Decentralized Edge-to-Cloud Load-balancing: Service Placement for the Internet of Things.

The Internet of Things (IoT) requires a new processing paradigm that inherits the scalability of the cloud while minimizing network latency using resources closer to the network edge. On the one hand, building up such flexibility within the edge-to-cloud continuum consisting of a distributed networked ecosystem of heterogeneous computing resources is challenging. On the other hand, IoT traffic dynamics and the rising demand for low-latency services foster the need for minimizing the response time and a balanced service placement. Load-balancing for fog computing becomes a cornerstone for cost-effective system management and operations. This paper studies two optimization objectives and formulates a decentralized load-balancing problem for IoT service placement: (global) IoT workload balance and (local) quality of service (QoS), in terms of minimizing the cost of deadline violation, service deployment, and unhosted services. The proposed solution, EPOS Fog, introduces a decentralized multi-agent system for collective learning that utilizes edge-to-cloud nodes to jointly balance the input workload across the network and minimize the costs involved in service execution. The agents locally generate possible assignments of requests to resources and then cooperatively select an assignment such that their combination maximizes edge utilization while minimizes service execution cost. Extensive experimental evaluation with realistic Google cluster workloads on various networks demonstrates the superior performance of EPOS Fog in terms of workload balance and QoS, compared to approaches such as First Fit and exclusively Cloud-based. The results confirm that EPOS Fog reduces service execution delay up to 25% and the load-balance of network nodes up to 90%. The findings also demonstrate how distributed computational resources on the edge can be utilized more cost-effectively by harvesting collective intelligence

Context Driven Concept Based Image Retrieval

Several semantic image search schemes have been recently proposed to retrieve images from the web. However, the query context is regularly ignored in these techniques and hence, many of the returned images are not adequately relevant. In this paper, we make use of context to further confine the outcome of the semantic search engines. For this purpose, we propose a hybrid search engine which utilizes concept and context for retrieving precise results. In the proposed model, an ontology is exploited for annotating images and accomplishing search process in the semantic level. Furthermore, the query of the user is modified with the concepts available in the ontology. Next, we make use of search context of the user and augment the query with the information extracted from the user’s context to additionally eliminate irrelevant results. Experimental results show that the combination of concept and context is effective in retrieving and presenting the most relevant results to the user

Designing Optimal Binary Search Tree Using Parallel Genetic Algorithms

Evolutionary algorithms (EAs) are modern techniques for searching complex spaces for on optimum [11]. Genetic algorithms (GAs) are developed as random search methods, which have not so sensitivity on primary data of the problems. They can be used in estimation of system parameters in order to obtain the best result. This can be achieved by optimization of an objective function. Genetic programming is a collection of methods for the automatic generation of computer programs that solve carefully specified problems, via the core, but highly abstracted principles of natural selection [12]. In this paper, genetic algorithms and parallel genetic algorithms have been discussed as one of the best solutions for optimization of the systems. Genetic and parallel genetic algorithms have been investigated in parallel programming environment called Multi-Pascal. Then an optimal binary search tree has been selected as a case study for decree sing of searching time. Also a dynamic programming method has been accelerated by using of a parallel genetic algorithm. In this case, by increasing the size of data, speed-up index will be increased. Key words

A New Randomized Algorithm for Handling Scheduling Conflicts in Grids

As the scale of Grid platforms grows, the idea of a centralized scheduler loses its efficiency, and it is replaced with the scheme of decentralized schedulers. However, a new problem emerges in distributed scheduling systems, which is how to coordinate the autonomous schedulers to avoid the occurrence of conflicting schedules. In this paper, by exploiting the idea of randomized algorithms, a new scheduling scheme has been proposed, which addresses the problem of scheduling conflicts. The proposed algorithm is thoroughly decentralized in the sense that there is no central point of contact in the system. In addition, our approach is a suitable way toward reaching scalability and autonomy in future Grids. We prove the feasibility and effectiveness of the proposed algorithm through statistical analysis