated using ngspice. The results show on average 52% reduction in power and 28% reduction in skew compared to the original networks. The self-tuning multi-objective solution is shown superior to any single objective solution with no impact on runtime.

Minimizing power and skew for clock networks are critical and difficult tasks which can be greatly affected by buffer sizing. However, buffer sizing is a non-linear problem and most existing algorithms are heuristics that fail to obtain a global minimum. In addition, existing buffer sizing solutions do not usually consider manufacturing variations. Any design made without considering variation can fail to meet design constraints after manufacturing. In this paper, first we proposed an efficient optimization scheme based on geometric programming (GP) for buffer sizing of clock networks. Then, we extended the GP formulation to consider process variations in the buffer sizes using robust optimization (RO). The resultant variation-aware network is examined with SPICE and shown to be superior in terms of robustness to variations while decreasing area, power and average skew.

In this paper, two effective preprocessing techniques for clustering large-scale circuits are presented. These techniques can be performed before the general multilevel clustering to enhance the speed of the partitioning technique while preserving the solution quality. A "wrapped" version of hMETIS is implemented, in which the proposed techniques are applied as the preprocessing step. The empirical results on standard benchmark circuits show that the application of the proposed techniques improves the over all runtime by 30% and the partitioning results by 2%.