Cloud computing for energy management in smart grid - an application survey

The smart grid is the emerging energy system wherein the application of information technology, tools and techniques that make the grid run more efficiently. It possesses demand response capacity to help balance electrical consumption with supply. The challenges and opportunities of emerging and future smart grids can be addressed by cloud computing. To focus on these requirements, we provide an in-depth survey on different cloud computing applications for energy management in the smart grid architecture. In this survey, we present an outline of the current state of research on smart grid development. We also propose a model of cloud based economic power dispatch for smart grid

Executing Large Scale Scientific Workflows in Public Clouds

Scientists in different fields, such as high-energy physics, earth science, and astronomy are developing large-scale workflow applications. In many use cases, scientists need to run a set of interrelated but independent workflows (i.e., workflow ensembles) for the entire scientific analysis. As a workflow ensemble usually contains many sub-workflows in each of which hundreds or thousands of jobs exist with precedence constraints, the execution of such a workflow ensemble makes a great concern with cost even using elastic and pay-as-you-go cloud resources. In this thesis, we develop a set of methods to optimize the execution of large-scale scientific workflows in public clouds with both cost and deadline constraints with a two-step approach. Firstly, we present a set of methods to optimize the execution of scientific workflow in public clouds, with the Montage astronomical mosaic engine running on Amazon EC2 as an example. Secondly, we address three main challenges in realizing benefits of using public clouds when executing large-scale workflow ensembles: (1) execution coordination, (2) resource provisioning, and (3) data staging. To this end, we develop a new pulling-based workflow execution system with a profiling-based resource provisioning strategy. Our results show that our solution system can achieve 80% speed-up, by removing scheduling overhead, compared to the well-known Pegasus workflow management system when running scientific workflow ensembles. Besides, our evaluation using Montage workflow ensembles on around 1000-core Amazon EC2 clusters has demonstrated the efficacy of our resource provisioning strategy in terms of cost effectiveness within deadline

A machine learning enhanced multi-start heuristic to efficiently solve a serial-batch scheduling problem

Serial-batch scheduling problems are widespread in several industries (e.g., the metal processing industry or industrial 3D printing) and consist of two subproblems that must be solved simultaneously: the grouping of jobs into batches and the sequencing of the created batches. This problem’s NP-hard nature prevents optimally solving large-scale problems; therefore, heuristic solution methods are a common choice to effectively tackle the problem. One of the best-performing heuristics in the literature is the ATCS–BATCS(β) heuristic which has three control parameters. To achieve a good solution quality, most appropriate parameters must be determined a priori or within a multi-start approach. As multi-start approaches performing (full) grid searches on the parameters lack efficiency, we propose a machine learning enhanced grid search. To that, Artificial Neural Networks are used to predict the performance of the heuristic given a specific problem instance and specific heuristic parameters. Based on these predictions, we perform a grid search on a smaller set of most promising heuristic parameters. The comparison to the ATCS–BATCS(β) heuristics shows that our approach reaches a very competitive mean solution quality that is only 2.5% lower and that it is computationally much more efficient: computation times can be reduced by 89.2% on average

Cloud Computing Strategies for Enhancing Smart Grid Performance in Developing Countries

In developing countries, the awareness and development of Smart Grids are in the introductory stage and the full realisation needs more time and effort. Besides, the partially introduced Smart Grids are inefficient, unreliable, and environmentally unfriendly. As the global economy crucially depends on energy sustainability, there is a requirement to revamp the existing energy systems. Hence, this research work aims at cost-effective optimisation and communication strategies for enhancing Smart Grid performance on Cloud platforms

Dynamic Resource Scheduling in Mobile Edge Cloud with Cloud Radio Access Network

Nowadays, by integrating the cloud radio access network (C-RAN) with the mobile edge cloud computing (MEC) technology, mobile service provider (MSP) can efficiently handle the increasing mobile traffic and enhance the capabilities of mobile devices. But the power consumption has become skyrocketing for MSP and it gravely affects the profit of MSP. Previous work often studied the power consumption in C-RAN and MEC separately while less work had considered the integration of C-RAN with MEC. In this paper, we present an unifying framework for the power-performance tradeoff of MSP by jointly scheduling network resources in C-RAN and computation resources in MEC to maximize the profit of MSP. To achieve this objective, we formulate the resource scheduling issue as a stochastic problem and design a new optimization framework by using an extended Lyapunov technique. Specially, because the standard Lyapunov technique critically assumes that job requests have fixed lengths and can be finished within each decision making interval, it is not suitable for the dynamic situation where the mobile job requests have variable lengths. To solve this problem, we extend the standard Lyapunov technique and design the VariedLen algorithm to make online decisions in consecutive time for job requests with variable lengths. Our proposed algorithm can reach time average profit that is close to the optimum with a diminishing gap (1/V) for the MSP while still maintaining strong system stability and low congestion. With extensive simulations based on a real world trace, we demonstrate the efficacy and optimality of our proposed algorithm

A survey of simulation techniques in commerce and defence

Despite the developments in Modelling and Simulation (M&S) tools and techniques over the past years, there has been a gap in the M&S research and practice in healthcare on developing a toolkit to assist the modellers and simulation practitioners with selecting an appropriate set of techniques. This study is a preliminary step towards this goal. This paper presents some results from a systematic literature survey on applications of M&S in the commerce and defence domains that could inspire some improvements in the healthcare. Interim results show that in the commercial sector Discrete-Event Simulation (DES) has been the most widely used technique with System Dynamics (SD) in second place. However in the defence sector, SD has gained relatively more attention. SD has been found quite useful for qualitative and soft factors analysis. From both the surveys it becomes clear that there is a growing trend towards using hybrid M&S approaches

Kestrel: Job Distribution and Scheduling using XMPP

A new distributed computing framework, named Kestrel, for Many-Task Computing (MTC) applications and implementing Virtual Organization Clusters (VOCs) is proposed. Kestrel is a lightweight, highly available system based on the Extensible Messaging and Presence Protocol (XMPP), and has been developed to explore XMPP-based techniques for improving MTC and VOC tolerance to faults due to scaling and intermittently connected heterogeneous resources. Kestrel provides a VOC with a special purpose scheduler for VOCs which can provide better scalability under certain workload assumptions, namely CPU bound processes and bag-of-task applications. Experimental results have shown that Kestrel is capable of operating a VOC of at least 1600 worker nodes with all nodes visible to the scheduler at once. When using multiple sites located in both North America and Europe, the latencies introduced to the round trip time of messages were on the order of 0.3 seconds. To offset the overhead of XMPP processing, a task execution time of 2 seconds is sufficient for a pool of 900 workers on a single site to operate at near 100% use. Requiring tasks that take on the order of 30 seconds to a minute to execute would compensate for increased latency during job dispatch across multiple sites. Kestrel\u27s architecture is rooted in pilot job frameworks heavily used in Grid computing, it is also modeled after the use of IRC by botnets to communicate between compromised machines and command and control servers. For Kestrel, the extensibility of XMPP has allowed development of protocols for identifying manager nodes, discovering the capabilities of worker agents, and for distributing tasks. The presence notifications provided by XMPP allow Kestrel to monitor the global state of the pool and to perform task dispatching based on worker availability. In this work it is argued that XMPP is by design a very good fit for cloud computing frameworks. It offers scalability, federation between servers and some autonomicity of the agents. During the summer of 2010, Kestrel was used and modified based on feedback from the STAR group at Brookhaven National Laboratories. STAR provided a virtual machine image with applications for simulating proton collisions using PYTHIA and GEANT3. A Kestrel-based virtual organization cluster, created on top of Clemson University\u27s Palmetto cluster, was able to provide over 400,000 CPU hours of computation over the course of a month using an average of 800 virtual machine instances every day, generating nearly seven terabytes of data and the largest PYTHIA production run that STAR ever achieved. Several architectural issues were encountered during the course of the experiment and were resolved by moving from the original JSON protocols used by Kestrel to native XMPP equivalents that offered better message delivery confirmation and integration with existing tools