In this work, we present a survey of residential load controlling techniques to implement demand side management in future smart grid. Power generation sector facing important challenges both in quality and quantity to meet the increasing requirements of consumers. Energy efficiency, reliability, economics and integration of new energy resources are important issues to enhance the stability of power system infrastructure. Optimal energy consumption scheduling minimizes the energy consumption cost and reduce the peak-to-average ratio (PAR) as well as peak load demand in peak hours. In this work, we discuss different energy consumption scheduling schemes that schedule the household appliances in real-time to achieve minimum energy consumption cost and reduce peak load curve in peak hours to shape the peak load demand.

Data-dependent branches constitute single biggest source of remaining branch mispredictions. Typically, data-dependent branches are associated with program data structures, and follow store-load-branch execution sequence. A set of memory locations is written at an earlier point in a program. Later, these locations are read, and used for evaluating branch condition. Branch outcome depends on data values stored in data structure, which, typically do not have repeatable pattern. Therefore, in addition to history-based dynamic predictor, we need a different kind of predictor for handling such branches.This paper presents Store-Load-Branch (SLB) predictor; a compiler-assisted dynamic branch prediction scheme for data-dependent direct and indirect branches. For every data-dependent branch, compiler identifies store instructions that modify the data structure associated with the branch. Marked store instructions are dynamically tracked, and stored values are used for computing branch flags ahead of time. Branch flags are buffered, and later used for making predictions. On average, compared to standalone TAGE predictor, combined TAGE+SLB predictor reduces branch MPKI by 21% and 51% for SPECINT and EEMBC benchmark suites respectively.

No abstract

History-based branch direction predictors for conditional branches are shown to be highly accurate. Indirect branches however, are hard to predict as they may have multiple targets corresponding to a single indirect branch instruction.We propose the Value Based BTB Indexing (VBBI), a correlation-based target address prediction scheme for indirect jump instructions.For each static hard-topredict indirect jump instruction, the compiler identifies a 'hint instruction', whose output value strongly correlates with the target address of the indirect jump instruction. At run time, multiple target addresses of the indirect jump instruction are stored and subsequently accessed from the BTB at different indices computed using the jump instruction PC and the hint instruction output values. In case the hint instruction has not finished its execution when the jump instruction is fetched, a second and more accurate target address prediction is made when the hint instruction output is available, thus reducing the jump misprediction penalty.We compare our design to the regular BTB design and the best previously proposed indirect jump predictor, the tagged target cache (TTC). Our evaluation shows that the VBBI scheme improves the indirect jump target prediction accuracy by 48% and 18%, compared with the baseline BTB and TTC designs, respectively. This results in average performance improvement of 16.4% over the baseline BTB scheme, and 13% improvement over the TTC predictor. Out of this performance improvement 2% is contributed by target prediction overriding which is accurate 96% of the time.

We live in the era of Intelligent Transport Systems (ITS), which is an extension of Vehicular AdHoc Networks (VANETs). In VANETs, vehicles act as nodes connected with each other and sometimes with a public station. Vehicles continuously exchange and collect information to provide innovative transportation services; for example, traffic management, navigation, autonomous driving, and the generation of alerts. However, VANETs are extremely challenging for data collection, due to their high mobility and dynamic network topologies that cause frequent link disruptions and make path discovery difficult. In this survey, various state-of-the-art data collection protocols for VANETs are discussed, based on three broad categories, i.e., delay-tolerant, best-effort, and real-time protocols. A taxonomy is designed for data collection protocols for VANETs that is essential to add precision and ease of understandability. A detailed comparative analysis among various data collection protocols is provided to highlight their functionalities and features. Protocols are evaluated based on three parametric phases. First, protocols investigation based on six necessary parameters, including delivery and drop ratio, efficiency, and recovery strategy. Second, a 4-D functional framework is designed to fit most data collection protocols for quick classification and mobility model identification, thus eradicating the need to read extensive literature. In the last, in-depth categorical mapping is performed to deep dive for better and targeted interpretation. In addition, some open research challenges for ITS and VANETs are discussed to highlight research gaps. Our work can thus be employed as a quick guide for researchers to identify the technical relevance of data collection protocols of VANETs.