We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

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The Cambridge Handbook Of The Learning Sciences

Models and Methods in Social Network Analysis presents the most important developments in quantitative models and methods for analyzing social network data that have appeared during the 1990s. Intended as a complement to Wasserman and Faust’s Social Network Analysis: Methods and Applications, it is a collection of original articles by leading methodologists reviewing recent advances in their particular areas of network methods. Reviewed are advances in network measurement, network sampling, the analysis of centrality, positional analysis or blockmodeling, the analysis of diffusion through networks, the analysis of affiliation or “two-mode” networks, the theory of random graphs, dependence graphs, exponential families of random graphs, the analysis of longitudinal network data, graphic techniques for exploring network data, and software for the analysis of social networks.

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Models and methods in social network analysis

The fast-paced development of power systems necessitates smart grids to facilitate real-time control and monitoring with bidirectional communication and electricity flows. Future smart grids are expected to have reliable, efficient, secured, and cost-effective power management with the implementation of distributed architecture. To focus on these requirements, we provide a comprehensive survey on different cloud computing applications for the smart grid architecture, in three different areas— energy management , information management , and security . In these areas, the utility of cloud computing applications is discussed, while giving directions on future opportunities for the development of the smart grid. We also highlight different challenges existing in the conventional smart grid (without cloud application) that can be overcome using cloud. In this survey, we present a synthesized overview of the current state of research on smart grid development. We also identify the current research problems in the areas of cloud-based energy management, information management, and security in smart grid.

Cloud Computing Applications for Smart Grid: A Survey

Resource scheduling in cloud is a challenging job and the scheduling of appropriate resources to cloud workloads depends on the QoS requirements of cloud applications. In cloud environment, heterogeneity, uncertainty and dispersion of resources encounters problems of allocation of resources, which cannot be addressed with existing resource allocation policies. Researchers still face troubles to select the efficient and appropriate resource scheduling algorithm for a specific workload from the existing literature of resource scheduling algorithms. This research depicts a broad methodical literature analysis of resource management in the area of cloud in general and cloud resource scheduling in specific. In this survey, standard methodical literature analysis technique is used based on a complete collection of 110 research papers out of large collection of 1206 research papers published in 19 foremost workshops, symposiums and conferences and 11 prominent journals. The current status of resource scheduling in cloud computing is distributed into various categories. Methodical analysis of resource scheduling in cloud computing is presented, resource scheduling algorithms and management, its types and benefits with tools, resource scheduling aspects and resource distribution policies are described. The literature concerning to thirteen types of resource scheduling algorithms has also been stated. Further, eight types of resource distribution policies are described. Methodical analysis of this research work will help researchers to find the important characteristics of resource scheduling algorithms and also will help to select most suitable algorithm for scheduling a specific workload. Future research directions have also been suggested in this research work.

A Survey on Resource Scheduling in Cloud Computing: Issues and Challenges

Now-a-days, Cloud computing is a technology which eludes provision cost while providing scalability and elasticity to accessible resources on a pay-per-use basis. To satisfy the increasing demand of the computing power to execute large scale scientific workflow applications, workflow scheduling is the main challenging issue in Infrastructure-as-a-Service (IaaS) clouds. As workflow scheduling belongs to NP-complete problem, so, meta-heuristic approaches are more preferred option. Users often specified deadline and budget constraint for scheduling these workflow applications over cloud resources. But these constraints are in conflict with each other, i.e., the cheaper resources are slow as compared to the expensive resources. Most of the existing studies try to optimize only one of the objectives, i.e., either time minimization or cost minimization under user specified Quality of Service (QoS) constraints. But due to the complexity of workflows and dynamic nature of cloud, a trade-off solution is required to make a balance between execution time and processing cost. To address these issues, this paper presents a non-dominance sort based Hybrid Particle Swarm Optimization (HPSO) algorithm to handle the workflow scheduling problem with multiple conflicting objective functions on IaaS clouds. The proposed algorithm is a hybrid of our previously proposed Budget and Deadline constrained Heterogeneous Earliest Finish Time (BDHEFT) algorithm and multi-objective PSO. The HPSO heuristic tries to optimize two conflicting objectives, namely, makespan and cost under the deadline and budget constraints. Along with these two conflicting objectives, energy consumed of created workflow schedule is also minimized. The proposed algorithm gives a set of Pareto Optimal solutions from which the user can choose the best solution. The performance of proposed heuristic is compared with state-of-art multi-objective meta-heuristics like NSGA-II, MOPSO, and e -FDPSO. The simulation analysis substantiates that the solutions obtained with proposed heuristic deliver better convergence and uniform spacing among the solutions as compared to others. Hence it is applicable to solve a wide class of multi-objective optimization problems for scheduling scientific workflows over IaaS clouds.

A hybrid multi-objective Particle Swarm Optimization for scientific workflow scheduling

Display Omitted The disadvantages of using extent analysis method for network selection problems are discussedWeights of network parameters are obtained by applying a nonlinear fuzzy optimization modelConsistency Index with this proposed model is better than the existing non-linear modelsParameterized utility functions are used to evaluate the utility values of network attributesResults obtained for network selection with the MEW method are better than TOPSIS and SAW methods Next generation wireless networks will integrate various heterogeneous technologies like WLAN, WiMax and cellular technologies etc, to support multimedia services with higher bandwidth and guaranteed quality of service (QoS) In order to keep the mobile user always connected to the best wireless network in terms of QoS parameters and user preferences, an optimal network selection technique in heterogeneous networks is required This paper proposes a novel fuzzy-Analytic Hierarchy Process (AHP) based network selection in heterogeneous wireless networks Triangular fuzzy numbers are used to represent the elements in the comparison matrices for voice, video and best effort applications Deriving crisp weights from these fuzzy comparison matrices is a challenging task When extent analysis method is applied, irrational zero weights are obtained for some attributes Due to this, many important criteria are not considered in the decision making process To overcome this problem, a new non-linear fuzzy optimization model for deriving crisp weights from fuzzy comparison matrices for network selection is presented The weights obtained from this model are more consistent than the existing optimization models Also, parameterized utility functions are used to model the different Quality of Service (QoS) attributes (bandwidth, delay, jitter, bit error rate) and user preferences (cost) for three different types of applications Finally, scores are calculated exclusively for each network by three MADM (Multiple Attribute Decision Making) methods Simple Additive Weighting (SAW), TOPSIS (The Technique for Order of Preference by Similarity to Ideal Solution) and MEW (Multiplicative Exponential Weighting) Results show that the MEW method gives more appropriate scores with utility functions than the SAW and TOPSIS methods

The utility based non-linear fuzzy AHP optimization model for network selection in heterogeneous wireless networks

The emergence of Cloud Computing as a model of service provisioning in distributed systems instigated researchers to explore its pros and cons on executing different large scale scientific applications, i.e., Workflows. One of the most challenging problems in clouds is to execute workflows while minimizing the execution time as well as cost incurred by using a set of heterogeneous resources over the cloud simultaneously. In this paper, we present, Budget and Deadline Constrained Heuristic based upon Heterogeneous Earliest Finish Time (HEFT) to schedule workflow tasks over the available cloud resources. The proposed heuristic presents a beneficial trade-off between execution time and execution cost under given constraints. The proposed heuristic is evaluated for different synthetic workflow applications by a simulation process and comparison is done with state-of-art algorithm i.e. BHEFT. The simulation results show that our proposed scheduling heuristic can significantly decrease the execution cost while producing makespan as good as the best known scheduling heuristic under the same deadline and budget constraints.

Cost-Time Efficient Scheduling Plan for Executing Workflows in the Cloud

Cloud Computing has become another buzzword after Web 2.0. The phrase cloud computing originated from the diagrams used to symbolize the internet. Cloud computing is not a completely new concept; it has intricate connection to the grid Computing paradigm, and other relevant technologies such as utility computing, cluster computing, and distributed systems in general. With the development of cloud computing, a set of security problems appears. Security issues present a strong barrier for users to adapt into Cloud Computing systems. Several surveys of potential cloud adopters indicate that security is the primary concern hindering its adoption. This paper introduces the background and service model of cloud computing. Along with this, few of security issues and challenges are also highlighted.

Cloud Computing Security Issues and Challenges: A Survey

Task scheduling and resource allocation are the key challenges of cloud computing. Compared with grid environment, data transfer is a big overhead for cloud workflows. So, the cost arising from data transfers between resources as well as execution costs must also be taken into account during scheduling based upon user's Quality of Service QoS constraints. In this paper, we present Deadline Constrained Heuristic based Genetic Algorithms HGAs to schedule applications to cloud resources that minimise the execution cost while meeting the deadline for delivering the result. Each workflow's task is assigned priority using bottom-level b-level and top-level t-level. To increase the population diversity, these priorities are then used to create the initial population of HGAs. The proposed algorithms are simulated and evaluated with synthetic workflows based on realistic workflows. The simulation results show that our proposed algorithms have a promising performance as compared to Standard Genetic Algorithm SGA.

Sakshi Kaushal

Papers

A hybrid multi-objective Particle Swarm Optimization for scientific workflow scheduling

The utility based non-linear fuzzy AHP optimization model for network selection in heterogeneous wireless networks

Cost-Time Efficient Scheduling Plan for Executing Workflows in the Cloud

Cloud Computing Security Issues and Challenges: A Survey

Deadline constraint heuristic-based genetic algorithm for workflow scheduling in cloud