A super-peer is a node in a peer-to-peer network that operates both as a server to a set of clients, and as an equal in a network of super-peers. Super-peer networks strike a balance between the efficiency of centralized search, and the autonomy, load balancing and robustness to attacks provided by distributed search. Furthermore, they take advantage of the heterogeneity of capabilities (e.g., bandwidth, processing power) across peers, which recent studies have shown to be enormous. Hence, new and old P2P systems like KaZaA and Gnutella are adopting super-peers in their design. Despite their growing popularity, the behavior of super-peer networks is not well understood. For example, what are the potential drawbacks of super-peer networks? How can super-peers be made more reliable? How many clients should a super-peer take on to maximize efficiency? we examine super-peer networks in detail, gaming an understanding of their fundamental characteristics and performance tradeoffs. We also present practical guidelines and a general procedure for the design of an efficient super-peer network.

Designing an Super-Peer Network

Cloud Computing provides an effective platform for executing large-scale and complex workflow applications with a pay-as-you-go model. Nevertheless, various challenges, especially its optimal scheduling for multiple conflicting objectives, are yet to be addressed properly. The existing multi-objective workflow scheduling approaches are still limited in many ways, e.g., encoding is restricted by prior experts' knowledge when handling a dynamic real-time problem, which strongly influences the performance of scheduling. In this paper, we apply a deep-Q-network model in a multi-agent reinforcement learning setting to guide the scheduling of multi-workflows over infrastructure-as-a-service clouds. To optimize multi-workflow completion time and user's cost, we consider a Markov game model, which takes the number of workflow applications and heterogeneous virtual machines as state input and the maximum completion time and cost as rewards. The game model is capable of seeking for correlated equilibrium between make-span and cost criteria without prior experts' knowledge and converges to the correlated equilibrium policy in a dynamic real-time environment. To validate our proposed approach, we conduct extensive case studies based on multiple well-known scientific workflow templates and Amazon EC2 cloud. The experimental results clearly suggest that our proposed approach outperforms traditional ones, e.g., non-dominated sorting genetic algorithm-II, multi-objective particle swarm optimization, and game-theoretic-based greedy algorithms, in terms of optimality of scheduling plans generated.

/pdf/multi-objective-workflow-scheduling-with-deep-q-network-2o88jz1zir.pdf

Multi-Objective Workflow Scheduling With Deep-Q-Network-Based Multi-Agent Reinforcement Learning

Thank you very much for reading swarm intelligence from natural to artificial systems. As you may know, people have search hundreds times for their chosen books like this swarm intelligence from natural to artificial systems, but end up in malicious downloads. Rather than reading a good book with a cup of coffee in the afternoon, instead they juggled with some infectious bugs inside their computer.

Swarm Intelligence From Natural To Artificial Systems

Multi-objective metaheuristics for discrete optimization problems: A review of the state-of-the-art

A novel multi-objective bacteria foraging optimization algorithm (MOBFOA) for multi-objective scheduling

Efficient scheduling of tasks in workflows of cloud or grid applications is a key to achieving better utilization of resources as well as timely completion of the user jobs. Many scientific applications comprise several tasks that are dependent in nature and are specified by workflow graphs. The aim of the cloud meta-scheduler is to schedule the user application tasks (and the applications) so as to optimize the resource utilization and to execute the user applications in minimum amount of time. During the past decade, there have been several attempts to use bio-inspired scheduling algorithms to obtain an optimal or near optimal schedule in order to minimize the overall schedule length and to optimize the use of resources. However, as the number of tasks increases, the solution space comprising different tasks-resource mapping sequences increases exponentially. Hence, there is a need to devise mechanisms to improvise the search strategies of the bio-inspired scheduling algorithms for better scheduling solutions in lesser number of iterations/time. The objective of the research work in this paper is to use bio-inspired bacteria foraging optimization algorithm (BFOA) along with other heuristics algorithms for better search of the scheduling solution space for multiple workflows. The idea is to first find a schedule by the heuristic algorithms such as MaxMin, MinMin, and Myopic, and use these as initial solutions (along with other randomly generated solutions) in the search space to get better solutions using BFOA. The performance of our approach with the existing approaches is compared for quality of the scheduling solutions. The results demonstrate that our hybrid approach (MinMin/Myopic with BFOA) outperforms other approaches.

/pdf/bio-inspired-workflow-scheduling-on-hpc-platforms-usam986458.pdf

Bio-Inspired Workflow Scheduling on HPC Platforms

Multi-level parallel scheduling of dependent-tasks using graph-partitioning and hybrid approaches over edge-cloud

Discovery of resources using MADM approaches for parallel and distributed computing

Research on decision making in an imprecise environment has gained and will continue to gain overriding importance in recent years. In this context, decision making on the location of hazardous plant installations is an issue of relevance. The authors believe that there is a need of a standard model to overcome the discrepancies in Environmental Impact Assessment (EIA) for rationalization of site selection. An inappropriate choice of a site for an upcoming hazardous industrial plant may lead to further degradation of existing environment thus resulting in hazardous effects on mankind as well as all other living organisms. It could be argued that there exists quantifiable (measurable parameters) and epistemic (non measurable parameters) uncertainties in the scoping phase and the overall EIA process. The uncertainties could be modeled using soft computing techniques in ranking the sites for upcoming hazardous industry and justify the results in linguistic terms. The case study in the paper relates to the application of Back propagation Artificial Neural Network (BP-ANN), Learning Vector Quantization (LVQ-ANN), Ant Colony optimization with Fuzzy Soft Sets (FSS) and Fuzzy Indexing (FI), in ranking the existing/upcoming power plant locations in India. Comparative evaluation of these methods is also an integral part of the paper.

Sanjay Kadam

Papers

A novel multi-objective bacteria foraging optimization algorithm (MOBFOA) for multi-objective scheduling

Bio-Inspired Workflow Scheduling on HPC Platforms

Multi-level parallel scheduling of dependent-tasks using graph-partitioning and hybrid approaches over edge-cloud

Discovery of resources using MADM approaches for parallel and distributed computing

Ranking of sites for power plant installation using soft computing techniques - A thought beyond EIA